Maximizing Business Value: Optimizing Technology Investment
 

Maximizing Business Value: Optimizing Technology Investment

on

  • 2,534 views

Presentation by Randy Lea at the Teradata 3rd Party Influencer's Meeting April 2010 in San Diego, CA

Presentation by Randy Lea at the Teradata 3rd Party Influencer's Meeting April 2010 in San Diego, CA

Statistics

Views

Total Views
2,534
Slideshare-icon Views on SlideShare
2,523
Embed Views
11

Actions

Likes
1
Downloads
136
Comments
0

1 Embed 11

http://www.slideshare.net 11

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Viewpoint logins will map the user to the sessions and queries that I have submitted based on account ID. When they get added to Viewpoint, there is a place in the Viewpoint profile to insert their Teradata User ID and account number. This is used to connect Viewpoint users to MyQueries sessions. These portlets are likely to be popular with the ”power users” who submit a lot of queries and understand data warehousing well. They will search for their query and drill down to see how it is progressing. The MY QUERIES portlet provides information about queries currently running in a Teradata Database system. This tool allows you to define selection criteria to display queries that have the potential to impact overall system performance. Not all queries that exceed a threshold are bad. MY QUERIES helps you determine if a query is important and useful, or just poorly written. Use MY QUERIES to view information about queries in either the summary view or the detail view. The summary view contains a table with one row allocated to each of the sessions logged on under one or more user names. Select a row in the summary view to drill down to additional session and query information in the detail view. Use one of four tabs in the detail view to display query statistics, SQL, Explain, and blocking information. The PREFERENCES: MY QUERIES view allows you to select one or more Teradata Database systems, and then select one or more users per system to monitor. From this view, you can also configure the appearance of the summary and detail views.
  • The capacity map allows you to quickly scroll through days or even a month of performance statistics. The red periods are times of days when the system is heavily saturated with work. This visual display can be easier and more useful than tabular reports for finding patterns of system performance and saturation. The CAPACITY HEATMAP portlet provides a visual representation of system resource utilization based on userdefined metrics over an adjustable time period. The CAPACITY HEATMAP portlet uses historical data to help identify periods during the day or week when a system is either over- or under-utilized. You can use this information to determine when to schedule resource-intensive jobs with minimal impact to other users. The CAPACITY HEATMAP portlet displays a grid with days on the horizontal axis and hours on the vertical axis. Each color-coded square represents an hour and provides a visual indication of the metric value during a 1-hour period.
  • Here we see the scroll bar that lets the DBA scroll backwards in time to look at system saturation on days that may be similar to one another. The and buttons allow you to adjust the CAPACITY HEATMAP date range displayed on the toolbar. When clicked, the buttons modify the date range to display 1 month or 3 months of historical data, with the previous end date remaining as the last date displayed. As you adjust the date range, the toolbar refreshes automatically to display the new date range. The (threshold button) displays the current threshold value. The purpose of the threshold value is to highlight periods when the system is over- or under-utilized. Click to open the threshold slider.
  • These are a subset of the functions available in TDWM. They tend to be the high use functions in many sites. With object throttles you should include users in your list of objects. A common use of object throttles today is to put one throttle on the performance group with a high concurrency limit, and then a second, different throttle on specific users within that PG that tend to submit large numbers of queries at one time, but give that a low limit, like 2.
  • Although you can set priorities to certain relative weight percents, in a dynamic environment the actual consumption usually will be quite different from these values. The ad hoc reporting queries and data loading tend to consume a lot of resources whereas the tactical queries and rush work are much small consumers. So even though tactical work may have a high relative weight, the work cannot consume that level of resources, so they are immediately made available to lower-priority priorities. The purpose of Priority Scheduler is to give quicker access to resources for the high priority (R & H) categories of work, even if it means making other categories of work wait longer for access to resources. Notice that the low priority jobs actually use much more CPU, memory, and disk I/O than the small real time priority tasks. This illustrates that prioritization does not equate to resource use. It only affects access to the resources in priority order. Weights and CPU usage in the ADW Benchmark Tactical queries: Relative weight of 45%...consume 12% DS queries: Relative weight of 1%...consume 77%
  • 04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
  • 04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
  • 04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
  • 04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
  • 04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
  • This is a Data Overlap Analysis which is specific to this industry. It was developed using the data from our iEDWr. With information like this, you could analyze your upcoming projects; and all things equal, you can determine which projects need less data added to the warehouse which could mean faster time to implement and lower overall risk.
  • This is a Data Overlap Analysis which is specific to this industry. It was developed using the data from our iEDWr. With information like this, you could analyze your upcoming projects; and all things equal, you can determine which projects need less data added to the warehouse which could mean faster time to implement and lower overall risk.
  • 04/30/10 Data warehouses usually follow a predictable evolution. After nearly 30 years, we’ve seen the stages companies go through on their path to enterprise data warehousing. Moving from Stage 1 (What Happened?) into Stage 2 (Why Did It Happen?) requires new capabilities for ad hoc analysis. Then as you evolve to Stage 3 (Predicting What Will Happen) you again grow in your platform and database requirements. As you cross the chasm into Stages 4 and 5, (Operational Intelligence) you require a platform capable of “active” analysis.
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
  • In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
  • In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
  • In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
  • In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
  • 04/30/10 This is a simplification of the Active Data Warehouse. The ADW begins with a Teradata data warehouse foundation. There are 6 elements that are used when activating your data warehouse: Active Access, Active Events, Active Load, Active Enterprise Integration, Active Workload Management, and Active Availability. Each of these are applied at different levels of investment depending ont eh maturity of the current data warehouse, the application being activated, and the users.
  • Teradata has a Virtualization Lab in San Diego Many Teradata products run on VMWare today We are working with VMWare R&D to optimize MPP and EDW workloads Reduce VMWare CPU tax Enhance VMWare support for EDW I/O configurations Enable MPP configurations Once VMWare is optimized for EDW workloads, it can be used in a single node configuration No release date for Teradata on top of VMWare in MPP configurations at this time
  • Why Teradata? Teradata has significant advantages over competitors in the Teradata 2500 space. We offer a single analytical database standard for all of your data warehousing needs. This means that you can leverage all of your existing resources and tools that have already been trained. It also mean one stop shopping and support for all of you data warehousing needs. No matter what your analytical system requirements are Teradata has you covered from entry level SMP to EDW/ADW implementations. All of this with simple ability to migrate applications into the Teradata EDW. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{FFDDBF73-709E-4B8A-9605-1AF4E8AFFA6A}}
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • OLAP (On-line Analytic Processing) is a tool that allows users to perform speed-of-thought analysis of data using business terminology. Speed of thought means responses are returned in a timely manner so it doesn’t disrupt the analytic thought process. Meaning OLAP tool users expect results in a matter of seconds, at most, minutes. OLAP tools also represents data using common business terms by defining dimensions in such time, location, product line, etc. And also allows users to drill down to different levels of the hierarchy in each dimension. For example for the time dimension, they can view at the higher levels such as year, quarter, month, or lower levels such as week and days. All OLAP technologies provide the user with an intuitive multi-dimensional view, allowing the user to analyze data based on multiple dimensions and at varying levels of detail, however, the underlying implementation can vary. The implementations typically vary based on two key factors: The type of data: the depth or levels of hierarchy within each dimension. Aggregated data vs. detailed data. Data store: Where the physical data resides. Is it in a physical cube on a server or leverage the data in a relational database. Let’s quickly examine the different implementation and how Teradata can optimize each. First we have the physical cube also referred to as MOLAP (Multi-dimensional OLAP). All dimensions and levels of your OLAP content are extracted, pre-calculated and stored in a cube - multi-dimensional database for analysis. In this case, Teradata is typically used as a data store and the data is extracted and moved into an OLAP server. The strengths of MOLAP is the quickness in response because the data is pre-calculated and available on the server, but that comes with a cost: maintenance and scalability Moving toward the right, is the ROLAP implementation. In this implementation relies heavily on the database engine. User request are submitted to the OLAP engine in the mid-tier which may house metadata on the server, however the processing is done in the database. The OLAP engine will convert the request into a SQL query that’s processed in the database. The benefits of the ROLAP implementation are lower maintenance cost and scalability in terms of breadth (in terms of more dimensions) and depth of analysis (more levels). An then there’s the hybrid implementation HOLAP. HOLAP leverages the strengths of MOLAP and ROLAP, where some dimensions and levels of your OLAP content are extracted and stored in a cube for analysis and drills-down into a relational database for detail analysis. Teradata optimizations that are done for MOLAP and ROLAP can be applied to HOLAP. For our partner technology, this is the best compromise solution. Our message is a complementary that we optimize all OLAP implementations. We’re in the process of developing technical training that will cover AJI implementation with a shorter segment for each of our key partners.
  • In order to optimize the performance for BI solutions it is advisable to make sure the data architecture supporting the BI solution is designed to take advantage of the built-in Teradata features. For OLAP solutions Teradata provides a key optimization technology called Aggregate Join Index (AJI), building and deploying AJIs is a manual process and requires understanding of the BI solution and the data architecture. TBIO is designed to automate this process and to recommend and build the necessary AJIs. TBIO consists of the following: Schema Workbench is a desktop designer tool and is used to manually build the schema definition for BI tools that are not compatible with the Aggregate Designer. In the initial release Microsoft SQL Server Analysis Services will be the only BI tool to produce the compatible schema definitions but we are working with other BI vendors on automating this process. Aggregate Designer is a desktop tool used to read/consume the schema definitions and recommends/builds the required AJIs assuming they meet the AJI requirements. OLAP Connect is used by Excel or MDX-based tools to connect to Teradata
  • In order to optimize the performance for BI solutions it is advisable to make sure the data architecture supporting the BI solution is designed to take advantage of the built-in Teradata features. For OLAP solutions Teradata provides a key optimization technology called Aggregate Join Index (AJI), building and deploying AJIs is a manual process and requires understanding of the BI solution and the data architecture. TBIO is designed to automate this process and to recommend and build the necessary AJIs. TBIO consists of the following: Schema Workbench is a desktop designer tool and is used to manually build the schema definition for BI tools that are not compatible with the Aggregate Designer. In the initial release Microsoft SQL Server Analysis Services will be the only BI tool to produce the compatible schema definitions but we are working with other BI vendors on automating this process. Aggregate Designer is a desktop tool used to read/consume the schema definitions and recommends/builds the required AJIs assuming they meet the AJI requirements. OLAP Connect is used by Excel or MDX-based tools to connect to Teradata
  • We’ve discussed the what are AJIs and the benefits and now I’d like to share actual results of a customer implementation. This proof of concept was implemented to measure the ability and impact of usin gthe Teradata database to directly support OLAP analysis at one of our wireless customers. This customer originally started with a MOLAP implementation for their Performance Assurance Reporting suite. They implemented up to 38 dimensions and 24 measures which is one of the largest MOLAP implementations or cubes at this sight. The cube was over 22GB in size and monthly data transfer of 334 MB of data. It took approximately 13 hours to refresh the entire cube. And the lowest level of detailed available in this cube was at the monthly level. In addition to demonstrating the performance improvement, this customer also wanted to add another dimension to their OLAP cube however this exceeded architecture and hardware limitation and was able to deliver a partial solution with only 20 of the 4857 wire center values. This 4 day project including the time to capture and compare results, resulted in faster build and maintenance of the cube, ability to reduce data & redundant data mart, provided the scalability required to add the 39 th dimension, provided more detailed data and history and provided faster query response. As you can see from the query times, OLAP optimized with Teradata was significantly faster. We compared a set of 5 canned queries and 6 interactive queries with and without the 39 th dimension (Wire Center). The red indicates the times it took for using an OLAP server without optimization and the times in orange indicates time after pushing the processing into Teradata leveraging AJI. Of course we were unable to compare the query time w/wire centers because they were unable to add this dimension in the cube, but I’ve included this just to illustrate that the performance doesn’t vary much by adding another dimension. That’s the beauty of Teradata’s scalability and parallelism.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Our technique is rather simple. Instead of bring the mountain of data to the data mining server as illustrated here, we bring the analytic functions to the mountain of data. The illustration on the top demonstrates the typically analytic environment where the data is extracted from the database on to the analytic server. Since data mining tasks, especially the data preparation tasks are data intensive and highly iterative, this architecture requires massive data movement which becomes the bottleneck in the data mining process. The processing is done on the server and once the results are available either as part of the data preparation or modeling phase, the results must then be written back into the warehouse for integration. With Teradata’s in-database technique, the data preparation, algorithms and the models are actually converted to SQL and pushed back into the database. The SQL is then processed in PARALLEL directly in the database and the results are written in the database. Our customers have found that in-database processing is significantly faster than the server based approach. On the average, performance improvements have been to the magnitude of 25, so tasks that took 25 minute now take less than 1 minute. Teradata takes an unique approach to analytic by pushing analytics into the database. Instead of bringing the data to the server, Teradata converts the ETL and analytic processing into optimized SQL then pushes the processing into Teradata.
  • They built an enterprise ADS for risk analytics on Teradata. This immediately improved quality because of the consistency of data across all users. They also accelerated their development cycles. When hurricane Katrina hit, they were able to build a hurricane exposure analysis within 90 minutes providing their executives with information regarding their customers affected by the hurricane. They were able to extend credit and provide special offers to customers to help their customers with the recovery process. Previously, this same task would have taken over 3 weeks. Creating an Risk ADS and allowing their users to direct access to Teradata provided greater efficiency… greater than expected. Last year, the Risk team developed 40 models, and this year with the same resources, they’re on target to develop 500 analytic models.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Teradata’s Geospatial implementation differs from other environments because most Geographic information systems are implemented as departmental data marts that are loosely, if at all integrated with their EDW. The data marts integrate geographic coordinates, geographic data such as Hazard data and a subset of the business data. Business intelligence environments are separated from the GIS enviornment
  • Next question is what can I do with these new types? We offer four classes of functions. Measurement queries: the distance between entities, the surface or the perimeter of an area, etc. These queries return a numeric value. Topological relations between two objects such as A intersects B, A contains B, A is within B, A is adjacent to B etc. These queries return a 0 for false or 1 for true. Examples of relevant query methods are: Single object spatial relations with other objects - retrieve all the objects located within the object A, touch object B etc. These queries return a list of objects. Examples of relevant query methods are: Object attribute queries - object type, number of points etc. These queries return attribute values. Examples of relevant query methods are: What are the real-world applications? Points can be represented on a map to indicate specific location such as stores, customers and competitors. You can measure the distance between the customer and your store. Click : You can also identify a 3 mile radius from your store and identify the relationship with the sales region of your competitor. Click : We can also retrieve objects that are contained within a region identifying the customers that are located in overlapping areas.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • This is an internal private cloud hosted inside the Teradata Server Provides self service, capacity on demand, and multi-tenancy Mixed workload management is a critical success factor The Viewpoint portlet source code is free This solution is good for Ad hoc and short term analysis Sand boxes, data miners, POC, developers Value discovery in the data and bring into EDW Controlling data mart proliferation You know where all the marts are Business users get what they need
  • This is an internal private cloud hosted inside the Teradata Server Provides self service, capacity on demand, and multi-tenancy Mixed workload management is a critical success factor The Viewpoint portlet source code is free This solution is good for Ad hoc and short term analysis Sand boxes, data miners, POC, developers Value discovery in the data and bring into EDW Controlling data mart proliferation You know where all the marts are Business users get what they need
  • This is simplified mockup of the workflow in the Elastic data mart life cycle. A GUI screen helps allocate perm space for a user and establishes security permissions for them. Once the perm space is built, the user can login and upload data sets into new tables. Since they are expected to use comma separated values format, the column names can be used to organize columns and to select data types and primary indexes. Once the data layout is accepted by the user, the data file can be loaded into the new table. Using a simple SQL interface, the user can also create, delete or modify tables. Based on their security permissions, they can also copy data from production data warehouse tables into the elastic mart. Creating a mart or sand box simply allocates a database to that UserID Here we upload a file, usually a spreadsheet of account numbers or products or whatever the user wants to analyze. 3) The portlet helps the user define the column names and data types of the CSV data, The data is then imported into the database created in step 1 Now the user can use joins or insert select statements to pull data from the production database into the mart or sand box. If the data needs to be modifiable, then the mart must have enough disk space to hold the tables that will be created from production data. If not, then the mart need only contain the control tables, that is not copy production data into the mart or sandbox.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
  • Our vision for how companies can do this is by being capable of operating with Active Enterprise Intelligence
  • [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{B180C764-08E0-4878-85C4-706538E48745}}
  • [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{B180C764-08E0-4878-85C4-706538E48745}}
  • [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{B180C764-08E0-4878-85C4-706538E48745}}
  • Retail Fraud is a $16 B year problem in the USA alone. With web receipts and better copying capabilities, thieves can make multiple copies of a single receipt and make multiple returns for cash or other merchandise. Or they can bring back shoplifted items and try to exchange for cash. The problem is that often the associates in Returns department don’t have access to past sales information and can’t keep track easily of returned merchandise. This is especially problematic if the policy is to make returns without receipts. So the solution is straightforward: hook up the Point of Sale systems so within seconds, the Teradata data warehouse is updated with sales, return, exchange, and void data, and provide the Returns department with the entire history of purchases by that customer,, so they can ensure that a sold product can only be returned once. The impact? Huge, according to one Teradata customer who has already built this system. They stopped a crime ring in the first day of their rollout, a group that had defrauded the company of thousands of dollars. They saw a 100% payback on their investment in just 5 months, and continue to reap the benefits of this example use of Active Enterprise Intelligence. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{70C11B6E-24EE-4FD8-BEBE-FBCB0DD1152C}}
  • [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{105DCFB0-BB36-405F-9D32-9E1A3CF1E66B}}
  • Retail Fraud is a $16 B year problem in the USA alone. With web receipts and better copying capabilities, thieves can make multiple copies of a single receipt and make multiple returns for cash or other merchandise. Or they can bring back shoplifted items and try to exchange for cash. The problem is that often the associates in Returns department don’t have access to past sales information and can’t keep track easily of returned merchandise. This is especially problematic if the policy is to make returns without receipts. So the solution is straightforward: hook up the Point of Sale systems so within seconds, the Teradata data warehouse is updated with sales, return, exchange, and void data, and provide the Returns department with the entire history of purchases by that customer,, so they can ensure that a sold product can only be returned once. The impact? Huge, according to one Teradata customer who has already built this system. They stopped a crime ring in the first day of their rollout, a group that had defrauded the company of thousands of dollars. They saw a 100% payback on their investment in just 5 months, and continue to reap the benefits of this example use of Active Enterprise Intelligence. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{70C11B6E-24EE-4FD8-BEBE-FBCB0DD1152C}}
  • Before process – very labor intensive, not scalable, error-prone
  • New process through multiple channels into a centralized customer database (using Teradata). The (realtime) scoring done via SPSS based on data feed from Teradata. Processing time cut from six working days to 30 sec for retail and to 2 minutes for commercial banking.
  • The credit decisioning challenge was around three key dimensions: Need to automate to improve efficiency Data consolidation and external sourcing to collect info about customers (in the Baltic, very few people have credit history) Realtime credit scoring ability due to the need to automate
  • This large brand-name consumer travel company uses both the web and email to provide vacation bundle offers. The situation was that the reaction rates for both of these were not meeting goals. The main problem was that the offers were too “generic”, e.g., not relevant for most consumers. The solution was to use information from Teradata about the customer web browsing behavior to better calibrate the offers – both on the web and via emails. The impact was outstanding – a 3-5 times improvement in the customer “take rates”. In addition, the compmany found that they were able to provide customers with more focused web offers using their existing system – 1.3M personalization queries per day created only a 3% increase in the workload for the system. More info: Solution: Leveraged the Teradata Active Data Warehouse capability to display customer specific ads during a shopping visit. Industry studies have shown that personalized offers which are compelling to the customer and presented at the keenest time of interest have response rates up to 12 times that of non-specific offers. Impact: Personalized offers are much more effective than generic offers. Should the warehouse not be available, the fall back position is a generic offer; which doesn’t stop business, just reduces the effectiveness of the ads to what they were before personalization. Leveraged existing data in the warehouse enabled rapid implementation. Tactical single amp queries consumed only a tiny portion of the system, even though the volume is high. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{8B595D92-4D1A-4551-87D7-3B5AA0921C33}} Situation: E-mail campaigns, while well targeted, were not gaining in effectiveness in part due to becoming more and more of the marketing noise that consumers get hit with. Generic offers displayed during a shopping session on the web site were only achieving the low response rates typical in the industry. Problem: Generic offer displays did not leverage the detailed customer information contained in the data warehouse. It would take thinking outside of the box to connect the web site, a mission critical customer facing application, to the Teradata data warehouse in order to leverage the detailed customer information in real time.
  • This page came from the PARTNERS 2006 presentation Booking Services: this is a transaction to book this trip choice. Originally this was called “looking and booking” because many times people search the site but don’t buy, especially for people that they already know via cookies. All of this is captured in the operational systems that run the website. Capturing click stream information to determine propensities to purchase in the future. So, if a deal was served up, we also have to record the deal at that moment because the same deal may be offered to 100s of people – all at different prices. It is therefore necessary to capture the click stream information and save the summary of key values in the stream to be sure what was offered at run time. Merchandising services is a layer of processes to do the active intelligence. From consumer requests, I can bring back the correct deal. This is where we find the customer matching process. The streaming (preset times daily – different for the type of data being loaded) and batch (overnight). They do this to assure they don’t bombard the EDW during prime times with data loads. Prime time is “daytime in the US”. They do a lot of the batch work in the evening or night. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{C8E81D4E-895F-4B85-9452-253B1674D205}}
  • Teradata’s goal is to help you transform Insight into Actions We do that by providing world-class database technology to create both Strategic and Operational intelligence. By strategic intelligence, we mean the insights that back-office workers create by using BI tools and analytic packages to build reports, write ad-hoc analytical queries, create predictive models, and then use these insights to create pre-planned customer dialogues and plans. Cycle times for developing these kinds of strategic insight activities take typically hours or days, sometimes weeks. By contrast, operational intelligence is the application of strategic intelligence at the front-line, at the point of customer contact. It is the application of historical context, current state, and predictive insights like the next best offer, specialized for one specific customer – the one standing in front of a bank teller, or at a check-in kiosk, or checking out of a retail store on the web or in person. In this case, the operational systems sense inputs (what does the customer want), assess and compare the inputs to the dialogue, and use that to drive a timely and relevant response. Cycle times for operational intelligence are fast – each step of the response may take under a second. For example, it could be a dialogue sequence of customized screen pages for a call center agent to use, a sequence of web pages to be rendered for that customer as she clicks, or a sequence of screens that a bank teller might see. You will go around this cycle numerous times on the right within one session with a customer. Then the customer walks away, and the results are captured and uploaded from the frontline system back into the database for further analysis. The insight cycle begins again, perhaps after hundreds or thousands of customers have interacted with the company. Changes are noted. By analyzing the data, refining the insights and dialogues, you can make them better for use the next time.
  • Lead: Businesses look to agility for differentiation, but it can be elusive. Study: The Economist (Intelligence Unit), Organizational agility: how businesses can survive and thrive in turbulent times, March 2009 (Conducted early 2009, 349 business executives around the world (UK, France, Germany, Singapore, US, Australia, New Zealand, Canada), 19 industries) Executives also said that Nearly 90% of executives surveyed believe that organizational agility is critical for business success. Organizational agility is a core differentiator in today’s rapidly changing business environment. Agility is essential to sustaining a strong competitive edge. It is and will be a core differentiator. One-half of all chief executive officers (CEOs) and chief information officers (CIOs) polled agree that rapid decision-making and execution are not only important, but essential to a company’s competitive standing. More than 80% of corporate executives have undertaken one or more change initiatives to improve agility over the past three years alone. More than a third, 34%, say the efforts have failed. On performance, the Massachussetts Institute of Technology (MIT) says: Agile organizations increase their revenue 37% faster than non-agile organizations. And, that they generate 30% higher profits from the revenue they pull in. Transition: The recent economic crisis has businesses rethinking the importance of agility.
  • It’s an organization that exhibits the agility to be able to: quickly gather intelligence creatively and nimbly act or react take advantage of changing conditions. move with strength or power And How Agile is your organization? Is their functional transparency throughout? Has consistency infiltrated every corner? Do you move faster? Can the organization handle More…always? Is there a means of readily seeing through the complexities? Are your strategies breakthrough? Is innovation part of the culture? Can you recall the last time resistance won out over change?
  • Why Teradata? Teradata enables organizations to be more intelligent, be more powerful, and create breakthrough strategies for their business . Intelligent It’s not just about making better decisions – it’s making the best decisions on the right issues to impact business performance. Intelligence that breeds the best decisions, breakthrough innovation, and decisive actions will deliver maximum value in the short and long term. Teradata enables a new visibility to your business by eliminating the black holes and delivering consistent insight across user functions and roles. Just look at JCPenney for example, during the worst stretch of retail downturn in our lifetime, was able to increase operating income by $63M+ eliminating black holes and tying sales trends to inventory. Powerful Today’s Businesses need power to outpace competition, handle more, and conquer the complexities of today’s environment. Teradata makes companies more powerful with: Our Unmatched speed in data mining from 100% parallel design An Architectural flexibility that adapts to the technical differences of your business A Storage capacity and accessibility that provides answers regardless of size or diversity of your business Optional – Look atFor example, download giant Real Networks replaced their massive royalty payment process by employing an existing Teradata data model to achieve better results faster and in the process created a view into lifetime customer value. Breakthrough Teradata creates new breakthrough strategies for your business, true category changing innovation, advantages sustainable over time. Teradata does this by allowing you to: Use intelligence and powerful solutions to feed a culture of innovation Partner with a professional support team steeped in industry expertise Network with other customers who lead their industries Optional - For example, in a tough industry Enterprise Rent-A-car gained competitive advantage by streamlining information delivery to the various parties involved in repairs and service… to provide an better experience for customers.

Maximizing Business Value: Optimizing Technology Investment Maximizing Business Value: Optimizing Technology Investment Presentation Transcript

  • Maximizing Business Value Optimizing Technology Investment Randy S. Lea Vice President, Products and Services Marketing Masters 2010
  • Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  • Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  • Teradata - Parallel Everywhere! Predictable, Scalable Performance Teradata Master node bottleneck 2-tier bottleneck “ Intelligent” Storage Database Software Host Node Enabling business growth and success! loader loader
  • Teradata – Easy to Manage The Database Should Manage Data, Not People Teradata DBA Self managing administration Few or automatic controls “ Bolt-On” Parallelism 100s of controls and parameters RDBMS resists change block sizes extents reorgs file placement buffer sizing temp space many indexes sort/spool space hints parallel threads partition, repartition memory controls static priorities constant monitoring data distribution sort area I can help you with that business problem
  • Teradata Viewpoint: Self Service Portlets My Queries
    • My Queries
    • Provides information about queries and sessions
      • Check your running query
      • Detailed view drill down on selected session
    • For business users, developers, DBAs
      • Power users will use drill down
    drill down Copyright Teradata © 2008 – All rights Reserved
  • Teradata Viewpoint: Capacity HeatMap Portlet
    • Analyze hotspots
      • Selectable metrics
      • Helps find time slots to run resource-intensive jobs
    • For DBAs, operations, developers, power users
    • “ Tuesday at 4PM is a good time to run a big report”
  • Teradata Viewpoint: Rewind Feature Scroll Back in Time Like Your Home DVR Scrolling back through history “ My query ran a long time this morning”
  • Industry’s Most Mature Optimizer Advanced Query Rewrite Capability
    • More than 30 years of “data warehouse” maturity
      • Supporting and learning from the most sophisticated data warehouse customers in the world
    • Why is this important?
      • Not everyone is a SQL expert
      • SQL from BI tools not optimized
    SELECT * FROM Sales1 WHERE EXTRACT(MONTH FROM Sales_Date) = 1 UNION ALL SELECT * FROM Sales2 WHERE EXTRACT(MONTH FROM Sales_Date) = 1 ; SELECT * FROM Sales1 WHERE EXTRACT(MONTH FROM Sales_Date) = 1; Before Re-write After Re-write It’ll even correct some of your mistakes!
  • Would Any of These be of Value to You? Dynamic Control of System Resources
    • CEO gets highest priority on his requests
    • Finance users get high priority between 8:00am and 10:00am and 1:00pm and 3:00 and then switch to low
    • Marketing queries that take longer than 10 minutes between 7:00am and 11:00am move to medium priority
    • User Randy gets high priority when running a compliance request
    • User Randy gets low priority when running a compliance analysis report
    • User Darryl can run no more than 10 queries at a time between 8:00am and 6:00pm
    • Web and call center applications get high priority 24 hours a day
  • Teradata Active Systems Management Some Workload Controls Date and time range controls Periods Exempt specific users from all rules Bypass Privileges Limit concurrent load and export jobs Load Utility Throttles Limit logons and/or queries active by account or performance group Object Throttles DDL, DML, select Type of SQL Databases, tables, views, macros, stored procedures Query Objects Time, row counts, joins, scans Query Resource Rules Parameters for a user or group of users Profiles
  • Viewpoint: Workload Monitor Portlet Teradata Ease of Use Number of Incoming Queries Outside of SLG Number of Queries After Filters and Throttles Exception Reclass Filter Reject Throttle Delay Throttle Reject Exception Reject
  • Workload Priority vs Actual Consumption Managed Dynamically by the Database R M H L Actual Server Utilization Workload Assigned Priorities Relative Weights Used R Real time – 47% H Tactical – 45% M Loads – 5% L DSS Queries – 1% L M H R
  • Teradata 12 New Releases Every 12-18 Months
    • ~30% improvement in query performance
    • ~20% improvement in archive
    • ~20-60% load improvement with SQL Bulk Load
    • ~30% query performance with Multi Level PPI
    • ~50% improvement in checktable performance
    • ~30% query performance with parameterized statement caches
    High Performance Innovation
  • Teradata 13 Continued Innovation
    • Teradata Virtual Storage (TVS)
    • Native Geospatial Types and Functions
    • Large Memory Support
    • AJI Enhancements
    • TASM Ruleset enhancements
    • Statistics Enhancements
    • Lock Manager Fault Isolation
    • 128 Way Joins
    • Java User Defined Functions
    90+ Release Features And the list goes on…..
  • Teradata Virtual Storage Automatic “Temperature Aware” Data Migration
    • Storage subsystem software product -
      • Operates between the Teradata database and storage array;
      • Optional product
    • Automatic Data Placement
      • Based on access patterns
      • Frequent = Hot
      • Less frequent = Colder
    • Managed by the database, not your DBAs!
    Teradata Active EDW Nodes Storage AMP AMP AMP TVS
  • Automatic data migration is what makes Teradata Virtual Storage a disruptive innovation… 6 sectors/rev 12 sectors/rev Automatically Place Data on Faster Sectors Teradata Virtual Storage 6 sectors/rev Slower Access “ Cold” Data 12 sectors/rev Faster Access “ Hot” Data
  • The Teradata Future…….
    • an Always-On , self healing database,
    • that is a Hybrid row and column store,
    • is S impler to manage and use,
    • Supports Time Travel data warehousing,
    • has built-in autonomic M anagement ,
    • is world leading in M ixed Storage support,
    • and runs on our A ppliance family and the public and private C loud
  • Teradata – The Clear Data Warehousing Leader Leadership in Execution and Vision Ability to Execute Completeness of Vision Gartner Magic Quadrant for Data Warehouse DBMS, 2009 The Magic Quadrant is copyrighted 23 December 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
  • Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  • Data Silos Limit Business Value
    • Limited Business Value
    • Each data mart can provide answers to subject-specific questions
    • With each new data mart, IT repeats its development efforts
    • This includes sourcing data that already exists in another environment
    Channel 29 Claims 31
    • Claims
    • Which states have exceeded expected targets by more than 20%?
    • Channel
    • What is the personal auto production by distribution channel?
    Business Value IT Development Duplicated Data
  • Integrated Data Enables Superior Value
    • Differentiated Business Value
    • Combining the environments requires only incremental work for each new subject area
    • Enables new cross-functional insights that can’t be achieved with separate data marts; new differentiated decisions
    Claims Channel 29 31 Business Value IT Development 70
    • Combined Claims and Channel
    • Which agencies have personal auto claims experience 20% higher than previous year?
    Shared Data
  • Integrated Data Enables Superior Value
    • Differentiated Business Value
    Claims 31 Channel 29 Business Value IT Development 189 70
    • Combined Claims, Channel, Customer
    • What is the renewal rate of personal auto customers who have filed at least one claim over the past five years, segmented by agency and household policy ownership (auto, homeowners, umbrella, boat, etc)?
    Customer 43 Financial 64 Customer 43
  • Integrated Data Enables Superior Value
    • Differentiated Business Value
    Claims 31 277 189 Channel 29 Customer 43 Financial 64 Business Value IT Development
    • Combined Claims, Channel, Customer, Financial Information
    • What is the profitability of personal auto customers with at least one claim over five years, by agency and household policy ownership?
    Financial 64
  • Integrated Data Enables Superior Value
    • Teradata customers find that as their warehouses grow, they add incrementally less data and less effort to enable new applications and new business value
    Claims 31 277 Channel 29 Customer 43 Financial 64 Business Value IT Development
  • Insurance: Data Reuse Analysis Faster Time to Market on New Insight and Apps Risk Management Optimization Underwriting Risk Analysis Product and Customer Alignment Sales Reporting and Performance Analysis Claims Payment Management Consistent Compliance Infrastructure Assurance Customer Acquisition Customer Communications Management Customer Retention New Business Improvement Opportunities through Data Leverage Channel Communications 100% 82% 75% 63% 57% 74% 83% 53% 62% 54% 24% 100% 25% 11% 21% 56% 22% 10% 18% 9% 64% 71% 100% 47% 45% 64% 64% 49% 54% 38% 27% 17% 24% 100% 47% 26% 38% 36% 31% 47% 28% 35% 26% 53% 100% 52% 32% 39% 29% 57% 16% 41% 16% 13% 23% 100% 8% 6% 14% 11% 56% 52% 66% 31% Customer Retention 50% 32% 65% 17% Customer Communications Management 50% 57% 69% 26% Customer Acquisition 58% 83% 78% 62% Consistent Compliance Infrastructure Assurance 44% 79% 65% 67% Claims Payment Management 26% 29% 72% 29% Channel Communications 100% 71% 65% 45% Product and Customer Alignment 49% 100% 50% 55% Underwriting Risk Analysis 40% 45% 100% 30% Sales Reporting and Performance Analysis 54% 95% 58% 100% Risk Management Optimization If Then
  • Insurance: Data Reuse Analysis Faster Time to Market on New Insight and Apps New Business Improvement Opportunities through Data Leverage
  • Architecture Alternatives Distributed, Hub and Spoke, Federated Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners Optional ETL Hub Hub and Spoke Enterprise Information Integration Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
  • Architecture Alternatives Distributed, Hub and Spoke, Federated Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners Optional ETL Hub Hub and Spoke Enterprise Information Integration What are some of the challenges? 1. There are too many copies of the data. 2. There is too much latency . Everyone sees different data with inconsistent points in time. 3. The solution is too complex . Multiple ETL processes and increased security risk. 4. The solution is too expensive . DBAs, system admin, maint., underutilized servers, data models. Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
  • Advocated Architecture by Teradata Enabling Superior Business Value Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners How is Teradata different? 1. Dramatic reduction in the number of copies of the data. 2. Reduced latency time with fewer input and access paths. 3. The architecture is simplified for faster solution development and quicker ROI! 4. Proven to be less expensive . Lower Total Cost of Ownership! Much fewer systems, people and processes. Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
  • Teradata’s Active Enterprise Intelligence™ Five Stages of Evolution and Maturity Continuous update and time-sensitive queries become important OPERATIONALIZING WHAT IS happening? Event-based triggering takes hold ACTIVATING MAKE it happen! Primarily batch and some ad hoc reports Increase in ad hoc analysis ANALYZING WHY did it happen? REPORTING WHAT happened? Analytical modeling grows PREDICTING WHAT WILL happen? Workload Sophistication Scalability Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
  • Capability Database and Platform Requirements Enablement for Superior Business Value Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance X Database Functionality X Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance XXX X Database Functionality XXX X Data Scalability XX Concurrency XX Cross Functional X Availability Workload Management Investment Protection Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance XXXX XXX X Database Functionality XXXX XXX X Data Scalability XXXX XX Concurrency XXXX XX Cross Functional XXX X Availability XX Workload Management XXX Investment Protection Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability XX Extreme Performance XXXXX XXXX XXX X Database Functionality XXXXX XXXX XXX X Data Scalability XXXX XXXX XX Concurrency XXXX XXXX XX Cross Functional XXX XXX X Availability XXXXX XX Workload Management XXXX XXX Investment Protection XXXX Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability XXX XX Extreme Performance XXXXX XXXXX XXXX XXX X Database Functionality XXXXX XXXXX XXXX XXX X Data Scalability XXXXXX XXXX XXXX XX Concurrency XXXX XXXX XXXX XX Cross Functional XXXXXX XXX XXX X Availability XXXXX XXXXX XX Workload Management XXXXXX XXXX XXX Investment Protection XXXXX XXXX Active Access/Loads
  • Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Capability Database and Platform Requirements Enablement for Superior Business Value Seven Customers in the Teradata “Petabyte Club” Proven References Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value “ Intelligent Scanning”, In-Database Analytics, Triggers, Stored Procedures, etc. Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Millions of Queries – Reports, Ad-hoc, Complex Analytics, Data Mining, Web Site, Call Center, etc Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value 128-Way Join Capable with the Most Mature Data Warehouse Optimizer in the Industry! Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Full Performance Continuity with Hot Standby Nodes/Disks and Dual “Active” Systems Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Achieving “Business SLG’s” Industry’s Most Advanced Workload Management Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Multi-Generation Node Co-Existence Reduce “Floor Sweep” Upgrades Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Batch and Continuous Loads While Supporting “Back Office” and “Front-Line” SLG’s Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Recognized as the Industry’s Leading Data Warehouse Platform Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Database and Platform Requirements Enablement for Superior Business Value Recognized as the Industry’s Leading Data Warehouse Platform “ Appliances” Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Teradata Purpose-Built Platform Family Capability Extreme Performance XXXX XXX X Database Functionality XXXX XXX X Data Scalability XXXX XX Concurrency XXXX XX Cross Functional XXX X Availability XX Workload Management XXX Investment Protection Active Access/Loads
  • Teradata Purpose-Built Platform Family Capability Data Warehouse Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Teradata Purpose-Built Platform Family Capability Extreme Performance XXX X Database Functionality XXX X Data Scalability XX Concurrency XX Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Data Mart Appliance
  • Teradata Purpose-Built Platform Family Capability Extreme Performance XXXXX XXXXX XXXX XXX X Database Functionality XXXXX XXXXX XXXX XXX X Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Teradata Purpose-Built Platform Family Capability Extreme Data Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
  • Teradata Purpose-Built Platform Family Data Warehouse Appliance Data Mart Appliance Capability Extreme Data Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
  • Australian Pharmaceutical Industries Data Mart Appliance
    • API is one of Australia’s leading providers of products and services to branded and independent community pharmacies. It provides daily distribution of products to pharmacies through its network of distribution centers and regional branches
    • Reasons API choose Teradata
      • Performance
      • Integrated Data View
      • Scalability
      • Affordability
      • Low TCO
    • “ Our new Teradata Data Mart Appliance is the beginning of our enterprise data warehouse implementation program and has already resulted in faster and more accurate information delivery to the Pharmacy Division, Since replacing our SQL Server-based environment with Teradata, we are gaining access to significant new insights and opportunities to do business more efficiently and effectively. We took on the majority of the implementation ourselves and we have maintained our self-sufficiency post implementation. By using our existing IT resources, together with the decision to start with an entry level appliance platform, we have been able to put together a cost effective solution that is showing ROI.”
      • – Brett Hart, Application Manager, API Pharmacy Division
  • Australian Pharmaceutical Industries Data Mart Appliance
    • API is one of Australia’s leading providers of products and services to branded and independent community pharmacies. It provides daily distribution of products to pharmacies through its network of distribution centers and regional branches
    • Reasons API choose Teradata
      • Performance
      • Integrated Data View
      • Scalability
      • Affordability
      • Low TCO
    • “ Our new Teradata Data Mart Appliance is the beginning of our enterprise data warehouse …. already resulted in faster and more accurate information delivery to the Pharmacy Division. Since replacing our SQL Server-based environment with Teradata, we are gaining access to significant new insights……. We took on the majority of the implementation ourselves and we have maintained our self-sufficiency post implementation. By using our existing IT resources, …. we have been able to put together a cost effective solution that is showing ROI.”
      • – Brett Hart, Application Manager, API Pharmacy Division
  • AirCel – India Active EDW and Extreme Data Appliance
    • AirCel is India’s seventh largest GSM mobile cellular service provider with a subscriber base of over 9.43 million.
    • Reasons AirCel bought Teradata
      • Enterprise Integration
      • Price point for specific application
      • Scalability
      • Affordability
    • AirCel’s Use of Active EDW 5550
      • Migration of their core data warehouse functions to Teradata
    • Aircel’s use of 1550 Appliance
      • Retain large volumes of Call Detail Records (CDR) to fulfill law enforcement compliance requirement
      • 7 years of CDR history offers valuable customer insight
  • Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse Extreme Data Appliance Data Warehouse Appliance Data Mart Appliance Extreme Performance Appliance
  • World’s Fastest Data Warehouse Extreme Performance Appliance
    • Teradata Blurr Technology ™
    • Fast and consistent query response times
      • When you need to know Now!
    • Scan and aggregate millions of rows in less than a second
      • Hyper Analytics for truly operational BI
    • Up to 20X Overall Performance Improvement
      • Over Industry Leader – Teradata
      • Not just one query or load job – Overall!
    100% Solid State Drive Appliance
  • Mechanical Rotation and Seek Limit HDD Speed 22X Faster on Typical Data Warehouse Workloads Mechanical Rotation and Seek Limit HDD Speed data data data data Database Server Database Server
  • Eliminating the Bottleneck! Direct Connect Array Controller Data Protection Enabled by Fallback Extreme Performance Appliance Competitive Systems Array Controller kmh
  • Teradata SSD versus Typical Flash Cache Focused on Data Warehousing, not OLTP! HDD Flash Cache 2.4 TB Data Space per Node ~.4 TB Data Space per Server Data Loads Data Loads Consistent Query Response Non Cache Hits Up to 22X Slower Unix and Windows Servers Teradata Node
  • Extreme Performance Appliance 4600 Target Application Environments
    • Hyper analytics for Operational BI
      • “ Beyond near real time” business environments needing consistent response to events in sub-second windows
      • Sense and respond to business-critical events immediately - when they are most valuable
    • Targeted at 100% hot data applications
      • Blazing fast operational queries
      • Aggregate millions of rows in a second
      • Complex analytics at the speed of thought
      • Super fast data loads
  • Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse Extreme Data Appliance Data Warehouse Appliance Data Mart Appliance Extreme Performance Appliance
  • 6 sectors/rev 12 sectors/rev Automatically Place Data on Faster Sectors or Drives Teradata Virtual Storage 6 sectors/rev Slower Access “ Cold” Data 12 sectors/rev Faster Access “ Hot” Data 12 sectors/rev Faster Access “ Warm” Data SSDs Fastest Access “ Hot” Data What If You Could…? Teradata Virtual Storage data data data Database Server
  • View of the Future Leverage SSD for Multi-Temperature Data 300GB SAS Drives 1 TB SAS Drives Solid State Drives (SSD) Traditional Performance On-Line Compliance /Archival Extreme Performance Teradata Active Enterprise Data Warehouse Auto-migration w/TVS
  • Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SAS Drives Solid State Drives (SSD) Active Enterprise Data Warehouse Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Integrated Auto-migration w/TVS
  • Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SAS Drives Solid State Drives (SSD) Active Enterprise Data Warehouse Integrated Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Auto-migration w/TVS
  • Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SATA Drives Solid State Drives (SSD) Integrated Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Auto-migration w/TVS In-database operations Data Movement Hot <-> Cold Inter-system operations Harder Ease of Workload Management Easier Single Database Integrated Data Silo’d Data (data model consistent) ~.8X (est.) Price 1X (est.) In-database operations Query Access Inter-system operations Single system simplicity Ease of Data Management Multi-System complexity EDW Single System / TVS Architecture Data Marts Multi-systems
  • Teradata Analytical Ecosystem Management Monitor and Manage All Teradata Systems Data Integration Business Intelligence Enterprise Data Warehouse Dual Active Production #2 Teradata Data Mover, Replication Services, Dual Load, ETL Partners Active Enterprise Data Warehouse Advanced Workload Management Master Data Management Teradata Multi-System Manager Monitoring, Alerting, Administration, Control Teradata Viewpoint Single Operational View of All Systems Test & Dev Extreme Data Data Mart BAR
  • Teradata Is About Choice Choose an Architecture to Meet Your Business Teradata Data Mover, Replication Services, Dual Load, ETL Partners Active Enterprise Data Warehouse Analytical Ecosystem Management Extreme Performance DSS Single, Integrated Active Data Warehouse SSD Entrp. HDD Fat HDD Unlimited Business Value Architecture Flexibility DSS Extreme Performance Extreme Data Extreme Data
  • Teradata Is About Choice Choose an Architecture to Meet Your Business
    • Teradata; Focused on
    • removing the technology barriers that inhibit the realization of Active Enterprise Intelligence™
    • a Platform Family with a price point for every need and architecture
    • on maximizing your business value plus optimizing your technology investment
    Extreme Performance DSS Single, Integrated Active Data Warehouse SSD Entrp. HDD Fat HDD Unlimited Business Value Architecture Flexibility Extreme Data
  • Teradata Express for VMWare Player Free Developers Edition
    • Free VMWare image
      • Teradata 12 + SLES10
      • Teradata 13 + SLES10
      • Up to 1TB database size
      • Non-production use
    • Intel servers and PCs
    • Runs on
      • 64-bit capable machines
      • Requires VMWare player
  • Teradata Express for Amazon Free Developers Edition
    • Free Teradata software
      • Teradata 13 + SLES10
      • 1TB disk limit
    • Runs in Amazon Web Services
      • Working with Teradata Engineering
      • Amazon has a Teradata EDW
    > Oct. 6, 2009 data sources EBS
  • Trusted Advisor on Your Architecture With Teradata It’s Your Choice Cloud Enterprise Data Warehouse Data Marts Star Schema Active Data Warehouse™ Sand Boxes 3 rd Normal Form Hub and Spoke Teradata is the only database that does not lock you into a sub-optimal enterprise approach to data architecture, but supports a cross-section of approaches to address all analytical requirements.
  • Benefits of Teradata’s Architectural Flexibility Investment Protection
    • Start Small at Any Price Point and Grow
    • Leverage Existing Resources and Tools
      • DBAs, systems administrators, and application developers can support multiple systems and leverage existing tools without additional training
    • Easily Migrate Applications Between Teradata Platforms as You Optimize Your Environment
      • Leverage same data, data model, table structures, views, queries, and load programs
    • Evolve to Active Enterprise Intelligence™ Over Time
      • Flexibility to meet short and long-term analytic data integration requirements
  • Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse 20-40+% wasted moving data OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
    • BI Tool Becomes ETL Tool from the Warehouse
    OLAP Architectures Various Amounts of Data Being Moved Physical Cube (MOLAP) Multi-Dimensional View Data Warehouse OLAP Engine (ROLAP) Hybrid Approach (HOLAP) 0 1101 1001 0101 1011 10110111011 01010011101 10110111011 10110111011 01010011101 10110111011 01010011101 10110111011 01010011101 10110111011 1 0 1 Marketing Executive Power Users Analyst CUSTOMER CUSTOMER NUMBER CUSTOMER NAME CUSTOMER CITY CUSTOMER POST CUSTOMER ST CUSTOMER ADDR CUSTOMER PHONE CUSTOMER FAX ORDER ORDER NUMBER ORDER DATE STATUS ORDER ITEM BACKORDERED QUANTITY ITEM ITEM NUMBER QUANTITY DESCRIPTION ORDER ITEM SHIPPED QUANTITY SHIP DATE
  • Teradata Business Intelligence Optimizer Building Dimensional Structure in Teradata TBIO Aggregate Designer TBIO Schema Workbench Excel or MDX Client Optimized SQL Excel or MDX Client BI Tool Schema Definitions TBIO Schema Definitions MDX AJIs
  • Teradata Business Intelligence Optimizer Building Dimensional Structure in Teradata TBIO Aggregate Designer Optimized SQL Schema Definition BI Schema Definition MDX Excel or MDX Client AJIs Teradata Schema Workbench
  • Teradata Aggregate Designer Workflow Schema Definition BI Schema Definition AJIs Teradata Aggregate Designer Teradata Schema Workbench Optimized SQL
  • Microsoft Excel PivotTables in Action Direct Connect to Teradata
  • Microsoft Excel PivotTables in Action Direct Connect to Teradata
  • OLAP Optimization Results Landline Communications Provider
    • 38 dimensions/24 measures with 5 years of history
      • Add 39 th dimension: Wire Center
    • Maintenance: 13 hours to 3 minutes
    • Cube size: 22.4 GB to <10GB
    • Detail: Month to Daily
      • Response Comparison:
    OLAP Server Optimized with Teradata
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • In-Database Data Mining Optimization More Models and More Business Value Sample Data Desktop and Server Analytic Architecture Results Enabling Mining In-database Results In-Teradata Analytic Architecture Database Processing from Hours to Minutes Data Mining Process from Days to Hours
  • Cabela’s Integrated Data Mining More Business Value
    • Before: Siloed data mining environment
      • 8 TB of data redundant
      • 3 ½ week old data impacted quality of analytic intelligence
    • After: Integrated data mining into Teradata
      • Eliminated 6 TB of redundant data
      • Efficiency tripled analytic output
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Teradata Geospatial Solution: The Integrated Environment for Faster Analytics GIS Data Mart Teradata System Geographic Information Systems Approach Teradata In-database Approach BI and GIS reports, Tools and applications Geographic data Address coordinates GIS applications EDW IT Department BI reports, tools, and applications Before After IT Department
  • Target Deliver Service to Profitable Customers Can’t be Done with Zip Code Alone
    • Customer
    • Segment:
    • >=$50
    • Profitability
    • Score <75
    • >=$50
    • Profitability
    • Score >=75
    Regions 3 mile radius 15 mile radius *Deliver Zone Customer Store Zipcodes 92024 92009 92024 92009 Bring the Analytics to the Data, Not the Data to the Analytics
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Business Need for Agile Analytics Flexibility vs. IT Process – Bridging the Gap!
    • Analyze quickly
      • New theory
      • New data
    • Fail fast
      • Was the theory right?
      • Does the new data provide additional insight?
    • Productionize what works
      • Add the new application
      • Add the new data
      • Or leave it “as is”
  • Traditional “Sand Box” Environments Duplicated Systems and Processes
    • Data moved across the enterprise
      • Cost of servers
      • Cost of data movement
      • Cost of “shadow IT”
    • Control and quality issues
      • Security
      • Privacy
      • Completeness of data
      • Freshness of data
    Data warehouse and multiple data marts or sand boxes Active Data Warehouse Base tables
  • Teradata’s Agile Analytics Cloud Almost Free!
    • An internal private cloud
      • On-demand self-service
      • Broad network access
      • Resource pooling [virtual]
      • Rapid elasticity
      • Measured Service [pay per use]
    • Enablers
      • Elastic Mart Builder
        • Free Viewpoint portlet
      • Teradata Active System Management (TASM)
      • Professional Services
    Analytic sand boxes inside the production Teradata system Active Data Warehouse Base tables
  • Elastic Mart Builder Simple, Self Service Provisioning Verify Data Table Name Integer Date Char Decimal Elastic Marts POC Sand box Virtual mart Enterprise Data Warehouse xyz Col3 PI Type 123 abc Sample Col2 Col1 CSV Upload File Upload Password Myfile.csv File name User Name Elastic Mart Builder Create UserName MySpecialID Password Perm space 500MB Import 1 2 3 4 5
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse 20-40+% wasted moving data Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on what is going to happen? Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on what is going to happen? Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse
    • Superior End-to-End Analytics!
    • Query processing orders of magnitude faster (In-database processing)
    • Analytic processes improved by hours and days
    • Data warehouse investment is optimized for analytics, not data movement between systems
    OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on what is going to happen? Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
  • Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  • Teradata Vision
    • ACTIVE ENTERPRISE INTELLIGENCE™
      • Integrate and manage data across the enterprise for strategic intelligence
      • Extend operational intelligence to front-line workers, suppliers, and customers
  • Active Enterprise Intelligence™ An Obvious Trend: More Speed, More Users Days Seconds 1,000s to 1,000,000s of “Front Line” Operational Decisions 100s of Corporate “Knowledge Workers” Operational Intelligence Strategic Intelligence
  • Active Enterprise Intelligence™ An Obvious Trend: More Speed, More Users Days Seconds
    • Call Center – Customer History
    • Gate Agent – Event Notification
    • Web – Personalized Offers
    • Sales – Product Profitability
    • Marketing – CRM
    • Manufacturing – Demand Chain Management
    Operational Intelligence Strategic Intelligence
  • Active Enterprise Intelligence™ An Obvious Trend: More Speed, More Users Days Seconds EDW Enterprise Integration SOA, BPMS, IDEs Portals/composite applications Mixed workload management Enterprise Data Warehouse BI Tools and reports Analysis and visualization Predictive Analytics Operational Intelligence Strategic Intelligence
  • Active Enterprise Intelligence™ in Retail Detecting Retail Fraud Associates query Teradata to quickly check if a return has already occurred on that receipt number. Also used by analysts to understand and prevent excessive returns. Solution Associates in returns department did not have historical POS receipt retrieval access to verify against previously “returned” receipts or to do returns without receipts. Problem Thieves make copies of cash register receipts, walk into the store, pick up merchandise, and return items for cash. Situation
  • Return Receipt Validation Process POS  Web Service  Teradata  POS Valid Return or Fraud? Teradata Web Service
    • Validate returns
    • Re-calc refund
    SOAP XML ODBC I’d like to return this item Stored Procedures ItemValidation GetTranDetails GetRootTransaction GetTransaction
  • Active Enterprise Intelligence™ in Retail Detecting Retail Fraud Associates query Teradata to quickly check if a return has already occurred on that receipt number. Also used by analysts to understand and prevent excessive returns. Solution Associates in returns department did not have historical POS receipt retrieval access to verify against previously “returned” receipts or to do returns without receipts. Problem Thieves make copies of cash register receipts, walk into the store, pick up merchandise, and return items for cash. Situation
    • (for 500-store chain)
    • 100% ROI in 5 months
    • Stopped a crime ring on the first day of rollout
    • “ Cost savings have been huge”
    Impact
  • Credit D ecision - making P rocess B efore S cenario
    • 3 5 00 people involved
    • Issuing 900 000 credit contracts yearly
    • Loan contract issuing on average in 6 working days
    • Issu e cost per contract 240 EUR
    Gathering 360 data view on customer Decision Maker Level 1 Decision Maker Level 2 Credit Committee Manager releases money Prepares contract Paper based credit application Customer adopts credit proposal Paper based contract Archives documents Signed contract Paper based decision Paper based decision Paper based decision Prepare for decision Favorable or not decision with conditions 1 2 3 Customer and teller Contract
  • Improved C redit D ecision -m aking P rocess After Scenario and Future S cenario Approval in 15 Minutes Customer Branch Call center Internet ATM External data providers Real time data gathering Automated scoring Automated pricing Automated special conditions Internal data sources/EDW Result Monitoring Data gathering and credit decision in 5 seconds today! Contract in 15 min. retail customers | 30 min. for corporate customers
  • Active Enterprise Intelligence™ In Banking Faster Loans Situation EMEA bank with 3M customers had costly and slow manual process for loans and as s essing customer creditability . 3500 people issued 900,000 loans/year. Problem Lacked ability to access 360 0 view o f customer data (internal and external) and use this data for real-time credit decision making . Solution Used Teradata to drive real-time internal and external data consolidation and delivery of near- instant credit decisions . Impact
    • Before: Loan took up to 6 days, cost 240 Euros
    • After: Loan takes < 15 min and 32 Euros to process
    • Teradata risk rating portion: 5 seconds
    • Cut out human error in credit decision making
    • Payback time 8 months
    • Substantially decreased headcount
    • Annual ROI: 26 . 7 %
  • Active Enterprise Intelligence™ in Online Retail Personalized Offers Used Teradata to display personalized real-time web offers and send more focused email offers reflecting recent and specific interests during the web shopping session. Solution Offers presented were “generic” – did not make use of detailed customer specific information – origin, shopped destinations, travel interests. Problem Response rates of targeted e-mail campaigns were good but not increasing. Conversion rates of offers during web shopping visit also not meeting goals. Situation
    • Personalized offers are 3-5X more successful than generic offers
    • 1.3M additional queries per day to do personalization used less than 3% of system
    • Leveraged existing information in the data warehouse
    Impact
  • Presentation Architecture Round trip < 300ms
    • Predictive models
    • Targeted offers
    • Triggers, Alerts
    • Channel adapters
    EDW Data capture and load batch stream
    • Deal finding
    • Profiles
    • Lead price harvesting
    • Booking
    • Campaigns
    • Shopping
    Merchandising Services Customer ID Svc TAP Real-time BI Presentation Layer Booking Services Cookie or IP Address GDS Merchant Finance OLTP1 OLTP2 OLTP3
  • STRATEGIC INTELLIGENCE Active Enterprise Intelligence™ INSIGHTS ACTION OPERATIONAL INTELLIGENCE Insights Into Action Results Intelligence Respond Assess Sense Plan Report Analyze Predict
  • Teradata Leadership and Innovation Staying Ahead of the Competition The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
  •  
  • Teradata Business Messaging
  • Agility – The Key to Differentiation
    • “ The market turbulence of the past year may have foreshadowed a new phase of globalization, one in which (business) volatility is likely to remain a constant.”
    • Organizational Agility: How Businesses Can Survive and Thrive in Turbulent Times , The Economist Intelligence Unit
  • What Is Enterprise Agility?
    • Enterprise Agility
      • Effectively maneuver across any complex and dynamic business environment – overcoming obstacles and creating opportunities to drive superior results.
    Core Concept MANEUVER
  • Teradata Delivers Enterprise Agility INTELLIGENT POWERFUL BREAKTHROUGH
  • New Teradata Business Messaging Enterprise Agility Establish the most comprehensive and transparent view of the enterprise for smarter decision making. Cut through the complexities of business dimensions to execute strategies and drive results at any scale. Apply unmatched capabilities and expertise to foster game-changing innovation. Enhance Visibility Conquer Complexity Ignite Ingenuity
  • Business Messaging – Proof Points Enhance Visibility Establish the most comprehensive and transparent view of the enterprise for smarter decision making.
    • Connect data silos to eliminate information black holes
    • Provide access to consistent and comprehensive information in a role-specific context
    • Strengthen customer and supplier relationships through dynamic information access
    Conquer Complexity Cut through the complexities of business dimensions to execute strategies and drive results at any scale.
    • Anticipate and act with speed and confidence as business conditions change
    • Execute business initiatives systematically to achieve optimal decision making at all levels of the enterprise
    • Quickly answer any question across the entire value chain by integrating information from all parts of the business
    Ignite Ingenuity Apply unmatched capabilities and expertise to foster game-changing innovation.
    • Utilize a proven set of uniquely powerful and superior technologies
    • Develop sustainable advantages by partnering with an organization focused solely on enterprise analytics
    • Leverage the knowledge and practices of an elite network of customers and partners
  • Enterprise Agility Examples Enhance Visibility Establish the most comprehensive and transparent view of the enterprise for smarter decision making. Connect data silos to eliminate information black holes Found millions in revenue by evaluating customer behavior AT&T Mobility migrated customers to more profitable plans based on analysis of their call detail records and location. Customers who roamed excessively were profiled by handset purchase and home address, and moved to better plans to protect profitability. Provide access to consistent and comprehensive information in a role-specific context Recovered 8% of sales lost to pricing discrepancies Haggen combines business intelligence solutions to monitor every aspect of the business from operations to inventory to promotions. Store and regional managers have access to transaction data within 15 minutes of occurrence and can respond quickly to standardize pricing. Strengthen customer and supplier relationships through dynamic information access Connected 30,000+ internal and external users to self-serve insight Norfolk Southern cut out time and labor from directly addressing the queries of power users on shipment schedule and delivery. Building a web-based “Report Wizard” enabled NS partners and employees to run more than 10,000 types of customized reports without waiting for NS to help. Dramatically increased productivity and cut cost.
  • Enterprise Agility Examples Conquer Complexity Cut through the complexities of business dimensions to execute strategies and drive results at any scale. Anticipate and act with speed and confidence as business conditions change. Increased operating income by $63M during a retail downturn JCPenney uses DCM to support advanced forecasting models to reduce inventory for items that are experiencing a decrease in demand. By aligning inventory with sales trends as they happen, JCP sells more merchandise at retail prices and minimizes unsold merchandise moving to clearance. Execute business initiatives system-atically to achieve optimal decision making at all levels of the enterprise. Reduced costs by $90 million through productivity initiatives Wesco is an electrical product distributor for with over 130,000 customers, 24,000 suppliers, 370 branches and eight distribution centers. Their solution brings together information from purchasing and inventory management systems, sales and marketing, pricing and discount management, and key historic multi-year data to cut costs and deliver profit to shareholders. Quickly answer any question across the entire value chain by integrating information from all parts of the business. Expedite policy writing to close 50% faster and more accurately Nationwide’s vision was to create an “authority source” for information and reporting to provide clarity, accuracy, and flexibility. The opportunity was to gain a single view of policy information, pricing, claims, billing, marketing and customer information. Results: products re-priced and re-rated within seconds, immediate information to agents, enabling faster writing and increased accuracy.
  • Enterprise Agility Examples Ignite Ingenuity Apply unmatched capabilities and expertise to foster game-changing innovation. Utilize a proven set of uniquely powerful and superior technologies. Transformed the loyalty program model into a profit and revenue center The AAdvantage program from American Airlines invented and defined rewards programs. Still the most successful on record, AAdvantage became a profit center, retention program, and a brand image enhancement. Teradata enabled innovations like balancing reward travel with paid seating, or diversifying mileage awards with outside partnerships. AAdvantage continues to extend to revenue opportunities such as Flex Awards: cash+miles purchasing. Develop sustainable advantages by partnering with an organization focused solely on enterprise analytics. Saved $1.62 billion in lost deposits, grew market share from 13% to 20% National Australia Bank, a top 50 global banking entity, developed a multi-channel relationship marketing strategy linking customer profile and behavior to event triggers. NAB reached response rates as high as 75%, 98% retention rate, and 88% referral rate within targeted segments and reduced media advertising by 75% because of the improved understanding of their customers. Leverage the knowledge and practices of an elite network of customers and partners. The Teradata Community is riveted on driving business innovation and learning Joining the Teradata community offers companies a unique opportunity to learn from the worlds most successful and innovative companies. From Amazon to Dell to Wal Mart, Teradata users are changing the shape of their industries and delivering breakthrough performance. From social networks to peer communities to the annual PARTNERS Conference the Teradata community is designing the future of business.
  •