Data quality and bi


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  • Dims have a simple PRIMARY KEY Facts have FOREIGN KEYS (which make up a compound primary key often used as a natural key in ETL coding DIMS are 2 nd normal form FACTS are 3 rd normal form
  • Fact.Sales is the fact table and there are three dimension tables Dim.Date, Dim.Store and Dim.Product. Each dimension table has a primary key on its PK column, relating to one of the columns (viewed as rows in the example schema) of the Fact.Sales table's three-column (compound) primary key (Date_FK, Store_FK, Product_FK). The non-primary key [Units Sold] column of the fact table in this example represents a measure or metric that can be used in calculations and analysis. The non-primary key columns of the dimension tables represent additional attributes of the dimensions (such as the Year of the Dim.Date dimension). Using schema descriptors with dot-notation, combined with simple suffix decorations for column differentiation, makes it easier to write the SQL for Star Schema queries. This is because fewer underscores are required and table aliasing is minimized. Most SQL database engines allow schemata descriptors, and also permit decoration suffixes on surrogate keys columns. Using square brackets, which are physically easier to type on the keyboard (no shift key needed) are not intrusive and make the code easier to read. For example, the following query extracts how many TV sets have been sold, for each brand and country, in 1997: SELECT Brand, Country, SUM ([Units Sold]) FROM Fact.Sales JOIN Dim.Date ON Date_FK = Date_PK JOIN Dim.Store ON Store_FK = Store_PK JOIN Dim.Product ON Product_FK = Product_PK WHERE [Year] = 1997 AND [Product Category] = 'tv' GROUP BY Brand, Country
  • DIMS connect out to be more 3 rd normal form
  • The skyrocketing power of hardware and software, along with the availability of affordable and easy-to-use reporting and analysis tools have played the most important role in evolution of data warehouses.
  • Another factor that is fast becoming an important variable in data warehousing equations is the emergence of vendors with popular business application suites. Led by wildly popular German software vendor SAP AG, flexible business software suites adapted to the particulars of a business have become a very popular way to move to a sophisticated multi-tier architecture. Other vendors such as Baan, PeopleSoft, and Oracle have likewise come out with suites of software that provide different strengths but have comparable functionality. The emergence of these application suites has a direct bearing on the increased use of data warehousing in that they are increasingly able to provide standard applications that are replacing existing custom developed legacy applications. In the near future, almost every data warehouse is likely to derive data from one of these application sources rather than the customized extraction from legacy systems. Further, there are significant initiatives at these vendors to make transaction data easily available to data warehousing systems. To the extent that these standard applications have extensive customization features, data acquisition from these applications can be much simpler than from the mainframe systems
  • Provides consistent use of data element (entity attributes) values – ie M, F vs 1,2 for gender
  • Yes, we can come up with more – but we’ll pay attention to these
  • “A challenge that organizations face as they attempt to define data quality key performance indicators is that completeness, validity and integrity may be relatively easy to measure, but measuring consistency, accuracy and timeliness is a whole other story. “ Information Mgmt
  • Hardware Software licenses ETL Testing Promotion to production
  • For the purpose of this analysis, Ability to Execute is a function of a vendor's score of five measures that Gartner believes customers care about most in vendor selection. It does not equate to revenue, revenue growth or market share. Completeness of Vision is based on the scoring of six key measures, including, but not exclusive to, "Offering (Product) Strategy." It is important to understand these criteria while judging vendors' positions on the Magic Quadrant. These evaluation criteria are detailed in the Evaluation Criteria section of this document.
  • With an analytical approach, the Patriots managed to win the Super Bowl three times in four years. The team uses data and analytical models extensively, both on and off the field. In-depth analytics help the team select players and stay below the NFL salary cap. Patriots coaches and players are renowned for their extensive study of game film and statistics, and Coach Bill Belichick reads articles by academic economists on statistical probabilities of football outcomes. Off the field, the team uses detailed analytics to assess and improve the "total fan experience." At every home game, for example, 20 to 25 people have specific assignments to make quantitative measurements of the stadium food, parking, personnel, bathroom cleanliness and other factors. In retail, Wal-Mart uses vast amounts of data and category analysis to dominate the industry. Harrah’s has changed the basis of competition in gaming from building megacasinos to analytics around customer loyalty and service. Amazon and Yahoo aren't just e-commerce sites; they are extremely analytical and follow a "test and learn" approach to business changes. Capital One runs more than 30,000 experiments a year to identify desirable customers and price credit card offers.
  • Mainly 2 tools: Multidimensional OLAP and Relationship OLAP HOLAP is a hybrid of the two
  • BI Engineer job posting: Responsibilities: Act as a point person for statistical analyses, data deep dives, and general reporting. Deep dive into massive data sets to answer key business questions using MS Excel, Oracle, SQL, SAS, Perl, and other data manipulation languages.  Interact with key stakeholders to understand business issues and recommend approaches to insure business questions are properly answered.  Manage large scale requests and projects to define requirements, manage timelines, and coordinate activities with other involved team members.  Use experimental design and statistics to assist in the design and measurement of marketing tests.  Report on key business metrics.  Participate in the design and development of analytics and reporting data mart.  Using data mining techniques, statistics, and SAS, build predictive models and segmentation schemes for the purposes of cross sell, retention, acquisition, and lifetime value. Qualifications : Master’s degree or foreign equivalent in Mathematics, Statistics, Analytics, Operations Research, or a related field plus one year of progressively responsible experience in the job offered or as a Business Analyst, Data Engineer, or another related occupation. Employer will accept a Bachelor’s degree in Mathematics, Statistics, Analytics, Operations Research, or a related field plus five years of experience in the specialty as equivalent to a Master’s degree and one year of progressively responsible experience. Experience in the job offered or related occupation must involve performing data modeling, database development, and statistical testing and analysis of large-scale datasets using Oracle SQL, Perl, MS Excel, and SAS.  
  • Data quality and bi

    1. 1. DW Part 2 The Twins: Data Quality & Business Intelligence Denise Jeffries [email_address] [email_address] 205.747.3301
    2. 2. Star Schema (facts and dimensions) <ul><li>The facts that the data warehouse helps analyze are classified along different dimensions: </li></ul><ul><ul><li>The FACT table houses the main data </li></ul></ul><ul><ul><ul><li>Includes a large amount of aggregated data (i.e. price, units sold) </li></ul></ul></ul><ul><ul><li>DIMENSION tables off the FACT include attributes that describe the FACT </li></ul></ul><ul><li>Star schemas provide simplicity for users </li></ul>
    3. 3. Star Schema example (Sales db)
    4. 4. SnowFlake Schema <ul><li>Central FACT </li></ul><ul><li>Connected to multiple DIMENSIONS which are NORMALIZED into related tables </li></ul><ul><li>Snowflaking effects DIMS and never FACT </li></ul><ul><li>Used in Data warehouses and data marts when speed is more important than efficiency/ease of data selection </li></ul><ul><li>Needed for many BI OLAP tools </li></ul><ul><li>Stores less data </li></ul>
    5. 5. Snowflake Schema example (Sales db)
    6. 6. Comparison of SQL Star vs SnowFlake <ul><li>SELECT Brand, Country, SUM (Units Sold) </li></ul><ul><li>FROM Fact.Sales </li></ul><ul><li>JOIN Dim.Date </li></ul><ul><li>ON Date_FK = Date_PK </li></ul><ul><li>JOIN Dim.Store </li></ul><ul><li>ON Store_FK = Store_PK </li></ul><ul><li>JOIN Dim.Product </li></ul><ul><li>ON Product_FK = Product_PK </li></ul><ul><li>WHERE [Year] = 2010 </li></ul><ul><li>AND Product Category = ‘TV' GROUP BY Brand, Country </li></ul><ul><li>SELECT </li></ul><ul><ul><li>B.Brand, </li></ul></ul><ul><ul><li>G.Country, </li></ul></ul><ul><ul><li>SUM (F.Units_Sold) </li></ul></ul><ul><li>FROM Fact_Sales F (NOLOCK) </li></ul><ul><li>INNER JOIN Dim_Date D (NOLOCK) ON F.Date_Id = D.Id </li></ul><ul><li>INNER JOIN Dim_Store S (NOLOCK) ON F.Store_Id = S.Id </li></ul><ul><li>INNER JOIN Dim_Geography G (NOLOCK) ON S.Geography_Id = G.Id </li></ul><ul><li>INNER JOIN Dim_Product P (NOLOCK) ON F.Product_Id = P.Id </li></ul><ul><li>INNER JOIN Dim_Product_Category C (NOLOCK) ON P.Product_Category_Id = C.ID </li></ul><ul><li>INNER JOIN Dim_Brand B (NOLOCK) ON P.Brand_Id = B.Id WHERE D.Year = 2010 </li></ul><ul><li>AND C.Product_Category = 'tv' </li></ul><ul><li>GROUP BY </li></ul><ul><li>B.Brand, </li></ul><ul><li>G.Country </li></ul>
    7. 7. Account, Customer & Address Relationships Account Contact Party Address link Account Party link Address Account Party Account Information loaded from ALL Source Systems ETL process builds the relationship between Accounts and Customers (Party) based on the relationship file from CUSTOMER CRM SYSTEM
    8. 8. EDW Process State Staging Area EDW Metadata | Data Governance | Data Management DM CPS MANTAS CRDB MKTG FIN SALES EDW Data cleansing Data profiling Sync & Sort BI Source System Cleanse / Pre-process IMP RM OEC ALS AFS ST RE DFP SBA AFS V-PR
    9. 9. Explosion in innovation <ul><li>BI software now able to be deployed on intranet vs hard to maintain thick client apps </li></ul><ul><ul><li>Thick client still used for developers </li></ul></ul><ul><li>Web server, application server, database server </li></ul><ul><ul><li>Allows offloading of processing to correct tier </li></ul></ul><ul><ul><ul><li>More power for everyone </li></ul></ul></ul>
    10. 10. Change in Business <ul><li>Global economy changed needs of organizations worldwide </li></ul><ul><li>Global markets </li></ul><ul><li>Mergers and Acquisitions </li></ul><ul><li>All increase data needs </li></ul><ul><li>More tech savvy end users (demand more data, more tools… </li></ul><ul><li>More information demanding executives facilitates sponsorship of DW </li></ul>
    11. 11. Single definition of a data element needed for BI <ul><li>DW brings in the data from multiple sources and conforms it so that it can be viewed together </li></ul><ul><ul><li>Multiple systems have individual customers/addresses, but warehouse gives single view of the customer and all the systems they are in </li></ul></ul><ul><ul><ul><li>Helping move from product centric systems to customer centric systems </li></ul></ul></ul>
    12. 12. Business view of data <ul><li>DW is only successful is it provides the view the business needs of its data </li></ul><ul><li>A data warehouse is a structured extensible environment designed for the analysis of non-volatile data, logically and physically transformed from multiple source applications to align with business structure, updated and maintained for a long time period, expressed in simple business terms, and summarized for quick analysis. </li></ul><ul><ul><li>Vivek R. Gupta, Senior Consultant [email_address] System Services corporation, Chicago, Illinois </li></ul></ul>
    13. 13. Example of conforming data for business view:
    14. 14. Business use of DW <ul><li>Business should use data mart created off data warehouse </li></ul><ul><li>Business uses want to use existing tools/methods (replicate queires, Excel, extract to Access) against DW and validate the data between existing and DW </li></ul><ul><li>Over time LoB gains confidence in DW and then begins to explore new possibilities of data use and tool use </li></ul>
    15. 15. EDW Development Project Cycle (New Source to EDW)
    16. 16. DW - Roadmap Management Architecture (Metadata, Data Security, Systems Management)
    17. 17. SECTION 3 <ul><li>What is Data Quality </li></ul><ul><ul><li>I can’t tell you what’s important, but your users can. </li></ul></ul><ul><ul><ul><li>Look for the fields that can identify potential problems with the data </li></ul></ul></ul><ul><li>What is Master Data Management (MDM) </li></ul>
    18. 18. Data Quality <ul><li>Data doesn’t stay the same </li></ul><ul><ul><li>Sometimes it does </li></ul></ul><ul><li>Considerations: </li></ul><ul><ul><li>What happens to the warehouse when the data changes </li></ul></ul><ul><ul><li>When needs change </li></ul></ul>
    19. 19. Roadmap to DQ <ul><li>Data profiling </li></ul><ul><li>Establishing metrics/measures </li></ul><ul><li>Design and implement the rules </li></ul><ul><li>Deploy the plan </li></ul><ul><li>Review errors/exceptions </li></ul><ul><li>Monitor the results </li></ul>
    20. 20. Data Profiling <ul><li>What’s in the data </li></ul><ul><ul><li>Analyze the columns in the tables </li></ul></ul><ul><ul><ul><li>Provides metadata </li></ul></ul></ul><ul><ul><ul><li>Allows for good specifications for programmers </li></ul></ul></ul><ul><ul><ul><li>Reduces project risk (as data is now known) </li></ul></ul></ul><ul><ul><ul><ul><li>How many rows, number of distinct values in a column, how many null, data type identification </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Shows the data pattern </li></ul></ul></ul></ul>
    21. 21. Data Profiling Example
    22. 22. Data Quality is measured as the degree of superiority, or excellence, of the various data that we use to create information products. <ul><li>“ Reason #1 for the failure of CRM projects : Data is ignored. Enterprise must have a detailed understanding of the quality of their data. How to clean it up, how to keep it clean, where to source it, and what 3 rd -party data is required. Action item: Have a data quality strategy. Devote ½ of the total timeline of the CRM project to data elements.” - Gartner </li></ul>
    23. 23. Data Quality Tools (Gartner Magic Quadrant)
    24. 24. Dimensions of Quality
    25. 25. Data Quality Measures <ul><li>Definition </li></ul><ul><li>Accuracy </li></ul><ul><li>Completeness </li></ul><ul><li>Coverage </li></ul><ul><li>Timeliness </li></ul><ul><li>Validity </li></ul>
    26. 26. Definition <ul><li>Conformance: The degree to which data values are consistent with their agreed upon definitions. </li></ul><ul><ul><li>A detailed definition must first exist before this can be measured. </li></ul></ul><ul><ul><li>Information quality begins with a comprehensive understanding of the data inventory. The information about the data is as important as the data itself. </li></ul></ul><ul><ul><li>A Data Dictionary must exist! An organized, authoritive collection of attributes is equivalent to the old “Card Catalog” in a library, or the “Parts and List Description” section of an inventory system. It must contain all the know usage rules and an acceptable list of values. All known caveats and anomalies must be descried. </li></ul></ul>
    27. 27. Accuracy <ul><li>The degree to which a piece of data is correct and believable. The value can be compared to the original source for correctness, but it can still be unbelievable. Conformed values can be compared to lists of reference values. </li></ul><ul><ul><li>Zip code 35244 is correct and believable. </li></ul></ul><ul><ul><li>Zip code 3524B is incorrect and unbelievable. </li></ul></ul><ul><ul><li>Zip code 35290 is incorrect but believable (it looks right, but does not exist). </li></ul></ul><ul><ul><li>AL is a correct and believable state code (compared to the list of valid state codes) </li></ul></ul><ul><ul><li>A1 is an incorrect and unbelievable state code (compared to the list of valid state codes) </li></ul></ul><ul><ul><li>AA is an incorrect but believable state code (compared to the list of valid state codes) </li></ul></ul>
    28. 28. Completeness <ul><li>The Degree to which all information expected is received. This is measured in two ways: </li></ul><ul><ul><li>Do we have all the records that were sent to us? </li></ul></ul><ul><ul><ul><li>Counts from the provider can be compared against counts of data received. </li></ul></ul></ul><ul><ul><li>Did the provider send us all the records that they have or just some of them? </li></ul></ul><ul><ul><li>This is difficult to measure without auditing and trending the source. </li></ul></ul><ul><ul><ul><li>How would we know that the provider had a ‘glitch’ in their system and records were missing from our feed? </li></ul></ul></ul>
    29. 29. Measures of Completeness <ul><li>The following questions can be answered for counts: </li></ul><ul><ul><li>How many records per batch by provider? </li></ul></ul><ul><ul><li>How is this batch’s counts compared to the previous month’s average. </li></ul></ul><ul><ul><li>How is the batch’s counts compared to the same time period last year? </li></ul></ul><ul><ul><li>How does this batch’s counts compare to a 12 month average? </li></ul></ul>
    30. 30. Coverage <ul><li>The degree to which all fields are populated with data. Columns of data can be measured for % of missing values and compared to expected % missing. </li></ul><ul><ul><li>i.e. Sale Type Code is expected to be populated 100% by all sources for Sales documents. </li></ul></ul>
    31. 31. Timeliness <ul><li>The degree to which provider files are received, processed and made available to for assembly to data marts. Expected receipt times are compared to actual receipt times. </li></ul><ul><ul><li>Late or missing files are flagged and reported on. </li></ul></ul><ul><ul><li>Proactive alerts trigger communication with the provider contact. </li></ul></ul><ul><ul><li>Proactive communication can alert to assembly processes. </li></ul></ul><ul><ul><li>Excessive lag times can be reported to providers in order to request delivery sooner. </li></ul></ul>
    32. 32. Validity <ul><li>The degree to which the relationships between different data are valid. </li></ul><ul><ul><li>Zip code 48108 is accurate. State code AL is accurate. Zip code 48108 is invalid for the state of AL. </li></ul></ul>
    33. 33. Data Quality Measures <ul><li>How do you know if your data is of high quality? </li></ul><ul><ul><li>Agree upon the measure that are important to the organization and consistently report them out. </li></ul></ul><ul><ul><li>Use the data measures to communicate and inform. </li></ul></ul>
    34. 34. Measurement
    35. 35. Exercise: Changing the Data (1 of 2) <ul><li>So, you need to add a new source </li></ul><ul><li>Or, you need to receive additional data from an existing source </li></ul><ul><li>Could be the data quality is an issue </li></ul><ul><li>Could be that the business rules weren’t defined adequately </li></ul>
    36. 36. Brainstorming Group Exercise (2 of 2) <ul><li>The data changed due to DQ measures – what do we have to do in the DW? </li></ul><ul><ul><li>What has to change </li></ul></ul><ul><ul><li>Estimate the change </li></ul></ul><ul><ul><li>Implement the change </li></ul></ul><ul><ul><li>How do we make sure it doesn’t happen again? </li></ul></ul><ul><ul><ul><li>What DQ measure can help? </li></ul></ul></ul>
    37. 37. MDM Master Data Management <ul><li>The newest ‘buzz word’ </li></ul><ul><li>The recent emphasis on regulatory compliance, SOA, and mergers and acquisitions has made the creating and maintaining of accurate and complete master data a business imperative. </li></ul>
    38. 38. MDM <ul><li>The pain that organizations are experiencing around consistent reporting, regulatory compliance, strong interest in Service-Oriented Architecture (SOA), and Software as a Service (SaaS) has prompted a great deal of interest in Master Data Management (MDM). </li></ul>
    39. 39. What Is Master Data Management? <ul><li>Master data is the technology, tools, and processes an organization needs to create and maintain consistent and accurate inventory of its data. </li></ul>
    40. 40. 5 Types of Data for MDM <ul><li>Unstructured —This is data found in e-mail, white papers like this, magazine articles, corporate intranet portals, product specifications, marketing collateral, and PDF files. </li></ul><ul><li>Transactional —This is data related to sales, deliveries, invoices, trouble tickets, claims, and other monetary and non-monetary interactions. </li></ul><ul><li>Metadata —This is data about other data and may reside in a formal repository or in various other forms such as XML documents, report definitions, column descriptions in a database, log files, connections, and configuration files. </li></ul><ul><li>Hierarchical —Hierarchical data stores the relationships between other data. It may be stored as part of an accounting system or separately as descriptions of real-world relationships, such as company organizational structures or product lines. Hierarchical data is sometimes considered a super MDM domain, because it is critical to understanding and sometimes discovering the relationships between master data. </li></ul>
    41. 41. 5 types of data cont’d <ul><li>Master —the critical nouns of a business and fall generally into four groupings: </li></ul><ul><ul><li>people </li></ul></ul><ul><ul><li>things </li></ul></ul><ul><ul><li>places </li></ul></ul><ul><ul><li>concepts </li></ul></ul><ul><li>Further categorizations within those groupings are called subject areas, domain areas, or entity types. </li></ul><ul><ul><li>For example, </li></ul></ul><ul><ul><ul><li>within people, there are customer, employee, and salesperson. </li></ul></ul></ul><ul><ul><ul><li>Within things, there are product, part, store, and asset. </li></ul></ul></ul><ul><ul><ul><li>Within concepts, there are things like contract, warrantee, and licenses. </li></ul></ul></ul><ul><ul><ul><li>Within places, there are office locations and geographic divisions. </li></ul></ul></ul><ul><ul><li>Some of these domain areas may be further divided. Customer may be further segmented, based on incentives and history. A company may have normal customers, as well as premiere and executive customers. Product may be further segmented by sector and industry. (4) </li></ul></ul>
    42. 42. Exercise: <ul><li>What processes need to be put in place for MDM </li></ul><ul><ul><li>Who needs to be involved </li></ul></ul><ul><ul><li>Who owns it </li></ul></ul>
    43. 43. SECTION 4 <ul><li>BI Tools </li></ul><ul><li>BICC </li></ul><ul><li>Jobs </li></ul><ul><li>Certifications </li></ul>
    44. 44. SECTION 4 <ul><li>What is business intelligence </li></ul><ul><ul><li>What are BI tools </li></ul></ul><ul><ul><li>What is a business intelligence competency center (BICC) </li></ul></ul><ul><li>What jobs are available </li></ul><ul><ul><li>certifications </li></ul></ul>
    45. 45. What is business intelligence <ul><li>Turning raw data into information. </li></ul><ul><li>Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems , query and reporting, online analytical processing ( OLAP ), statistical analysis, forecasting, and data mining . (1) </li></ul>
    46. 46. What is BI <ul><li>It is about making better business decisions easier and quicker. </li></ul><ul><li>Data Mining is a BI technique which is done to extract valid, useful and previously unknown information from a companies data sources. </li></ul>
    47. 47. BI solutions examples by industry <ul><li>Retail </li></ul><ul><ul><li>Forecasting </li></ul></ul><ul><ul><li>Ordering & supply </li></ul></ul><ul><ul><li>Marketing </li></ul></ul><ul><ul><li>Merchandising </li></ul></ul><ul><ul><li>Distribution </li></ul></ul><ul><ul><li>Transportation </li></ul></ul><ul><ul><li>Inventory planning </li></ul></ul><ul><ul><li>Space management ….. </li></ul></ul><ul><li>Insurance </li></ul><ul><ul><li>Claims & premium analysis </li></ul></ul><ul><ul><li>Customer Analytics </li></ul></ul><ul><ul><li>Risk analysis </li></ul></ul><ul><li>Banking </li></ul><ul><ul><li>Customer profitability </li></ul></ul><ul><ul><li>Credit Management </li></ul></ul><ul><ul><li>Branch sales </li></ul></ul>
    48. 48. BI term coined Sept 1996 by Gartner Group in a report <ul><li>“ By 2000, Information Democracy will emerge in forward-thinking enterprises, with Business Intelligence information and applications available broadly to employees, consultants, customers, suppliers, and the public. The key to thriving in a competitive marketplace is staying ahead of the competition. Making sound business decisions based on accurate and current information takes more than intuition. Data analysis, reporting, and query tools can help business users wade through a sea of data to synthesize valuable information from it - today these tools collectively fall into a category called Business Intelligence.” (1) </li></ul>
    49. 49. Magic Quadrant for BI (Gartner)
    50. 50. BI <ul><li>BI is a term categorizing a variety of software applications that are used to analyze a business’ raw data. </li></ul><ul><li>It is also a discipline categorizing activities that include data quality, data mining, OLAP (online analytical processing), querying and reporting. (2) </li></ul>
    51. 51. What kinds of companies use BI <ul><li>All kinds, restaurants, sports franchises, retailers….any company. </li></ul><ul><li>Examples include: New England Patriots, Walmart, Harrah’s, Amazon, Yahoo, Capital One….. </li></ul>
    52. 52. When are you doing BI? <ul><li>When looking at your market share or profitability you are doing BI. </li></ul><ul><li>Looking at the best area to increase your sales you are doing BI. </li></ul><ul><li>Anytime you analyze data and turn it into information you are doing BI. </li></ul>
    53. 53. How do you know if you are really doing BI? <ul><li>Efforts around changing individual and team work practices arise, from the individual and from the teams </li></ul><ul><li>New jobs are posted talking about analyzing data and delivering reports </li></ul><ul><li>Dashboards appear </li></ul><ul><li>The CEO and CIO start talking about it </li></ul>
    54. 54. BI Tools & What they do <ul><li>Cognos </li></ul><ul><li>SAS </li></ul><ul><li>Oracle (Siebel & Hyperion) </li></ul><ul><li>MicroStrategy </li></ul><ul><li>Microsoft </li></ul><ul><li>Information Builders </li></ul><ul><li>QlikView ….. etc </li></ul><ul><li>Querying & Reporting </li></ul><ul><li>OLAP </li></ul><ul><ul><li>And its sisters: </li></ul></ul><ul><ul><ul><li>MOLAP </li></ul></ul></ul><ul><ul><ul><li>ROLAP </li></ul></ul></ul><ul><ul><ul><li>HOLAP </li></ul></ul></ul><ul><li>Data Mining </li></ul>
    55. 55. BICC <ul><li>A Business Intelligence Competency Center (BICC) is a cross-functional organizational team that has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of Business Intelligence (BI) across an organization. </li></ul><ul><li>As early as 2001, Gartner , an information technology research and advisory company, started advocating that companies need a BICC to develop and focus resources to be successful using business intelligence. [1] Since then, the BICC concept has been further refined through practical implementations in organizations that have implemented BI and analytical software. </li></ul><ul><li>Taken directly from Wikipedia </li></ul>
    56. 56. BICC <ul><li>In practice, the term &quot;BICC&quot; is not well integrated into the nomenclature of business or public sector organizations and there are a large degree of variances in the organizational design for BICCs. Nevertheless, the popularity of the BICC concept has caused the creation of units that focus on ensuring the use of the information for decision-making from BI software and increasing the return on investment (ROI) of BI. [2] </li></ul><ul><li>A BICC coordinates the activities and resources to ensure that a fact-based approach to decision making is systematically implemented throughout an organization. It has responsibility for the governance structure for BI and analytical programs, projects, practices, software, and architecture. It is responsible for building the plans, priorities, infrastructure, and competencies that the organization needs to take forward-looking strategic decisions by using the BI and analytical software capabilities. </li></ul><ul><li>A BICC’s influence transcends that of a typical business unit, playing a crucial central role in the organizational change and strategic process. Accordingly, the BICC’s purpose is to empower the entire organization to coordinate BI from all units. Through centralization, it &quot;…ensures that information and best practices are communicated and shared through the entire organization so that everyone can benefit from successes and lessons learned.&quot; [3] </li></ul><ul><li>The BICC also plays an important organizational role facilitating interaction among the various cultures and units within the organization. Knowledge transfer, enhancement of analytic skills, coaching and training are central to the mandate of the BICC. A BICC should be pivotal in ensuring a high degree of information consumption and a ROI for BI. </li></ul><ul><li>Taken directly from Wikipedia </li></ul>
    57. 57. Jobs in Business Intelligence <ul><li>Business Analyst </li></ul><ul><li>BI Programmer </li></ul><ul><li>BI Architect </li></ul><ul><li>BI Support Engineer </li></ul><ul><li>BI Manager </li></ul><ul><li>1000+ jobs on </li></ul><ul><li>5,357 jobs on </li></ul>
    58. 58. References <ul><li>Data Management and Integration Topic, Gartner, </li></ul><ul><ul><li>Articles: Key Issues for Implementing an Enterprise wide Data Quality Improvement Project, 2008, Key Issues for Enterprise Information Management Initiatives, 2008, Key Issues for Establishing Information Governance Policies, Processes and Organization, 2008 </li></ul></ul><ul><li>Data Quality Management, The Most Critical Initiative You Can Implement, J. G. Geiger, </li></ul><ul><li>Information Management, How to Measure and Monitor the Quality of Master Data, </li></ul><ul><li>Data Management Assn of Michigan Bits & Bytes, Critical Data Quality Controls, D Jeffries, Fall 2006 </li></ul><ul><li>(1) </li></ul><ul><li>(2) </li></ul><ul><li>(3) </li></ul><ul><li>(4) </li></ul><ul><li>Wikipedia: BICC </li></ul><ul><ul><li>Strange, K. H., Hostmann, B. (22 July 2003), BI Competency Center Is Core to BI Success, Gartner Research </li></ul></ul><ul><ul><li>Miller, G., Queisser, T (2008), The Modern BI Organization, Heidelberg, MaxMetrics GmbH </li></ul></ul><ul><ul><li>Miller, G., Bräutigam, B, & Gerlach, S. (2006). BICC: A Team Approach Competitive Advantage. Hoboken: Wiley </li></ul></ul>