This document discusses the growing importance of business intelligence and data analytics. It introduces the roles of the data detective, who bridges business and IT to uncover missed opportunities and improve processes. Two use cases are described: 1) A detective helped a retailer increase layaway sales through targeted promotions. 2) A detective analyzed pricing data to identify the "sweet spot" and increase sales for a manufacturer. Throughout, the document emphasizes that data needs context to be useful and stresses the detective's focus on both technical skills and business understanding.
Big Challenges in Data Modeling: Modeling MetadataDATAVERSITY
We invite you to join us in this monthly DATAVERSITY webinar series, “Big Challenges with Data Modeling” hosted by Karen Lopez. Join Karen and guest expert panelists each month to discuss their experiences in breaking through these specific data modeling challenges. Hear from experts in the field on how and where they came across these challenges and what resolution they found. Join them in the end for the Q&A portion to ask your own questions on the challenge topic of the month.
The Definitive Guide to Data Modeling for Business IntelligenceEran Levy
Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence.
To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
How Can You Calculate the Cost of Your Data?DATAVERSITY
Today, self-service, Cloud and big data technologies make new data preparation capabilities necessary…and possible. But, we've all been through the hype cycle and know the trough of disillusionment can come on hard and fast.
Organizations have been trying to solve the data quality problem and democratize insights for years spending millions of dollars and dedicating an increasing amount of resources to manage and govern the data. The result? Everyone is still looking to solve the problem.
Data preparation offers a new paradigm, but how can you avoid another round of minimal business impact? We’ll review a true data ROI model that helps organizations understand the value of existing versus modern data management architectures.
The Heart of Data Modeling: 7 Ways Your Agile Project is Managing Data WrongDATAVERSITY
Is your organization using agile approaches to systems development project? Have you found that there are conflicting opinions with what should be done, when it should be done and who should do it? Is there even a suggestion that data modeling isn’t needed on an Agile project? Are your data architects stuck in a waterfall world? Are you asking for “no more changes” to the data model? Do your developers thing that “just the right documentation” means no modeling allowed? Does anyone even know where the reference data for the application is located? Or how it is updated?
In this month’s webinar, Karen will show you how data modeling and Agile approaches CAN work together to deliver quality information systems and solutions, with fewer dysfunctions and less tears.
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
Big Challenges in Data Modeling: Modeling MetadataDATAVERSITY
We invite you to join us in this monthly DATAVERSITY webinar series, “Big Challenges with Data Modeling” hosted by Karen Lopez. Join Karen and guest expert panelists each month to discuss their experiences in breaking through these specific data modeling challenges. Hear from experts in the field on how and where they came across these challenges and what resolution they found. Join them in the end for the Q&A portion to ask your own questions on the challenge topic of the month.
The Definitive Guide to Data Modeling for Business IntelligenceEran Levy
Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence.
To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
How Can You Calculate the Cost of Your Data?DATAVERSITY
Today, self-service, Cloud and big data technologies make new data preparation capabilities necessary…and possible. But, we've all been through the hype cycle and know the trough of disillusionment can come on hard and fast.
Organizations have been trying to solve the data quality problem and democratize insights for years spending millions of dollars and dedicating an increasing amount of resources to manage and govern the data. The result? Everyone is still looking to solve the problem.
Data preparation offers a new paradigm, but how can you avoid another round of minimal business impact? We’ll review a true data ROI model that helps organizations understand the value of existing versus modern data management architectures.
The Heart of Data Modeling: 7 Ways Your Agile Project is Managing Data WrongDATAVERSITY
Is your organization using agile approaches to systems development project? Have you found that there are conflicting opinions with what should be done, when it should be done and who should do it? Is there even a suggestion that data modeling isn’t needed on an Agile project? Are your data architects stuck in a waterfall world? Are you asking for “no more changes” to the data model? Do your developers thing that “just the right documentation” means no modeling allowed? Does anyone even know where the reference data for the application is located? Or how it is updated?
In this month’s webinar, Karen will show you how data modeling and Agile approaches CAN work together to deliver quality information systems and solutions, with fewer dysfunctions and less tears.
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
Mastering Data Modeling for NoSQL PlatformsDATAVERSITY
Data is proliferating at an accelerated rate, with all the mobile and desktop apps, social media, online purchasing, and consumer loyalty programs available today. All of these data sources have not just changed the way we operate on a day-to-day basis, but it has immensely increased the volume, velocity, and variety of data being created. Faced with this growing trend, data professionals now often have to look beyond the relational database to NoSQL database technologies to fully address their data management needs for data lakes, data warehouses, and other data stores. IDERA’s Ron Huizenga will discuss the NoSQL data modeling support included in ER/Studio, including round-trip engineering for Hadoop Hive and MongoDB.
Data Leadership - Stop Talking About Data and Start Making an Impact!DATAVERSITY
<!-- wp:paragraph -->
<p>For any organization to be successful, whatever we do with data must connect to meaningful business improvements—and those must be measured. If current data efforts lack results or accountability, then Data Leadership is our answer.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>But Data Leadership isn’t really about the data at all. What makes Data Leadership so powerful is its ability to completely transform organizations. Going beyond traditional data management and governance, Data Leadership builds momentum and delivers the change we’ve long known our businesses need. Data Leadership helps us overcome the lingering data challenges our legacy approaches never will.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This webinar will cover the key concepts of Data Leadership, and what anybody can do to start making a bigger impact for their teams and businesses. Whether your role today is large or small, Data Leadership will be essential to your future data success! </p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>Key Learnings Include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>What Data Value really is, and why creating it is the goal of everything we do with data</li><li>Introduction to the Data Leadership Framework</li><li>Why Data Leadership is fundamentally about balance</li><li>How to immediately start making a Data Leadership impact in your organization</li></ul>
<!-- /wp:list -->
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
Faster and more accurate reporting, analysis or planning; better business decisions; improved employee satisfaction and improved data quality top the list. Benefits achieved least frequently include reducing costs, and increasing revenues.
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Data visualization is central to big data analytics because the volumes of data involved are beyond the scale that can be effectively manually reviewed by humans. Data visualization provides techniques for quickly viewing, understanding, and identifying patterns in big data that can’t otherwise be reasonably absorbed by humans through detail inspection.
Join our discussion and get insights on:
Best practices in effective big data visualization
Transformations necessary to enable effective visualization
Visualizing patterns
Data discovery versus descriptive analytics visualization
Implementing Big Data, NoSQL, & Hadoop - Bigger Is (Usually) BetterDATAVERSITY
From its widespread formal business practice to the scope of casual popular awareness, “Big Data” has a tendency to live up to its name. Featured in countless headlines, journal articles, and industry reviews, Big Data metrics and methods such as NoSQL and Hadoop have taken up plenty of the spotlight as of late. However, most of what has been written about these topics is focused on the hardware, services, and scale-out involved with them, a misguided focus that ignores the critical questions driving any shift in corporate strategy: what can Big Data do for you? Which approach to it best fits your organization? And perhaps most importantly, what is required on your end in order to spur a successful implementation process?
In the interest of answering these and other questions, this webinar will:
Provide guidance on how to think about and establish realistic Big Data management plans and expectations for generating business value, as well as on the means by which big data can complement existing data management practices
Introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL
Elaborate upon the prototyping nature of practicing Big Data techniques
Show how to demonstrate a sample use ca
Current data management trends.
Companies are moving from business intelligence standardization to experimentation, data-quality concerns are easing a bit, and cloud-based, real-time, and big-data platforms are on the rise. Amongst them are both structured and unstructured database trends which are professionally validated. Here then are the trends to watch out for:
Internal Systems Data
Analytical Databases
Data Integration Management
Business Intelligence Development
Data Governance and Visualization
Data Storytelling and Collaboration
Mobile Business Data Intelligence
Analytics and BI standardization
Cloud-based Data Warehousing
Real-time Technology
Data Quality Concerns
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Pitfalls and pro-tips for effective and transparent Business Intelligence too...Data Con LA
Data Con LA 2020
Description
*Identify key players plus team functions
*Unpack user requirements to answer business critical service or support needs
*Question everything to know what you don't know
*Build Business Intelligence Tools and Services governance for change management roadmap
*What this means for you
Speaker
Jason Medina, Global Decision, Data Scientist
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
Mastering Data Modeling for NoSQL PlatformsDATAVERSITY
Data is proliferating at an accelerated rate, with all the mobile and desktop apps, social media, online purchasing, and consumer loyalty programs available today. All of these data sources have not just changed the way we operate on a day-to-day basis, but it has immensely increased the volume, velocity, and variety of data being created. Faced with this growing trend, data professionals now often have to look beyond the relational database to NoSQL database technologies to fully address their data management needs for data lakes, data warehouses, and other data stores. IDERA’s Ron Huizenga will discuss the NoSQL data modeling support included in ER/Studio, including round-trip engineering for Hadoop Hive and MongoDB.
Data Leadership - Stop Talking About Data and Start Making an Impact!DATAVERSITY
<!-- wp:paragraph -->
<p>For any organization to be successful, whatever we do with data must connect to meaningful business improvements—and those must be measured. If current data efforts lack results or accountability, then Data Leadership is our answer.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>But Data Leadership isn’t really about the data at all. What makes Data Leadership so powerful is its ability to completely transform organizations. Going beyond traditional data management and governance, Data Leadership builds momentum and delivers the change we’ve long known our businesses need. Data Leadership helps us overcome the lingering data challenges our legacy approaches never will.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This webinar will cover the key concepts of Data Leadership, and what anybody can do to start making a bigger impact for their teams and businesses. Whether your role today is large or small, Data Leadership will be essential to your future data success! </p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>Key Learnings Include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>What Data Value really is, and why creating it is the goal of everything we do with data</li><li>Introduction to the Data Leadership Framework</li><li>Why Data Leadership is fundamentally about balance</li><li>How to immediately start making a Data Leadership impact in your organization</li></ul>
<!-- /wp:list -->
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
Faster and more accurate reporting, analysis or planning; better business decisions; improved employee satisfaction and improved data quality top the list. Benefits achieved least frequently include reducing costs, and increasing revenues.
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Data visualization is central to big data analytics because the volumes of data involved are beyond the scale that can be effectively manually reviewed by humans. Data visualization provides techniques for quickly viewing, understanding, and identifying patterns in big data that can’t otherwise be reasonably absorbed by humans through detail inspection.
Join our discussion and get insights on:
Best practices in effective big data visualization
Transformations necessary to enable effective visualization
Visualizing patterns
Data discovery versus descriptive analytics visualization
Implementing Big Data, NoSQL, & Hadoop - Bigger Is (Usually) BetterDATAVERSITY
From its widespread formal business practice to the scope of casual popular awareness, “Big Data” has a tendency to live up to its name. Featured in countless headlines, journal articles, and industry reviews, Big Data metrics and methods such as NoSQL and Hadoop have taken up plenty of the spotlight as of late. However, most of what has been written about these topics is focused on the hardware, services, and scale-out involved with them, a misguided focus that ignores the critical questions driving any shift in corporate strategy: what can Big Data do for you? Which approach to it best fits your organization? And perhaps most importantly, what is required on your end in order to spur a successful implementation process?
In the interest of answering these and other questions, this webinar will:
Provide guidance on how to think about and establish realistic Big Data management plans and expectations for generating business value, as well as on the means by which big data can complement existing data management practices
Introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL
Elaborate upon the prototyping nature of practicing Big Data techniques
Show how to demonstrate a sample use ca
Current data management trends.
Companies are moving from business intelligence standardization to experimentation, data-quality concerns are easing a bit, and cloud-based, real-time, and big-data platforms are on the rise. Amongst them are both structured and unstructured database trends which are professionally validated. Here then are the trends to watch out for:
Internal Systems Data
Analytical Databases
Data Integration Management
Business Intelligence Development
Data Governance and Visualization
Data Storytelling and Collaboration
Mobile Business Data Intelligence
Analytics and BI standardization
Cloud-based Data Warehousing
Real-time Technology
Data Quality Concerns
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Pitfalls and pro-tips for effective and transparent Business Intelligence too...Data Con LA
Data Con LA 2020
Description
*Identify key players plus team functions
*Unpack user requirements to answer business critical service or support needs
*Question everything to know what you don't know
*Build Business Intelligence Tools and Services governance for change management roadmap
*What this means for you
Speaker
Jason Medina, Global Decision, Data Scientist
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Transform Your Downstream Cloud Analytics with Data Quality Precisely
Untrustworthy results or inaccurate insights from ML, AI, and advanced analytics systems were due to a lack of quality in the data as reported by nearly half of respondents to Precisely’s Enterprise Data Quality survey. Are you ready to improve your trust in the data your organization is using in the cloud for business decision-making?
Register now to learn how to take the first steps to high-quality data in the cloud by better understanding your data through profiling.
During this on-demand webinar, we will explore key topics such as:
• The five key steps to effective data profiling
• How profiling informs your next steps to deliver quality data to the cloud
• How Precisely customers have elevated marketing and customer service results by focusing on data quality
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
La BuzzWord dell’ultimo anno è “Data Science”. Ma cosa significa realmente? Cosa fa un “Data Scientist”? Che strumenti sono messi a disposizione da Microsoft? E che altri strumenti ci sono oltre a Microsoft?
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Five Attributes to a Successful Big Data StrategyPerficient, Inc.
The veracity, variety and sheer volume of data is increasing exponentially. With Hadoop and NoSQL solutions becoming commonplace, there are many technical options for managing and extracting value from this data. Many companies create labs to experiment with Big Data solutions, only later become IT playgrounds or unstructured dumping grounds.
To help avoid these pitfalls,companies with successful Big Data projects approach challenges by formulating a strategy that assures real business value is derived from their Big Data investments. In a Perficient poll, 73% of companies stated they are in the early-evaluation stage to find solutions to their Big Data problems and are only beginning to create their strategy.
Join us for a webinar featuring thought-provoking best practices used by successful companies to quickly realize business value from their Big Data investments. You'll learn:
The top five steps to increased business value
What the top companies are doing in Big Data that you need to know
Next steps to lay the ground work for a successful Big Data strategy
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
David Bernstein of eQuest, the global leader in job-posting delivery and job board performance analytics, discusses how Big Data analysis provides organizations with greater recruitment marketing effectivenss than ever before. By not only delivering predictive information on job postings but by also taking a holistic look at your talent pipeline, Big Data analysis provides the insight organizations need to make better-informed decisions more quickly, reducing time-to-hire, costs and administrative burden.
4. Data in the Organization
• Data is EVERYWHERE
• Transactional systems
• Customer data
• Social media
• Sensors
• eCommerce
• Created every second
• Created every day
5. Data Not Yet in the Organization
• IoT is on the near horizon
• Massive amounts of device and sensor data
• Everything is connected and communicates
• Soon your home robot will be learning about
you based on observations and personal
device data
6. Data Creation
“Every two days we create as much
information as we did from the dawn of
civilization up until 2003”
http://techcrunch.com/2010/08/04/schmidt-data/
7. Data Growth
• Every analyst, every industry expert seem
to agree
• Data volumes are only going to get larger
11. Data Growth
• Point is: Data continues to grow at unheard
of rates
• Data sources are only increasing
Data without context is useless, and any
analysis you create with it will be useless
13. Congratulations! You’re Having Data!
• Data starts off as a twinkle is some source
code’s eye
• It’s born when:
• A user fills out an online form
• A product scans across a register
• You make a phone call
• You update your social media status
• All useless unless put into context
14. Organizational Data Goals
• Companies want to effectively:
• Manage…all their data
• Leverage…information and
opportunities
• Integrate…applications and devices
• Store…data inexpensively (read: cloud)
• Access…data anytime, anywhere
15. Data Done Right
• If done well, there’s immediate competitive
advantage
16. Are We Doing It Right?
• Information Asset Optimization Framework
• The what?!?
• A framework used to gauge a company’s
data maturity position
19. Getting to the Point
• Importance of data in the organization
• Importance of giving context to data
• Now what?
20. The Data Detective
• Understands everything we just covered
• Business focused, but technology skilled
• Wait a second…this sounds like a Data
Analyst or a Business Analyst!
21. The Data Detective
• Actually, the Data Detective is closely
related to these roles
• Key characteristics:
• Understands both business systems and
processes, as well as IT systems
• Can create front-end reports and write raw SQL
to pull data from databases
• Understands data models and how the data
relates
• Understands business strategy and objectives
• Rooted in technology, but well-versed in business
22. Why is this Role Needed?
• Bridges the language gap between
business and IT
• Understands business challenges
• Understands where to look in the data to
investigate
23. Use Case #1
• Data Detective was working with a large
grocery retailer
• Understood the business challenges and
objectives
• Used technical skills to investigate the data
• Derived valuable insight
• Increased sales
25. Situational Analysis
• Large retailer kicking off Layaway initiative
• Wanted to deem the program a success
• Incoming data spread across numerous
systems
• Promotional efforts were minimal
• Had access to all sources of data
26. The Approach
1. Business Understanding
2. Data Understanding
3. Hypothesis Creation
4. Data Preparation
5. Data Discovery and Exploration
6. Insights and Action
27. CRISP-DM
• Cross Industry Standard Process for Data
Mining
• A data mining process model that
describes commonly used approaches
that data mining experts use to tackle
problems.
29. 1. Business Understanding
• Set Objectives
• Increase Layaway market basket size
during the program
• Project Plan
• Meet with all stakeholders of the
Layaway program
• Business Success Criteria
• Increased Layaway sales or increased
product in Layaway market basket
Business
Understanding
30. 2. Data Understanding
• Reviewing samples of the data
• Learning relationships between the
different entities
This lays down theground work for data
discovery
Data
Understanding
31. 3. Hypothesis Creation
• With an understanding of the business
expectations and the data available:
We could relate the applicable databases
to increase a department’s Layaway sales
through targeted marketing and
promotional efforts
32. 4. Data Preparation
CRM DB Loyalty
DB
Transactional
DB
Extractio
n • Datamart
• Datalab
• Excel
Spreadsheet
** Dimension correlation between sets
comes into play
Product
DB
Data
Preparation
33. 4. Data Preparation/Modeling
• Datamart
• Datalab
• Excel
Spreadsheet
Load
Visualization and Data
Exploration Tools
Data Wrangling
Modeling
Data
Preparation
34. 5. Data Discovery
• Product database allowed for analysis of
whose buying what, when, and how much
• Discovered that the loyalty database
could be used to tie coupon value back to
total cost to ensure gross profit is made
• Statistical analysis showed over 50% of
Layaway customers signed up for
texting/email
Data
Discovery
Exploration
35. Insights: The Target
Target: Customers who had a PS4 or Xbox
One in their Layaway basket that did not
have outstanding payments
PS4 and Xbox One were the top 2
products being placed in Layaway
market baskets
Insights and
Action
36. Insights: The Offer
A coupon was presented that allowed for
20% off a video game as long as it was
added to their layaway basket
This coupon
still allowed
for positive
gross profit
Insights and
Action
37. Action
• Blasted out a text message with the
digital coupon URL to all customers with
an Xbox One, PS4, or an associated
game controller
Insights and
Action
39. Detective vs. Scientist
• So, a Data Detective is not exactly a
Business / Data Analyst
• What about a Data Scientist?
40. Data Scientist
• Techopedia.com explains Data Scientist:
“Data scientists generally analyze big data, or data
depositories that are maintained throughout an
organization or website's existence, but are of virtually
no use as far as strategic or monetary benefit is
concerned. Data scientists are equipped with statistical
models and analyze past and current data from such
data stores to derive recommendations and suggestions
for optimal business decision making.”
41. Data Scientist
• Usually does not interact directly with the
business
• Focused more on discovering insights from Big
Data
• More hypothesis testing
• Trying to find that “Ah-ha!” insight
• Social skills may be lacking
42. The Role of the Data Detective
• Uncover missed business opportunities
• Discover new business opportunities
• Recommend changes to the data model
• Help resolve data quality issues
• Interact heavily with both business and IT
43. Use Case #2
• Data Detective working with a subset of
data from a building materials supply and
manufacturing company
• Data dumped into an Excel file
• Scope included multiple product line
• Find the pricing sweet spot
48. SEMMA
• Five phases
developed by
SAS Institute
• Aimed more
specifically
toward data
analysis upfront
Sample
Explore
Modify
Model
Assess
49. Sample
• Acquired data dump
excel spreadsheet
• 47,000 rows of data
• Quote data
• Only had access to the
spreadsheet
Sampl
e
50. Data Exploration
• No hierarchal structure
• Inconsistent data and formatting
• Pricing was down to the individual product
level
• Given geographical sales regions
• Quote Status
• Won, Lost, Pending
• Pricing, GeoLoc, and Quote Status could
all be related and rolled up/drilled down
Explor
e
51. Modify
• The Modify phase contains methods to
select, create and transform variables in
preparation for data modeling
• Cleaned up the data quality
• Zip codes, rep names, project names
• Standardized variables
Modify Model
52. Insights Discovered
• Sweet spot for pricing
by time and regional
dimensions
• Closing Ratio
percentages
• Pricing could now be
tied to quoting status
• Quotes could now be
tied to rep performance
Assess
54. Action
• With a pricing baseline
established, quotes now
have more direction
• Increase in annual
sales growth
• More visibility into how
sales are trending
• Enhanced Operations
to Rep follow through
56. Case Closed
• Proliferation of data at astronomical rates
• Data must have context to be useful
• Do you know your company’s information
usage maturity level?
• Data Detective vs. Business/Data Analyst
vs. Data Scientist
• How many opportunities is your company
missing?
58. Take Our Data Insight Challenge!
• Provide us with a subset of your data
• We’ll investigate and analyze the data
• We’ll derive insights you may not have
known were there
www.yldatachallenge.com
Editor's Notes
Add images of police officer, investigator, forensics