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Creating Big Data Success with the Collaboration of Business and IT

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Creating Big Data Success with the Collaboration of Business and IT

  1. 1. Creating Big Data Success with the Collaboration of Business and IT Teams By Edward Chenard
  2. 2. Edward Chenard - Started big data at Best Buy, was working in big data at GE before it was called big data. - Set up one of the first hadoop clusters in Retail and the Midwest. - Won tax innovation credits for my work on big data - Tekne finalist for big data innovation - Set up big data, data science and data visualization teams - Managed teams as large as 300 with product portfolios of over $4B - I spend my time in cold places Twitter: Echenard Slideshare: Echenard
  3. 3. Everyone is Jumping on to Big Data
  4. 4. The Reality of Big Data • As many as 3/4 of big data projects fail according to one Gartner study. • The third is that 39 percent of the failure of Big Data project is attributed to the fact the data is siloed and there’s not a lot of cooperation in gaining access to that data. Now that is the oldest problem in the history of IT. - Infochimps • 1. They focus on technology rather than business opportunities. • 2. They are unable to provide data access to subject matter experts. • 3. They fail to achieve enterprise adoption. Terradata's top three reasons why big data projects fail. • Lack of alignment. Business and IT groups are not aligned on the business problem they need to solve but instead are tackling it from a technology perspective. Lack of true commitment from business stakeholders also makes alignment harder to achieve. Peter Sheldon - Forrester Analyst
  5. 5. Big Data at Most Companies IT Business
  6. 6. How a typical big data project takes place • Someone hears about big data and then seeks funding. • Other teams want to own it. Months of fighting takes place over ownership. • The opportunity is either lost or the mission of the project gets altered. • Teams work in silos, poor communication takes place as teams spend more time playing CYA. Achievement: Project failure with cost over runs, deadlines missed and lack of focus.
  7. 7. No One Team can Handle Big Data Alone
  8. 8. Current state of big data collaboration Business (Strategy) Analyst IT (Insights) (Systems)
  9. 9. What does Big Data Really Mean to Business “The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it's going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.” Hal Varian
  10. 10. Everything is about discovery
  11. 11. Why a focus on collaboration? • Projects fail for simple reasons, lack of understanding the need for better collaboration and then knowing how to implement that collaboration, helps to ensure success. • Failure does not need to be an option • Big data is the future of how we live and work, but only if we get it right. Big data can be bigger than ecommerce in terms of impact on how we live.
  12. 12. Everyone Discovers
  13. 13. Data Discoverers “The Data Discoverers looks a lot like you and me, but what‟s different is their preoccupation with personal data. They are relentlessly digital, they obsessively record everything about their personal lives, and they think that data is sexy. In fact, the bigger the data, the sexier it becomes. Their lives - from a data perspective, at least - are perfectly groomed.” data as a lifestyle
  14. 14. Data Discoverers Data Discoverers are setting the trend in what will be common place in just a few short years. More people will want to use their data and the consumerization of data and technology will continue. As this trend goes, only organization that learn to merge the various disciplines of strategy, analytics and IT, will be successful data as a lifestyle
  15. 15. How We Need to Look at Discovery Horizon Future Soon Present Past
  16. 16. Discovery is the leading emerging interaction category of the Age of Insight
  17. 17. Complex ecosystems: multi-channel experiences everyware environments Service models Reactive data dynamic perspectives
  18. 18. Activity Centered Thinking
  19. 19. How Different Functions See the Same Issue “Understand the quality performance of a system so I can better determine if I need to replace it.” - IT “Understand a portfolio's exposures to assess portfolio-level investment mix.” - Strategy Manager “I need to understand the customer trends in the data so I can better create models.” - Analyst
  20. 20. Identifying Modes “Understand the quality performance of a system so I can better determine if I need to replace it.” - IT “Understand a portfolio's exposures to assess portfolio-level investment mix.” - Strategy Manager “Understand the customer trends in the data so I can better create models.” - Analyst Mode = „Comprehend‟ (understand)
  21. 21. Comprehending „To generate insight by understanding the nature or meaning of an item or data set‟ e.g. “I need to analyze and understand consumer-customer-market trends to inform brand strategy & communications plan” – Director, Brand Image Each Team has the same goal, to understand, what they may want to understand is often different but not exclusive or limit to the other team’s need to understand.
  22. 22. Identifying Modes “I need visibility into the systems my colleagues are using in order to maximize the network ROI for the company.” - IT “I need to identify customers/marketers/dealers failing & at risk of de-branding based on performance problems.” - Strategy “I need to identify the best customer/consumer/region targets for our brand/products.” - Analyst Mode = „Explore‟
  23. 23. Modes are the verbs of discovery scenarios.
  24. 24. 9 distinct modes Locate Verify Monitor Compare Comprehend Explore Analyze Evaluate Synthesize
  25. 25. Where to Start
  26. 26. The Business Value Framework Initiatives Customer Acceptance Business value Customer Acceptance Focus on Customers Perceived Value Business value Focus on Internals Ease of Data Collection Prerecorded Customer Needs Timeliness Ease of Implementation Ease of Data Collection Value Perceived Customer’s Wallet Share Ease of Implementation Production Flexibility Different Products Automated and prompted Customer’s Wallet Share More Products Production Efficiency
  27. 27. How work gets structured Vision & Goals Clearly articulated vision for personalization and recommendations, precisely defined goals with how to measure. Defined scope of the product. Governance Execution Market strategy, customer segmentation, prioritization, org focus, measurement and incentive systems Production process, flexibility at scale, efficiency, relationship management, benchmarking , metrics, initiatives
  28. 28. Framing Collaboration Value (Shared): Show me the money!?! - Measurable Results Multi-Channel Case Studies Strategy: Where are you headed? IT: What Tools and Why - MapReduce, Hadoop Cassandra, The Cloud Pig, Hive, HDFS Big Data Collaboration - Buy vs. Build Open source options Alignment with Analytical Infrastructure Speed to Market Privacy Considerations Analyst: Who, How, Where? - Data Scientist vs. Statistician Where to find talent? Retain, Train Offshore vs. Onshore University involvement 28
  29. 29. Always Remember: Data, Insights, Actions Listen Share Engage Innovate Perform • Listen to the data streams • Share the data with the rest of the organization • Engage to the data to find the insights • Innovate new ideas from the insights gained from the data • Perform insightful actions from the data to create better customer experiences
  30. 30. Collaboration helps to achieve where others fail.
  31. 31. Thank You! • Edward Chenard – Twitter: Echenard – Email: – Blog:
  32. 32. Resources • Why Do Big IT Projects Fail So Often? ( • A statisticians view of big data (!) • Using Big Data to Create a Data Driven Organization ( • The Language of Discover ( n=download_notification) • Images by Emma Kim, Jason Maehl, David Spurdens, Evgenia Shadrina and Hill Street Studio

Notas del editor

  • Tale of Two dinner parties and the develop of big data trends in 2014I create big data conferencesCommon problem was a theme, project failure
  • I am a practitioner of big dataI look into the future 90% accuracyGE industrial internetFocus on the practical and solutionsThis is about solutions
  • Data is expected to grow as 4 Billion people go online creating 50 trillion gigs of data each year. Information is doubling at a rate that we have never seen before and the rate is accelerating. With this acceleration comes new challenges In 2011 almost no one in MN was working on big data, now, almost everyone is at least looking at it.
  • Most big data projects fail
  • Team’s compete, they don’t collaborate.
  • Big data has evolved beyond the abilities of any one team to manage. It needs to be an organizational level endeavor otherwise it will fail, there needs to be collaboration among teams. Data equals value
  • Business handles strategy the what and whyAnalysts : who data science guys and BIIT: The how
  • Big data means value discovery, the ability to engage customers on a new level we never could before. Big Data is bigger than ecommerce.
  • Everything is about discovery, we are social creatures who love to have new experiences,Big data takes us to a new level of discovery
  • Bigger than ecommerceBig data is about discovery and understanding the people doing the discovering
  • As big data moves beyond the IT aspects, we realize that it is really about discovery and making sense of those discoveries. Everyone is a discoverer, means can be drawn from anything.
  • Data discoverers are the future, Data as a lifestyle
  • Fitbit as an example of data discoverersData as the new self discovery toolLeads to consumerization of IT IT needs to adapt to be socialThis means teaming up with marketing and letting marketing joing the conversation with data
  • How we look at big data needs to take into account the factor of time and it’s relationship to the discovery process.Most organizations are stuck on 1 and 2 with a few on 3. True innovators look at Future and Horizon
  • As data discoverers grow, the age of insight will take over the age of information, this is an age in which all teams need to work together in order to have success.
  • Age of insight is marked with Complex ecosystems, more interconnectedness think city ecosystems.Multi channel experience, no one works in a silo.Everryware: wearable and internet of thingsFluid data: reactive dataDynamic perspectives: No one view of the same, but many views, even from just one person.
  • A shift from systems and forecasts to activities as our center of design needs to take place.Data alone can’t predict an unpredictable social animal known as the human beingFocus on the activities of people, not so much predicting them because we can’t do that good of a job with what we have.
  • To be activity centered, understand how different functions see the world
  • We all have the same mode in many cases or related mode
  • Comprehension is the core of what we all seek.
  • Key is to identify the mode to find common ground for collaboration.
  • Each mode can be used to help various teams collaborate by understanding the mode in which they are operating.Mode helps to find common ground.
  • Each mode can be used to help various teams collaborate by understanding the mode in which they are operating.Mode helps to find common ground.
  • Have a framework in place to help weight priorities and attributesWhat is really important.
  • Set up a structure Understand the vision, make it clearGovernance: have structure around the tasksExecution: Know how to get it done and why
  • Know your zone of the framework.Frame itMake it simple, one pager big picture, help explain it in the simplest of form
  • Data, Insights Actions, must be core to your strategy.
  • Working with the right systems in place, teams achieve what others cannot.