ROI on BI: Analytics, New Capabilities, and Next-Generation Ease of Use

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Looking for Business Intelligence ROI? A next-generation approach to “ease of use” may hold the answer. Neil Raden shared his perspective on the topic as the keynote speaker for New York Tri-State Chapter of the Data Warehousing Institute. The event was organized by Jon Deutsch and the board of directors of the TDWI Chapter. TDWI board member Jaime Fitzgerald assisted with event design & curation. Mr Fitzgerald is also the founder of the Analytics and Data in Financial Services Meetup group in New York City, a group that works in tandem with the TDWI chapter to promote local data and analytics events.

For decades, Business Intelligence has been seen as both an essential and sometimes disappointing area of technology investment. Billions of dollars have been invested in presenting insights to business managers, but frequently the ROI has been soft and difficult to measure.Neil Raden has long been concerned about the fact that usage rates for large-sale BI systems has “stalled at 10 to 20 percent of users, depending on which survey you believe.”

Of course BI will survive, but Raden says “we may not recognize it ... the need to analyze and use data will not go away, but BI will be part of a 'decision management continuum' incorporating predictive modeling, machine learning, natural language processing, business rules, traditional BI, visualization, and collaboration capabilities.”

Neil addressed the following questions and more:

∎ Why do many Business Intelligence implementations fail to achieve their potential?
∎ Will a broader definition of the concept enable better results?
∎ How can you optimize BI systems when you are not in complete control?
∎ What best practices and case studies are most instructive?

Neil Raden is CEO and Principle Analyst at Hired Brains Research. He is a long-time practitioner, well-known author and consultant focused on data warehousing, Business Intelligence, analytics, big data, and decision sciences. He is the author of "Smart (Enough) Systems,” together with James Taylor. This book focuses on decision automation for optimizing practical business decisions. A favorite topic for Mr. Raden has been how to integrate strategy, planning, management and execution as the tools to achieve optimum decision making.

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ROI on BI: Analytics, New Capabilities, and Next-Generation Ease of Use

  1. 1. BI, Analytics and Ease of Use Neil Raden Founder, Hired Brains Research Principal, Radiant Advisors TDWI NY Chapter, March 6, 2013 Twitter: NeilRaden Blog: http://hiredbrains.wordpress.com Website: http://www.hiredbrains.com Mail: nraden@hiredbrains.com LinkedIn: http://www.linkedin.com/in/neilraden Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved
  2. 2. Where Is My Robot? 1962Why Am I Working So Hard? 2
  3. 3. Decisions: A Miracle Happens?Decision Processes? 40 years of BI
  4. 4. Outline for Today’s Discussion1. Analytics + BI2. Ease of Use3. Related topics and discussion 4
  5. 5. Analytics: Topics for Discussion• Performance – no more managing from  scarcity• Meaning – what was lacking in BI• Models – Data not a crystal ball• Decision Making • Old‐Gen vs Next‐Gen expectations 5
  6. 6. Analytics: Performance/Scarcity• Scale: grid, SSD, columnar, the H‐word• In‐Memory – HANA, Exalytics e.g.• NoSQL• Cloud Conclusion: Time to focus on the process. Not the limitations of infrastructure 6
  7. 7. Definition vs. MeaningDefinition‐Neil Armstrong‐Apollo 11‐July 20, 1969‐Tranquility Base, Moon, 90210Meaning‐First human to step on another planet‐End of the “space race”‐Healthcare diagnostics & therapeutics‐Microelectronics‐Conspiracy theories: where are the stars?
  8. 8. Deriving Meaning from Text Not Easy “Katy Perry and Russell Brand  are now officially husband and wife.” She doesn’t look like a husband… But neither does he, actually.
  9. 9. Willie Sutton: Infamous Bank Robber Q: Willie, why do you rob banks? A: Because that’s where the money is
  10. 10. We’re Not Quite There Yet 10
  11. 11. Even Big Data Doesn’t Speak for Itself • Incomplete • Behaviors under- represented • Anonymizing disasters • Selection • Provider limitations Not a crystal ball 11
  12. 12. My Generation This Generation Control Experience Security Engagement Stability Gamification Manage from Scarcity Open Source Single Version of Truth Context
  13. 13. Convergence: End of managing from scarcity Y2K/ERP C/S OLTP Big Data Hybrid CICS/OLTP Batch Reporting 4GL/PC/SS DW/BI1950 1960 1970 1980 1990 2000 2010 2020 14
  14. 14. A Final Thought About Analytics The challenge of analytics is communication and  creating a shared understanding. It’s about focusing on high impact areas, moving  forward one step at a time, being skeptical, being  creative, searching for the truth.120% Stock Market Returns for the “Competing on Analytics” Cohort80% Any company can “Compete on Analytics.”40% Average Stock Market Return 0% But not like this  Progressive Marriott Verizon Amazon Wal‐Mart UPS Yahoo Honda Barclays Novartis Dell P & G Intel Capital One‐40%‐80% 15
  15. 15. But SomeoneStill Has ToCount the Beans 16
  16. 16. • Questions and maybe answers
  17. 17. Definition of EOU Now• Familiar because it works as expected• Similar across multiple tools• Fast and efficient: Fewer clicks more automation  and personalization• Intuitive and obviousFrom Ease of Use and Interface Appeal in Business Intelligence ToolsBy Cindi Howson, BiScorecard
  18. 18. Ease of Use(fulness)• A expanded model of “ease of use”• Means to achieve positive results from  analytical work• ROI, getting return at enterprise or group  level• Aimed at getting to informed decisions
  19. 19. Compare to EOU(N)• Unlike EOU, “Ease of usefulness” addresses group collaboration and consensus• Leads directly to informed decision‐making• Moving analysis from the frontal lobes of an  analyst to other stakeholders 
  20. 20. “Engagement” Is Nice But…• Ease of use on an individual level pales in  importance to how well a given application  contributes to the overall ease of use of the  group i.e., “Ease of Usefulness”• Very easy to mistake presumed EOU to actual  EOU
  21. 21. Presumed vs Actual EOUPresumed ease of use A robotic vacuum cleaner than runs on its own,  vacuuming the floor in an unattended way. Actual Experience The small bag has to be changed frequently,  doesn’t thoroughly vacuum completely and  usually requires bringing out the conventional  sweeper to finish the jobActual Ease of Use A sweeper with exceptional suction that  vacuums in one sweep and has an easy to  empty canister with no bag.
  22. 22. Conclusion EOU• Questions?
  23. 23. Big Is RelativeThough Volume is interesting, it isn’t what distinguishes Big Data
  24. 24. What Big Data Really Does• Churn, fraud, etc., the usual suspects• Applications look for anomalies, and outliers• Begs for detail, not summary/aggrgated• Hadoop sets up environment for deep  analytics• But think bigger‐fix the world
  25. 25. Big Data vs. In‐memory• In‐memory not economical at large volumes,  even with compression• When Big Data promoters talk about 100’s of  TBs, what do you do with 1TB of RAM?• How do we reconcile this?
  26. 26. The Data Scientist• Term invented by Yahoo• Super‐tech, super‐quant• Business expert too• Interesting• We used to call them quants• Few and far between• How do you find/train them?• Hint: like actuaries 27
  27. 27. Types of AnalysisAnalytic Types Descriptive Title Quantitative  Sample Roles Sophistication/NumeracyType I Quantitative R&D PhD or equivalent Creation of theory,  development of algorithms.  Academic /research. Work in  business/government for  very specialized rolesType II Data Scientist or Quantitative  Advanced Math/Stat, not  Internal expert in statistical  Analyst necessarily PhD and mathematical modelling  and development, with solid  business domain knowledge. Type III Operational Analytics  Good business domain,  Running and managing  background in statistics  analytical models. Strong  optional skills in and/or project  management of analytical  systems implementationType IV Business Intelligence/  Data and numbers oriented,  Reporting, dashboard, OLAP  Discovery but no special advanced  and visualization, some  statistical skills design, posterior analysis of  results from quantitative  methods. Spreadsheets, “business discovery tools” 28
  28. 28. Conclusion• Ease of use matters most at the enterprise level• Organizational learning a key indicator of BI success• Tools with relevance to the work people do• IT is often focused on work of collection of  individuals, not a collaborative group• BICC’s usually aligned with the tools, not the work
  29. 29. New Best Practices for BI from “BI Is Dead. Long Live BI” http://smartdatacollective.com/node/57461• Expressiveness• Declarative method• Model visibility• Abstraction from data sources• Extensibility • Visualization • Closed‐loop processing• Continuous enhancement• Zero code• Core semantic information model (ontology)• Collaboration and workflow• Policy
  30. 30. • Questions

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