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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

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Where Is My Robot? 1962Why Am I Working So Hard? 2

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Decisions: A Miracle Happens?Decision Processes? 40 years of BI

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Outline for Today’s Discussion1. Analytics + BI2. Ease of Use3. Related topics and discussion 4

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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

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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

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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?

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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.

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Willie Sutton: Infamous Bank Robber Q: Willie, why do you rob banks? A: Because that’s where the money is

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We’re Not Quite There Yet 10

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Even Big Data Doesn’t Speak for Itself • Incomplete • Behaviors under- represented • Anonymizing disasters • Selection • Provider limitations Not a crystal ball 11

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My Generation This Generation Control Experience Security Engagement Stability Gamification Manage from Scarcity Open Source Single Version of Truth Context

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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

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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

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But SomeoneStill Has ToCount the Beans 16

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• Questions and maybe answers

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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

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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

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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

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“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

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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.

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Conclusion EOU• Questions?

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Big Is RelativeThough Volume is interesting, it isn’t what distinguishes Big Data

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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

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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?

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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

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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

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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

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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

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• Questions