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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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Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved
BI, Analytics and Ease of Use
Neil RadenFounder, Hired Brains ResearchPrincipal, Radiant Advisors
TDWI NY Chapter, March 6, 2013Twitter: NeilRaden
Blog: http://hiredbrains.wordpress.comWebsite: http://www.hiredbrains.com
Mail: [email protected]: http://www.linkedin.com/in/neilraden
Where Is My Robot?
2
1962
Why Am I Working So Hard?
Decisions: A Miracle Happens?
40 years of BIDecision Processes?
Outline for Today’s Discussion
1. Analytics + BI
2. Ease of Use
3. Related topics and discussion
4
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
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
Definition vs. Meaning
‐Neil Armstrong‐Apollo 11‐July 20, 1969‐Tranquility Base, Moon, 90210
‐First human to step on another planet‐End of the “space race”‐Healthcare diagnostics & therapeutics‐Microelectronics‐Conspiracy theories: where are the stars?
Definition
Meaning
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.
Willie Sutton: Infamous Bank RobberQ: Willie, why do you rob banks?
A: Because that’s where the money is
We’re Not Quite There Yet
10
Even Big Data Doesn’t Speak for Itself
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• Incomplete• Behaviors under-
represented• Anonymizing
disasters• Selection• Provider limitations
Not a crystal ball
My Generation This Generation
ControlSecurityStabilityManage from ScarcitySingle Version of Truth
ExperienceEngagementGamificationOpen SourceContext
1950 1960 1970 1980 1990 2000
Batch Reporting
CICS/OLTP
C/S OLTP
Y2K/ERP
4GL/PC/SS DW/BI
Big DataHybrid
2010
Convergence: End of managing from scarcity
2020
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A Final Thought About Analytics
15
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.
Any company can“Compete on Analytics.”
But not like this
Stock Market Returns for the “Competing on Analytics” Cohort
‐80%
‐40%
0%
40%
80%
120%
Amazon
Marrio
tt
Hond
a
Intel
Novartis
Wal‐M
art
UPS
Veriz
on
P & G
Progressive
Capital O
ne
Yaho
o
Dell
Barclays
Average Stock Market Return
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But Someone
Still Has To
Count the Beans
• Questions and maybe answers
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 obvious
From Ease of Use and Interface Appeal in Business Intelligence ToolsBy Cindi Howson, BiScorecard
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
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
“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
Presumed vs Actual EOU
Presumed 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 job
Actual Ease of Use A sweeper with exceptional suction that
vacuums in one sweep and has an easy to
empty canister with no bag.
Conclusion EOU
• Questions?
Big Is Relative
Though Volume is interesting, it isn’t what distinguishes Big Data
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
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?
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
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Descriptive Title Quantitative Sophistication/Numeracy
Sample Roles
Type I Quantitative R&D PhD or equivalent Creation of theory, development of algorithms. Academic /research. Work in business/government for very specialized roles
Type II Data Scientist or Quantitative Analyst
Advanced Math/Stat, not necessarily PhD
Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge.
Type III Operational Analytics Good business domain, background in statistics optional
Running and managing analytical models. Strong skills in and/or project management of analytical systems implementation
Type IV Business Intelligence/ Discovery
Data and numbers oriented, but no special advanced statistical skills
Reporting, dashboard, OLAP and visualization, some design, posterior analysis of results from quantitative methods. Spreadsheets,“business discovery tools”
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Analytic Types
Types of Analysis
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
New Best Practices for BIfrom “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
• Questions
