Conference Review – IBM Business Analytics Summit, Chicago | Data & Analytics
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Conference Review – IBM Business Analytics Summit, Chicago

I recently attended the IBM Business Analytics Summit in Chicago.  My IBM “Analytics” experience at this point in my career is limited.  Consequently, the purpose of attending this summit was to gain a better understanding of the IBM product offerings, gain some contacts in the industry, and to understand some “real-life” examples of what is being done.  As such, I would like to provide a summary and some interesting points that were presented at the summit in this blog.  I was not able to attend all of the presentations due to some client meetings during the presentations, but the sessions that I did attend were excellent!

Presentation: Weaving Analytics into the Fabric of Your Business



This presentation was delivered by Jason Verlen (Director, Product Strategy & Product Management, and Predictive Analytics at IBM).  Jason’s focus was on the technology disruptions – “Social, Big Data, Mobile, Cloud” – and the challenges that are introduced through these disruptions.  One or two analytical capabilities will not render success anymore.  He emphasized that there is not a magic potion (combination of capabilities) that is necessary to create success.  Jason likened analytical decision making to an “Analytics Journey” which requires going from “Passive Viewing – Slowing changing, Scheduled, Historical metrics, Knowledge, Separate but equal, Management views, Absolute measures, On premises, Inside out thinking” to “Active Discovery – Rapidly adapting, Interactive/real time, Dynamic predictive models, Next best action, Completely integrated, Operational models, Risk-adjusted performance, Private/public cloud, Outside in thinking” and delivering a richer set of analytic capabilities.  The key takeaway was, “Not about having capabilities, but about integrating those capabilities into your business.”

This presentation also included a demo of some of the IBM Analytical software capabilities.  In this demo a retail bank, MBI Banking, was used to demonstrate the progression of completing analysis using enterprise data versus integrating the use of social media to increase loyalty and increase customer share of wallet.  I do not want to go into the details of the demo, but the synopsis was demonstrating how a “Passive” analysis of the enterprise data revealed that adjusting interest rates would help increase profitability of the credit card product.  This was done by looking at a series of performance reports across their different product lines which identified that credit cards had the lowest profitability.  After completing “what-if” analysis it was determined that lowering credit card rates would increase the profitability of the product.  However, after completing “Active” analysis by looking at external social media data, it was discovered that reward points are more important than interest rates.  This was done by Social Media Analytics which completed analysis of “Snippets of text” and “Hot words” to determine that, according to social media sites, reward points were the most talked about.

Interesting Points:

  • Key shifts are fueling the urgency for Smarter Analytics
    • Emergence of Big Data
      • 65% of business are not using Big Data for business advantage
    • Increasing Consumer expectations
      • 84% of consumers rely on social networks for purchase decisions
    • Accelerating pressure to do more with less
      • 32% of organizations using advanced analytics enjoy 32% higher return on invested capital
  • Analytics Journey (% of organizations that identified their organization in the step; n = 5748 organizations; IBM Study of AQ respondents, 2012)
    • Step 1: Novice (5%)
      • using spreadsheets, no governance, manual intervention and extracts
    • Step 2: Builder (64%)
      • departmental governance, sharing some standards, silo KPIs and metrics, emerging CoEs
    • Step 3: Leader (22%)
      • Beginning to work across silos, unified view of data, aligned KPIs and metrics, full self-service, aligned CoEs
    • Step 4: Master (9%)
      • 360 degree insights, enterprise aligned, highly collaborative, top-down approach to analytics
  • Big Data: technology outlook
  • big_data
  • Innovations and capabilities are the starting point and must be woven into your business to deliver value
    • Customer
      • Advanced client segmentation
      • Leveraging customer sentiment analysis
      • Reducing customer churn
    • Finance
      • Enabling continuous planning and forecasting
      • Automating financial and management reporting
      • Improving visibility, insight and control
    • Risk
      • Making risk-aware decisions
      • Managing financial and operational risks
      • Reducing the cost of compliance
    • Operations
        • Optimizing the supply chain
        • Deploying predictive maintenance capabilities
        • Transform threat & fraud identification processes
  • And applied to address key business challenges, across industries
    • Customer
      • Banking
        • Increase account profitability
      • Insurance
        • Retain policy holders with better service & marketing
      • Retail
        • Understand sales patterns
      • Telecommunications
        • Reduce churn with custom retention offers
    • Finance
      • Government
        • Effective budget management
      • Retail
        • Develop dynamic merchandise plans
      • Industrial
        • Plan and forecast sales & operations
    • Operations
      • Industrial
        • Predict maintenance issues before they occur
      • Retail
        • Improve store performance with P&L reports
      • Telecommunications
        • Understand & manage network traffic
      • Banking
        • Measure branch performance
      • Insurance
        • Streamline claims process
      • Government
        • Reduce fraud and waste
    • Risk
      • Banking
        • Align risk strategy and financial planning
        • Improve compliance & regulatory response
      • Insurance
        • Improve compliance & regulatory response

Presentation: Succeeding with Analytics: Six roles that probably aren’t in your job description


This presentation was delivered by Bob Morison (Guest Keynote; Intellectual Capital – Marketing Publications – Business Research).  He presented his material very well and this topic was very intriguing given that Data Scientists are the “Hot” new role in the world of BI.  I was curious to hear if this was going to be all about the role of Data Scientists and how this role is vital to every organization.  Gladly, it was not.  Bob’s focus was on the integration of “Technical Analytics” (Technical Definition: “Analytics means the extensive use of data, statistical and quantitative analysis, and explanatory and predictive models to drive decisions and actions.”) and an “Analytical Organization” (Behavioral Definition: “Analytical means being attentive to the information you’ve got; exploring what it means; recognizing patterns, connections, novelties, and new questions to ask; and deciding and acting accordingly.”) to generate “Successful Analytics.”  A very important point that Bob made was, when deciding where to begin an analytics project an organization should start by looking at their process flows and identify the most important process – this is the starting point where the most impact can occur.

Bob identified “Three Cadres” (top-down knowledge of analytics (triangle image)) of analytical talent that is required by every organization in order to be successful.
1. Professionals

  • Not enough trained statisticians, model builders, data scientists
  • Need professionals; top-notch folks, but not a ton

2. Semi-Pros: Quantitatively oriented business analysts, problem-solvers, advisors

  • Six-sigma/lean teams
  • Probably have more than you think

3. Amateurs: Analytically oriented and capable business people

  • We want the business to be populated with Amateurs

The six (actually seven, but who is counting) roles that Bob identified for Successful Analytics are below.

  • Organizational Success Factors You Can Influence

1. Data Smarts: be a Data Demon

  • Attuned to the information you are using and what to do with it
  • Common Question:
    • Is it complete, accurate, timely, accessible, and secure?
    • Demon makes sure
      • Proves data is sufficient, combinable, unique, and valuable.

2. Clear Targets: Be an opportunity finder

  • Review performance excessively
  • Generate and explore data
  • Offer new metrics
    • “Be wary of benchmarking, because they make you look at the pack; not breakthrough!”
    • Questions behind Decisions: good device to start a conversation
      • Analyze the past to look at the present to predict the future
        • Information
          • Past: what happened (reporting)?
            • Past provides you alerts on the present
            • Present: what is happening now (alerts)?
            • Future: what will happen (extrapolation)?
            • Insight
              • Past: how and why did it happen (modeling, experimental design)?
              • Present: What’s the next best action? (recommendation)
              • Future: What’s the best /worst that can happen? (Prediction, Optimization, Simulation)

3. Able Amateurs: be a Personal Trainer

  • Aware of data and his/her decision making process
  • Self-serve as much as possible à self-service BI
  • Understand, trust and use analytics
  • To help them be successful
    • Incorporate early
    • Teach
    • Communicate visually
    • Include and rotate people in and out
      • Some organization also rotate professional folks (modelers) to help them understand the business

4. Analytical Culture: Hallmark Behaviors

  • Experiment – test and learn
  • Search for the truth
  • Value negative results
    • All organizations say they allow for negative results, but there are limits to how many times someone can “fail”
    • Never rest on your laurels
      • Wants to get better all the time
      • deming

5. Analytical Culture: Be a Cultural Arbiter

  • Culture – Driven by the executive team
    • A lot of times their behavior does not trickle down into the organization
    • “Push back” when data and rigor are weak
    • “Pat on the back” when data and rigor are strong
    • Asks tough questions
      • To make sure that “analytics” are occurring
      • What data supports your conclusion?
      • How did you test your plan of action?
      • Did you use a control group?
    • Plays devil advocate

6. Strong Leadership: Be an Exemplar

  • Everyone:
    • Be an amateur
    • Be an Exemplar of analytical behavior and decision making
    • Manager:
      • Reward people who are being analytical
      • Institute a devil’s advocate
        • Some organizations openly assign a devil’s advocate in meetings in order to spark conversation and incorporate this role into the group
        • Appoint someone to be one during meetings
          • It’s their obligation to speak up
          • People start to get used to a free-flow of voicing opinions and questioning “things”
    • Executive
      • Sponsor analytics projects
        • Pay attention to them and review success often
          • Not in 6 months
      • Uses analytics in performance management in the enterprise
        • Balanced scorecards, dashboards, etc.
      • Bob’s Devil’s advocate questions when looking at scorecards
        • How much is predictive?
          • Usually rear-view mirror reporting
          • How much of this information reports across categories?
            • Usually does not happen
            • When was the last time it was revised
              • Should reverse an area for experimentation/sandbox

7. Ample Talent: Be a Community Organizer (Supply Side Success Factor)

  • Bring analytics folks into the organization
    • Bring people together inside and outside the organization
    • Curiosity is a key personality of analysts
    • Clearinghouse type people:
      • Always on the lookout for new information

Bob provided the below “Final Blessings” that I would like to share.  I thought this summarized his presentation very well.  It is not about having all of these skills yourself.  Rather it is about understanding and using the skills in your organization.  “Indulge your curiosity, Influence our friends, Enable your organization. May your analytics always be good – and your judgment even better. ”

Presentation:  Drive Actionable Business Insights from Social Media
This presentation was delivered by Scott Goenendal (Program Director – Predictive Analytics Market Management at IBM).  This topic is one of more interesting topics for me personally.  I find the use of Social Media Analytics very intriguing.  The focus of Scott’s presentation was on Social Media ROI and the “fluffy” measurement that is used.  Admittedly, Scott mentioned that determining the ROI of Social Media Analytics is vague and there is no “magic formula,” but that should not deter organizations from using it.  It should be used across all business units within an organization, but Marketing should be the heaviest user of it – see below for the growth stats.  An interesting example he gave for the use of Social Media Analytics was for a phone company using it to determine early warning signs of an outage.  If Social Media Analytics are in place to search for hot words across a geography, then this information could be shared with customer service representatives to alert them about the outage.

Interesting Points:

  • Succeeding with Analytics: Where is the growth?
    • Top functions applying social approaches (% of functions using analytics in the next 2 years)
      • Marketing: 75%
      • Public relations: 64%
      • Human resources: 62%
      • Sales: 60%
      • IT: 53%
      • Customer Service (call center): 54%
        • Highest growth over the next 2 years – 42% growth
  • The number of initiatives that connect employees with external parties will rise significantly
    • Use of social business: % next 2 years
      • Enable customer interaction: 50%
      • Enable vendor/partner interaction: 46%
      • Leverage external talent (ex: crowdsourcing): 25%
  • IBM’s Framework is Key to: Engagement, Product Development and Partnering
  • IBMs_Framework

Overall: Excellent summit; worth attending!  Did I mention it is FREE?

Overall I felt the summit was very informative.  The journey from “Passive Viewing” to “Active Discovery” is one that I see many of my clients discussing.  However, many of them are timid or unaware on how to begin.  IBM Business Analytics solutions appear to deliver a rich and comprehensive set of tools and capabilities to assist clients with this journey.  Needless to say, but I am very eager to grow my skillset in this space and assist my clients with this journey.

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