What does Business Intelligence integration with R really mean

“A little prediction goes a long way” wrote Eric Siegel in his popular Predictive Analytics book. True, predictive analytics is now part and parcel of most Business Intelligence (BI), analytics and Big Data platforms and applications. Forrester Research anecdotal evidence finds that open source R is by far the most ubiquitous predictive analytics platform. Independent findings and surveys like the ones by KDNuggets and RexerAnalytics confirm our conclusions (and I quote) “The proportion of data miners using R is rapidly growing, and since 2010, R has been the most-used data mining tool.  While R is frequently used along with other tools, an increasing number of data miners also select R as their primary tool.”

To jump on this R feeding frenzy most leading BI vendors claim that they “integrate with R”, but what does that claim really mean? Our take on this – not all BI/R integration is created equal. When evaluating BI platforms for R integration, Forrester recommends considering the following integration capabilities:

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Analytics Helps Retailers Improve Customer Retention

IBM recently kicked off its big data market planning for 2014 and released a white paper that discusses how analytics create new business value for end user organizations. The major differences compared with last year’s event:

  • Organizational change. IBM has assigned a new big data practice leader for China, similar to what it’s done for other new technologies including mobile, social, and cloud. IBM can integrate resources from infrastructure (IBM STG), software (IBM SWG), and services (IBM GBS/GTS) teams, although the team members do not report directly to them.
  • A new analytics platform powered by Watson technology. The Watson Foundation platform has three new functions. It can be deployed on SoftLayer; it extends IBM’s big data analysis capabilities to social, mobile, and cloud; and it offers enterprises the power and ease of use of Watson analysis.
  • Measurable benefits from customer insights analysis. Chinese organizations have started to buy into the value of analytics and would like to invest in technology tools to optimize customer insights. AmorePacific, a Hong Kong-based skin care and cosmetics company, is using IBM’s SPSS predictive analytics solution to craft tailored messages to its customers and has improved its response rate by more than 30%. It primarily analyzes point-of-sale data, demographic information from its loyalty program, and market data such as property values in the neighborhoods where customers live.
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Data Governance: Did We Make The Right Choices?

Coming back from the SAS Industry Analyst Event left me with one big question - Are we taking into account the recommendations or insights provided through analysis and see if they actually produced positive or negative results?

It's a big question for data governance that I'm not hearing discussed around the table.  We often emphsize how data is supplied, but how it performs in it's consumed state is fogotten.  

When leading business intelligence and analytics teams I always pushed to create reports and analysis that ultimately incented action.  What you know should influence behavior and decisions, even if the influence was to say, "Don't change, keep up the good work!"  This should be a fundamental function of data govenance.  We need to care not only that the data is in the right form factor but also review what the data tells us/or how we interpret the data and did it make us better?

I've talked about the closed-loop from a master data management perspective - what you learn about customers will alter and enrich the customer master.  The connection to data governance is pretty clear in this case.  However, we shouldn't stop at raw data and master definitions.  Our attention needs to include the data business users receive and if it is trusted and accurate.  This goes back to the fact that how the business defines data is more than what exists in a database or application.  Data is a total, a percentage, an index.  This derived data is what the business expects to govern - and if derived data isn't supporting business objectives, that has to be incorporated into the data governance discussion.

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