Tag Archives: Predictive Analytics

IBM Report on Analytics


In October, IBM released a report from their Institute for Business Value titled Analytics – A Blueprint for Value. IBM releases these reports on a periodic basis, and this one is focused on the growing importance of analytics to business success. Through their analysis, they came up with nine levers that represent the sets of capabilities that most differentiated leaders exhibit:

  1. Culture: Availability and use of data and analytics within an organization
  2. Data: Structure and formality of the organization’s data governance process and the security of its data
  3. Expertise: Development of and access to data management and analytic skills and capabilities
  4. Funding: Financial rigor in the analytics funding process
  5. Measurement: Evaluating the impact on business outcomes
  6. Platform: Integrated capabilities delivered by hardware and software
  7. Source of value: Actions and decisions that generate results
  8. Sponsorship: Executive support and involvement
  9. Trust: Organizational confidence

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A Closer Look at Transformation: Sense and Respond Systems


Next up in this transformations series is the sixth enabler: sense and respond systems. These systems are critical to the transformation agenda, as most of the disruptive technologies likely to impact the enterprise in the next decade have data at its core. The resulting data explosion promises to complicate information management for most companies. As the speed of business accelerates and the amount of data flowing through company ecosystems expands, the need to sense stimuli and enable a real time response intensifies. Fortunately, rapid advancements in the price and performance of technology make realizing this sense and respond paradigm achievable and economical for a wide range of use cases – but this is arguably one of the most difficult components of transformation road maps.

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Digital Enterprise Road Map Series: Part 6 – Insight


Part six wraps up our Digital Enterprise road map series with a focus on moving insight delivery from descriptive to prescriptive. Throughout this series, I have stressed the importance of analytic excellence to long term success. But current methods such as traditional business intelligence (BI) focus on reporting and analysis that seeks to answer questions related to past events – what happened. Advanced analytics seeks to answer questions such as: why is this happening, what if these trends continue, what will happen next (predict), and what is the best that can happen (prescribe). There is a growing view that prescribing outcomes is the ultimate role of analytics. To accomplish this, analytic initiatives need to leverage an insight-action-outcome framework that starts by defining outcome-enabling insight and ends with a focus on data provisioning.

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A New Kind of Intelligence


The explosion of data and content is not limited to social media and represents a top of mind issue for many companies.  The opportunity exists to create unprecedented business value – but there are significant hurdles like greater risk exposure, more complicated risk management, and difficulty extracting relevant insight from large volumes of data. 

As volume grows, automation is critical. For example, social media monitoring is a common practice today, one that becomes increasingly ineffective and costly as the social web expands. Monitoring tools that enable the analysis of dialog on social networks like LinkedIn, FaceBook and Twitter provide a basic level of insight. But a deeper level of insight still requires a manual process, where irrelevant content is filtered before finding meaningful insight. Information management is therefore a growing challenge.  

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From Social Listening to the Prescriptive Enterprise


I find myself talking a lot lately about the slow evolution from basic social listening to a more robust use of analytics to truly gain actionable business insight. I have long felt the evolution was inevitable – of course I often think these things and they take years to materialize – a story for a different day. This Recent Forrester Blog Post touches on the notion of moving from social listening, to integrating social and customer data. It also presents a roadmap for how to move through the crawl-walk-run-fly stages.

I am sure the authors realize that although this is a piece of the evolution, there are other steps along the path to actionable business insight. I’m already seeing the movement from basic social media monitoring to the broader use of text analytic platforms. Companies that started their journey focused on brand mentions are evolving to new use cases that deliver considerable business value. One of the signs that we are reaching an inflection point can be found in a growing move towards evaluating text analytics software for a broader set of use cases.

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The Hottest Trends in Analytics


In this recent video, Eric Siegel, PhD, Conference Chair for Predictive Analytics World and Text Analytics World, discusses three innovative advanced analytics trends.  

These trends build upon the growing focus on social data and text analytics. The three areas covered are:

  1. Using social data to improve predictive models
  2. Applying text analytics unstructured data to better predict customer behavior
  3. Using net-lift modeling to determine which customers will be receptive to retention offers.

It’s a brief five minute video that is well worth the look.


Predictive Analytics in the Insurance Industry


This very good Article by Anand S. Rao discusses the growing use of predictive analytics in the Insurance Industry. I believe Mr. Rao is right on the mark – although I continue to emphasize the expanding role of Text Analytics in the analytic value equation. In this article, he identifies some of the drivers of predictive analytics adoption.

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