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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The Evovling Role of Business Analytics


Business Analytics refers to the skills, technologies, applications and practices for the continuous exploration of data to gain insight that drive business decisions. Business Analytics is multi-faceted. It combines multiple forms of analytics and applies the right method to deliver expected results. It focuses on developing new insights using techniques including, data mining, predictive analytics, natural language processing, artificial intelligence, statistical analysis and quantitative analysis. In addition, domain knowledge is a key component of the business analytics portfolio. Business Analytics can then be viewed as the combination of domain knowledge and all forms of analytics in a way that creates analytic applications focused on enabling specific business outcomes.

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A look at 2011


As 2010 winds down, it is time to look ahead to 2011. I have high expectations as the calendar turns, and I believe 2011 will be the year of adoption. I believe we’ve reached a point where cash will come off the sidelines and I expect to see technology investments focused on revenue generation and value creation. With that as a backdrop, I’ll Label 2011 as the year of collaboration and intelligence – the two areas that I believe align very well with pressing business priorities. So here are my thoughts on 2011.

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Realizing the Benefits of CRM


This Press Release from Verint continues the Social Media and customer service drum beat. The recurring themes are:

  1. A growing challenge to identify, collect and act upon data delivered through an expanding number of customer communications channels
  2. The need for an aggregated and unified view of customer data across all channels
  3. Analysis that delivers insight drives rapid and informed action
  4. Companies are not equipped to analyze the interactions that occur via email, chat, social media, forums, etc.
  5. There is a heightened need for text analytics. Solutions that mine interactions and feedback across multichannel customer communications – including email, chat, blogs, review sites, social media and other text-based channels – continue to emerge
  6. Workflow delivers insight to the people that are best equipped to manage issues, address specific customer service concerns, or get ahead of negative sentiments in social media

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Reinventing Customer Relationships


CEOs believe that the time has come to reinvent customer relationships. That’s one of the key findings in IBM’s 2010 CEO Study titled “Capitalizing on Complexity”. This belief is based on the following observations: 

  1. The world is massively interconnected, thereby making customer intimacy a priority
  2. Customers have more options due to globalization and dramatic increases in available information
  3. Differentiation will come from innovation and co-creation with customers
  4. The information explosion presents a great opportunity to develop deep customer insights 

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Analyzing Data in Motion


In his “Building a Smarter Planet” Blog, Steve Hamm talks about Analyzing Data in Motion to save stroke victims. There is no better way to appreciate the power of emerging analytic technology than viewing it in the context of life saving applications. This example combines very powerful stream computing technology with predictive analytics and data mining. This combination enables high speed, scalable and complex analytics of data streams in motion. As the author describes, they are using this technology to look for patterns in data that could help them identify patients who are experiencing a severe complication to ruptured brain aneurysms.

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A look at Trends in Analytics


In his Industry Trend Blog for 2010, Nenshad Bardoliwalla identified the top ten trends in analytics, business intelligence, and performance management. The author sees a vibrant market and a resurgence of innovation.

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Gartner’s Strategic Technologies for 2011


Earlier this week at the Gartner Symposium / ITxpo, Gartner presented their list of strategic technologies for 2011.  Four of the ten technologies fit squarely in the social technologies and business analytics space covered by this Blog.

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Hype Cycle Report from Gartner


In the recent Gartner Hype Cycle Report issued in August, Gartner had the following to say about predictive analytics:

“The quantity and variety of digital data continue to explode, along with the opportunities to analyze and gain insight from new sources such as location information and social media. The techniques themselves, such as predictive analytics, are relatively well established in many cases; the value resides in applying them in new applications such as social analytics and sentiment analysis”

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