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James Taylor

I will use this blog to discuss business challenges and how technologies like analytics, optimization and business rules can meet those challenges.

About the author >

James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

January 2010 Archives

I am working with the folks at B-eye Network and sponsors Oracle, SAS, Aha!, Adaptive and Fuzzy Logix on some research - Business Analytics: Putting Analytics To Work.There is growing interest in the power of analytics, especially predictive analytics, to improve business operations. The use of data mining and analytic techniques in operational systems is moving beyond its early adopter base in financial services and into the mainstream. As companies adopt business analytic techniques they struggle with the balance between using these techniques to improve reporting and dashboards ("Predictive Reporting" as it is sometimes called) and using them to improve systems and thus every individual transaction ("Business Analytics" or "Decision Management"). A clear understanding of what business analytics are, how to use them, and the compelling business value of doing so is called for. Hence the research.

The study will describe business analytics and what should you expect from a business analytics vendor. It will discuss the motivation for adopting business analytics and how you should approach the evaluation of business analytics as well as how business analytics fit within an enterprise and business architecture. It will discuss risks and issues and describe the benefits and challenges based on real customer experience. Finally it will discuss the kinds of decisions that will show a positive return on business analytics and how business analytics can change businesses fundamentally.

All in all it should be a lot of fun to write and I am looking forward to completing it. In the meantime you can help by taking the survey - http://www.zoomerang.com/Survey/?p=WEB22A3HRGXRBS.

Look for the report in a couple of months on BeyeResearch.

Posted January 25, 2010 7:50 AM
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Quick note to let you know that the early bird pricing for Predictive Analytics World expires this week so it's time to register. You can also use my discount code - SPEAKPAW010 - to get a 15% discount. I am running a workshop the day before (you should be there) and speaking/moderating on the first day.

Posted January 14, 2010 9:53 AM
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I got a chance to catch up with Aha! recently. Aha! is based in the Denver Tech Center and was founded back in 2006. Aha!'s premise is that it is now possible to build analytics into a platform and to focus on how to operationalize predictive models and deliver analytics within business processes. Initial customers are in healthcare, telecom, travel and transportation. Their aim is to deliver a complete analytics management system. The pain points of traditional BI solutions that they address are: limited use and access (big focus on self-service), long time to value (SaaS platform or rapid start up with minimal IT), rear-view focus (predictive analytics), piles of data (model-based analytics) and Excel/scattered data (single network). They focus on being "dynamic and aligned" and focused on business users.

Their market is the $2-$3Bn "business embedded analytics" that is part of the overall $26Bn global analytics market. In particular, they provide non power analytic users with access to analytics without having to obtain specialized skills. They see themselves helping the vast majority of business users who don't use analytics today - the people that dominate the operations of a company like marketing managers, sales managers, customer care managers, product managers, marketers, engineers, and operations specialists. Financials matter to these folks but they don't dominate the way they do with "traditional" financial department analytics users.

Aha! sells direct as a SaaS offering (setup fees and subscription), offers model development and data discovery services and licenses through OEM/SI partners. Partners are typically domain experts and vertically focused.

Some example customers include: a telecom company using analytics to handle the ROI of proactively building out a fiber network and to optimize sales and marketing to light up this fiber; a telecom handling customer retention, product segmentation and customer experience satisfaction; a healthcare company working on customer retention and acquisition.

Their offering (Axel) is a SaaS multi-tenant, multi-hosted system. It is designed to bring models into the business process - business process based models - make the analytics actionable and close the loop between analytics to actions. The whole thing is based on KPIs and designed to help companies actually act on their strategy, using a KPI model that runs from head office strategy to the front line. The platform has 5 core elements:

  • Language
    The Aha! Expressions analytic model definition language that allows business analysts to build the models
  • Dynamic services
    Secure, multi-tenant, forecasting, simulation and optimization
  • Visualization
    Self-service, near real-time and model driven
  • Data Engine
    Profiler, designer, ETL, Smart Pub/Sub
  • Extensions
    Support for third parties to extend and integrate the platform
The basic process looks like this (for a healthcare member retention example):
  1. Customer profile, billing, survey and claims data is used to create a model data file
  2. Predictive models are developed based on this data
  3. Customers are scored using these models
  4. Contact and campaign management define available actions based on these scores
  5. KPI-based models are developed using the same data
  6. Collaborative analytics link all this together to support decision making and drive ROI

The target for this customer was to reduce churn. They were up and running in 60 days, improved retention by 7.5% (v target of 3%), improved new member retention by 9%. NPV of $43M in a single enrollment period and an all-in ROI of 2447%. This was recognized at the World Health Congress as a top example of using predictive analytics to drive member retention and satisfaction. Users ranged from call center operations to VP level executives.

The model data was used to create retention or churn scores for each customer that were loaded into the operational system in batch. These scores can be updated regularly from the model data file and can be calculated live based on intra-day data or, in theory, even during a conversation (using a standard web-services interface). The use of this model is much the same as the use of any other predictive model except that the data is tightly coupled with the KPI hierarchy. Models can be built from and evaluated against the historical data that drives the KPIs, so that users start off with a valid historical base. Axel also provides a stochastic enrichment engine ( Monte Carlo simulation with category selection, probability, and triangular distributions) that supports PMML, allowing models built outside to be imported using PMML. Models can also be generated via an Microsoft Excel Template.

Aha! is driven by a KPI model hierarchy. In the case of this healthcare company it was Retention Campaign (Strategic), then the health plan a member was in (Tactical) then events within a member lifecycle (Operational). This drives how the data is viewed and KPIs - in this case customer retention measures of various kinds - are tracked against this hierarchy. So, for instance, each KPI could be viewed with respect to a specific member lifecycle step, a particular plan or a particular campaign.

Each KPI has a calculation defined for it and are calculated dynamically. In addition to mathematical calculations, the Expressions language also provides addition functionality that supports the calculation of KPIs based on Year to Date, Quarter to Date, Month to Date, Sum of values for a defined period, Average of values for a defined period, etc.

The interface allows different reference periods to be selected and the KPIs to be viewed within that period along with measures like averages, high/low values for the period, goals etc. For instance, this customer saw a lot of new members were signing up but then being lost. The prediction showed that the trend would clearly exceed their target for such losses and allowed them to see the impact on all their KPIs. This provoked a focus on the reasons for this and they found an external verification service that was needlessly disqualifying people. They had no expectation that this would be a problem and the tool allowed them both to spot it and see the impact on their KPIs quickly enough to take action before the open enrollment period was completed and the opportunity to fix it lost.

The most interesting thing about Aha! for me is the tie to a formal model of KPIs that drive from a high level to an operational level. This allows impact analysis and decision making to be clearly linked to the objectives set at different levels.

For more information on Aha!, you can visit their website at www.ahasoftware.com or download their paper on Business Embedded Analytics.

Aha! is one of the sponsors of some research I am conducting with B-Eye Network and you can participate by taking the survey athttp://www.zoomerang.com/Survey/?p=WEB22A3HRGXRBS. You can find more reviews of products on my blog at http://jtonedm.com/category/product-news/


Posted January 13, 2010 6:21 PM
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One of my google alerts pointed me to this thread on Oracle's discussion forums OTN Discussion Forums : Text Mining Dictionary .... I got the alert because one of friends at Oracle responded to the question "Where to Start?" by quoting the book (Smart (Enough) Systems) I wrote with Neil Raden:

Wrong: Catalog everything you have, and decide what data is important.
Right: Work backward from the solution, define the problem explicitly, and map out the data needed to populate the investigation and models.

This was one of Neil's bon mots and I was glad to be reminded of it. With analytics - executable analytics, business analytics, predictive analytics or any other kind of analytics - begin with the decision in mind. Figure out what it is you are trying to do, which decision you are trying to improve and work into your analytics and data from there. Be driven by your business needs, not by the data you have. You may find that you don't need to integrate this data source or clean that one to improve the decisions that drive your business. You may find that you don't even own the data you need and will need to go shopping for it. But if you don't start with the end in mind, you will never know.


Posted January 5, 2010 5:04 PM
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