Good Example of Using Predictive Analytics

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Dilbert by Scott Adams-Dilbert ©2010, United Feature Syndicate,Inc.
Published at July 22, 2010 in dilbert.com

Instrumenting Your Enterprise for Maximum Predictive Power

I read an interesting post in the Forrester's blog, entitled Instrumenting Your Enterprise for Maximum Predictive Power, written by James Kobielus, where he told about why the companies need to be able to predict future scenarios.

He started explaining: "predictive analytics can play a pivotal role in the day-to-day operation of your business. It can help you focus strategy and continually tweak plans based on actual performance and likely future scenarios." He said: "The grand promise of predictive analytics—still largely unrealized in most companies—is that it will become ubiquitous, guiding all decisions, transactions, and applications. For the technology to rise to that challenge, organizations must move toward a comprehensive advanced analytics strategy that integrates data mining, content analytics, and in-database analytics. Already, we’ve sketched out a vision of “Service-Oriented Analytics,” under which you break down silos among data mining and content analytics initiatives and leverage these pooled resources across all business processes."

He defined: "For starters, assess whether your analytics tools support the following capabilities for developing, validating, and deploying predictive models:

- Model multiple business scenarios: You should be able to build complex models of multiple, linked business scenarios across different business, process, and subject-area domains, using such key features as strategy maps, ensemble modeling , and champion-challenger modeling.
- Incorporate multiple information types into models: You should be able to develop models against multiple information types, including unstructured content and real-time event streams, while leveraging state-of-the-art algorithm in sentiment analysis and social network analysis.
- Leverage multiple statistical algorithms and approaches in models: You should be able to develop models using the widest, most sophisticated range of statistical and mathematical algorithms and approaches, including regression, constraint-based optimization, neural networks, genetic algorithms, and support vector machines.
- Apply multiple metrics of model quality and fitness: You should be able to score and validate model quality using multiple metrics and approaches, including quality scores, lift charts, goodness-of-fit charts, comparative model evaluation, and auto best-model selection.
- Employ multiple variable discovery and assessment approaches: You should be able to build and validate models using various approaches for variable discovery, profiling, and selection, including decision trees, feature selection, clustering, association rules, affinity analysis, and outlier analysis."

Kobielus adviced: "to instrument your organization for maximum predictive power, you should also tool your advanced analytics to support the following capabilities:

- DW-integrated data preparation: To speed up and standardize the most time-consuming predictive modeling project tasks, you should be able to leverage your existing data warehouse, extract transform load, data quality, and metadata tools to support a full range of data preparation features.
- Deep application and middleware integration: To deliver models deeply into whatever heterogeneous SOA-enabled platform you happen to use, your predictive analytics tool should deploy on and/or integrate with a wide range of enterprise applications, middleware, operating platforms, and hardware substrate.
- Consistent cross-domain model governance: To avoid fostering an unmanageable glut of myriad models, your predictive analytics solution should support a wide range of tools, features, and interfaces to support life-cycle governance of models created in diverse tools.
- Flexible model deployment: To execute modeling functions--such as data preparation, regression, and scoring—on the widest range of data warehouses and other platforms, your tools should support in-database or embedded analytics. And to scale to the max, your predictive analytics tools should deploy models to massively parallel data warehouses, software-as-a-service environments, and cloud computing fabrics. Your advanced analytics tools should also support development of application logic in open frameworks—such as MapReduce and Hadoop—to enable convergence of data mining and content analytics in the cloud.
-Rich interactive visualization: To deliver their precious payload—actionable intelligence—your advanced analytics tools should support interactive visualization of models, data, and results."

James concluded the article with the following statement: "We see a robust, flexible, SOA-enabled data mining tools as the centerpiece of advanced analytics for fully predictive enterprises. The competitive stakes are too great for businesses to take the traditional silo-mired approach when implementing this mission-critical technology."

James Kobielus wrote a good article, the predictive analytics is increasingly becoming important for help the organizations to make better decisions.

Does Your Operational BI Integrate Predictive Analytics?

David Stodder wrote a post in the Ventana's blog, called Does Your Operational BI Integrate Predictive Analytics? In the post, he commented that in the Ventana benchmark research on Operational Business Intelligence Trends, they found that a large number of organizations are deploying technology to enable better decisions and actions by front-line workers and operational managers.

He said: "Organizations should consider whether they need to supplement BI with predictive analytics: that is, statistical and data mining tools for intelligently monitoring processes and implementing predictive models to guide response to events. To manage operations, many organizations are already deploying a range of sometimes overlapping technologies, including business process management, ERP, workflow, business activity monitoring and business rules management."

He considers that analytics are most often not presented in operational BI dashboards, but the technology vendors are beginning to address this problem. He commented about the new release of TIBCO Spotfire bring together the Spotfire analytics and visualization products with the predictive analytics tools gained through its acquisition of Insightful. For him, this sort of integration is an important development; organizations should evaluate how well their vendors are integrating predictive analytics with analytics and BI to help them achieve the most optimum outcomes.

Predictive Analytics: The BI Crystal Ball

The Aberdeen Group investigated recently the predictive analytic capabilities through a survey research program, called Predictive Analytics: The BI Crystal Ball, that uncovers the strategies, actions, technology investments, and services that Best-in-Class companies are utilizing to improve performance through gaining predictive knowledge about their business. This study was based on survey responses from over 280 organizations.

Reality Mining: Predicting Where You’ll Go and What You’ll Like

The New York Times published an interesting news today, in its section of technology, about a predictive analytics tool, called Macrosense, released earlier this month by Sense Networks, a software analytics company based in New York City.

According Sense Networks: "Macrosense is the world's first platform capable of collecting and analyzing massive amounts of anonymous, aggregate location data in real-time" and "Macrosense applies complex statistical algorithms to sift through the growing heaps of data about location and to make predictions or recommendations on various questions — where a company should put its next store, for example".

The Key Features of Macrosense are: Real-Time Activity Analysis, Powerful Analytics, Historical Data Normalization, Contextual Data Inputs,and Flexible Interfaces and Visualizations.

Sandy Pentland,co-founder of Sense Networks, said that Macrosense tool lets companies engage in “reality mining”, term coined by her.

Reality mining raises questions about privacy, but according the company, it is interested only in aggregate data and that it’s looking for broad patterns, not the specific behavior of individuals.

Sense Networks also announced another tool, called Citysense, defined by them as: an innovative mobile application for local nightlife discovery and social navigation, answering the question, "Where is everybody?" Citysense shows the overall activity level of the city, top activity hotspots, and places with unexpectedly high activity, all in real-time. Then it links to Yelp and Google to show what venues are operating at those locations.

They are testing Citysense in the city of San Francisco, California; and it is currently available on BlackBerry devices and will be released for the Apple iPhone soon.

I think the use of predictive analytics will allow several kind of applications like that, and that is just beginning.

Business Objects announces predictive analytics tool

Business Objects announced a couple of days ago, its predictive analytics tool, called Predictive Workbench, a module integrated into Business Objects X1 3.0, latest version of its BI Platform.

Predictive Workbench is the result of an OEM deal, where Business Objects is using the technology of SPSS, called Clementine. SPSS is a company specialized in predictive analysis and data mining.

According Business Objects,the key features are:
- Uncover trends and patterns to reach organizational goals
- Forecast and anticipate future business conditions
- Use predictive analytics with innovative and proven BI capabilities

Currently, the segment of predictive analytics is widely dominated by SAS, but IBM/Cognos and SAP/BO are investing heavily in this direction. IBM/Cognos also has the same kind of deal with SPSS, and it is probable that briefly will integrate a predictive analytics tool into Cognos suite.

Adaptive Decision Management

Last week, Paul Haley wrote in his blog, an interesting article, entitled Adaptive Decision Management, where he describes his ideas about why he thinks that the future of predictive analytics in decision management is through the Adaptive Decision Management (ADM).

That is a good explanation about those interesting subjects.