In 1989, Howard Dresner invented the term "business intelligence". In 1992, Dresner joined Gartner, Inc. where he built up the analyst firm's BI practice and created and chaired the annual Gartner Business Intelligence Summit. In the fall of 2005, Dresner lbecame Hyperion's (a BI and performance management vendor) Chief Strategy Officer. He spoke with SearchDataManagement.com last year.
Q:What's the difference between BI and analytics?
A:I see analytics as simply applied business intelligence. Sometimes it's very simple, sometimes it's very sophisticated. But effectively, what you're doing is providing some level of analysis, typically within the context of an application.
My vision of business intelligence was to establish what I call
What I talk about today is literally aligning people and process with purpose. The next high ground is business performance management (BPM) [also called corporate performance management], which is what BI becomes when it grows up. Or, as I like to call it, BI with a purpose.
Q:Has BI evolved as you expected?
A:A lot of good things have happened but we haven't made the progress that I would have hoped. The good news is that the technology has advanced tremendously. It's just so much more usable and so much more sophisticated -- all of the things you would hope. The current generation of products are much more evolved from where we were. And of course all of the infrastructure -- the hardware, the networks -- have just gotten much better from where we were 15 years ago. Then, a lot of this would have been fundamentally impossible. You couldn't have scaled BI to entire organizations. Today, technologically and architecturally, you can scale BI to every user constituent. That would not have been possible ten, even five years ago.
Q:Are there practical ways for IT to communicate with business people about BI?
A:There needs to be a knowledge transfer between the two functions, hence the notion of a competency center. You take [IT and business people] out of their environment and you start getting them in an environment where they're now peers. Unless you can forge that sort of relationship between IT and the business, you never break the cycle.
Read the full interview at SearchDataManagement.com.
In this excerpt, industry expert Wayne Eckerson reviews the benefits of embedded analytics and how they will take BI to the next level.
Embedded analytics: The next wave in business intelligence
For BI to penetrate deeper into organizations and deliver strategic benefits, BI tools must be easier to use and provide insights into business events as they happen. To simplify BI tools, vendors are beginning to offer dashboards and scorecards with role-based views of key metrics, natural language queries and, most recently, Google-like searches of corporate data. Yet, more needs to be done.
On the data side, organizations are moving to adopt "operational BI." They are modifying classic batch-oriented data warehouses with event-driven processing models so they can deliver real-time information to users. They also use federated query capabilities, Web services and service-oriented architectures to cull operational data in real time from disparate systems.
The promise of embedded analytics
In the end, the best way to simplify and operationalize BI is to embed it directly into operational applications and processes that drive the business. This is the definition of embedded analytics.
Embedded analytics are nothing new. Organizations have embedded BI functionality into applications and business processes for years. For example, Java and .NET developers often create reporting capabilities from scratch when building custom applications. Portals display charts and tables generated by reporting tools inside portal windows, or "portlets." Microsoft Office applications maintain live links to reports stored on BI servers. Extranet applications integrate with BI engines that let online customers view and analyze personal accounts and activity. Online applications embed predictive models that score customer transactions in real time to detect fraud, cross-sell products, evaluate risk or assess credit worthiness.
However, most embedded analytics to date have barely scratched the surface of what is possible. Usually, the analytics are fairly primitive, displaying canned views of existing reports with little ability to drill down, publish views in other formats, or compare with other data. The more compelling analytically driven applications are implemented by leading-edge companies with deep pockets and legions of skilled developers who code, debug and test monolithic applications that are time-consuming to build and costly to modify.Read the entire column at SearchCIO.com
This was first published in April 2007