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The Face of Analytic Data Access

  • April 14 2008, 11:38am EDT

The following is excerpted from the William McKnight white paper “Modernizing and Advancing Information Management across the Enterprise.”

The face of analytic data access is changing, whether it occurs in operations or with a batch-loaded data warehouse. Investments in the online analytical processing paradigm give way to embedded and machine-driven forms of business intelligence (BI), allowing business analysts to accede to higher functions of the business. Information leadership drives power to the business and the programmer.

However, many more organizational strategies and people within those organizations are coming online with more complete utilization of information. Increasingly, users comprise diverse business interests and perspectives relative to the company business, such as vendors, supply chain partners and customers. Advanced analytics will not happen by rolling out the same reports to these nontraditional users. Users demand customization under their control, expect user-friendly, Google-like access to information and require a wider range of analytical styles.

BI tools are not as easy to use, or as interesting, as the BI community tends to think. This is partly why the bulk of analytic access work continues to take place in spreadsheets. However, users repeatedly show that when given simple tools to perform useful functions, they utilize the tools. Information leadership in an organization must go beyond making the raw data available. Front-line users need the data to graphically and visually fit their skills and preferred delivery mechanisms, which are increasingly wireless and exception-based.

Getting the right information into operations can mean utilizing the data warehouse to collect detail and process, and summarize and feed selective results to the operational environment for utilization in the real-time environment (see Figure 1). The necessary latency between a data warehouse’s batch load and the batch process that occurs on the data before feeding it back to the operational systems usually means this data arrives the next day. For example, the contact center operator updates a customer profile with whatever new segments the customer belongs to based on the day’s activities. However, she typically is working with day-old activities, so the segmenting lags behind the real-time environment. Consequently, more shops are beginning to cleanse, lightly integrate and hold information operationally and do the processes and summarization necessary on the spot in the operational environment. However, limitations on data volume and processing cycles in operational environments continue to force some operational workloads into the data warehouse. If highly processed, the data warehouse may share its data back to operations as XML or formatted HTML. It’s almost as if the data warehouse becomes decision-support middleware when performing this function.

For analysts, dashboards and portals are a better step in the right direction than reports, and they can be placed in either the operational or data warehouse environment. The technology is largely unaffected. Perhaps the trend best reflecting the required interface comes in the many forms of enterprise search that are manifesting themselves in toolsets. Enterprise search provides an extensive body of data for the search - at best, all corporate data - giving rise to data virtualization. The search mechanisms are also simplified. In some cases, from few keywords entered, formerly complex queries can be assembled.

This model of self-sufficiency is also evident in the area of data mining, which has long been the domain of experts. The mining process currently deployed in many organizations is not only time-consuming due to the challenge of the tools and the semantic gap between the front line and the statisticians, it is also noniterative in nature. Discovered nuggets are only selectively interesting and actionable. Mining tools that are interactive, visual, understandable, well-performing and work directly on the data warehouse or mart of the organization can be used by front-line workers for immediate and lasting business benefit. If advanced analysis is reserved for the few instead of the masses, the full value of data mining in the organization cannot be realized. For those with average analytical capabilities, mining is not nearly as effective as it could be. However, numerous accessible mining techniques are more effective than most, simply because they are used by so many within an organization. With little investment, these techniques can draw attention to significant anomalies that deserve further investigation.

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