Business intelligence (BI) sits at the top of the IT priority list for many enterprises. Enterprises that haven’t paid enough attention now see a need to act, and those that have kept up with BI want to consolidate their siloed implementations.
The promises of BI attract any organization, but how do you get started? Enterprises […]
Business intelligence (BI) sits at the top of the IT priority list for many enterprises. Enterprises that haven’t paid enough attention now see a need to act, and those that have kept up with BI want to consolidate their siloed implementations.
The promises of BI attract any organization, but how do you get started? Enterprises face multidimensional choices, and they cannot start with vendor selection. Tasks such as data governance, matching requirements with logical architectures and picking an experienced architect and implementer should be at the top of the list.
Convergence of structured and unstructured content analyses:
Mability to detect patterns and run what-if scenarios. Consider retail customer segmentation: The old wayodern analytics blends unstructured data with traditional structured data to give users the meant combining customer sales with customer and store demographics.
Today retailers on the cutting edge realize that adding comments and complaints from email and call centers will significantly enhance their segmentation analysis. You could always pore through text manually and code it along criteria you developed, though few ever do because of the time and effort it requires. BI calls for these connections to be automated, so analysts can focus on turning insight into action, instead of hunting through multiple mail systems, phone systems and enterprise applications
Combining data with process awareness.
BI and business process management (BPM) have always addressed a common need separately, bringing people and information into alignment. The operational improvement cycle – learn, design, inform, act and repeat – focuses on increasing efficiency.
But organizations with ambitious strategic goals need to be more than efficient – they need to be effective. BPM might make processing a customer credit application less expensive, but analytics can use sophisticated customer segmentation to increase cross-sell and up-sell ratios in real time during a customer interaction – when it counts.
Expect solutions to combine data and process dashboards, event-based actions triggered by data conditions that initiate a business process, and traditional BI layers (reports, dashboards and analytics) responding seamlessly to business processes across multiple systems.
Entry of relational database alternatives.
The relational database management system (RDBMS) was originally designed to execute small transactions, not to examine large volumes of data with BI queries.
Over the years the technology has caught up, but RDBMSes still have to shoehorn two personalities into one body. Alternate DBMS models will increasingly enable BI for two big reasons: 1) removing the bias between structured and unstructured data, and 2) OLAP query processing.
Explosion of dimensions to support future BI analysis.
Traditional cross-tabular reporting quickly becomes useless after more than a few dozen dimensions, no matter how sophisticated the “slicing and dicing” interfaces are.
One possible approach is so-called “guided analytics,” where users can rapidly mix and match dimensions interactively. Another is visualizing patterns graphically, giving users a big-picture view of extremely large data matrices for identifying trends.
Figure 1: How to Choose a Vendor Category
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