Data warehouse layers and OLAP cubes still have a place with enterprise data, though one Gartner analyst says precision, personalization and collaboration will propel the coming wave of analytic capabilities.
Kurt Schlegel, analytics research VP for Gartner, led a presentation Tuesday entitled “The Future of Business Analytics.” With a nod to the history and existing use of analytics in business—and none of the dire calls to kill off EDW or existing data systems—Schlegel outlined the direction of information in the coming years.
For more precise analytics, Schlegel says data architects should look to augment rather than replace existing operations with an eye toward agility and acceptance of external and varied data sources. Here, Schlegel sees plenty of more acceptance in data discovery tools, especially where there is little training required and as big data frameworks become more visually appealing. There is also an opportunity for industry-specific, trusted data aggregators to fill in enterprise data gaps with as-a-service options. Schlegel says existing providers of benchmarking and industry analysis are putting together niche offerings that provide the content that many enterprises may not be able to for lack of in-house talent, budget or existing data resources.
“More and more, analytics services will deliver … easy to consume analytic applications in the cloud that will be very powerful,” he says.
Building atop the traditional OLAP methodology, enterprises should also expect more customization and personalization of information segmentations. To this end, coming analytic capabilities will be geared toward high-volume forecasting, a greater possibility with more data streams. With that, analytic processing needs a more granular edge to avoid broad strokes with information that loses customization possibilities for both end users and their customers.
The third area of business analytics evolution discussed by Schlegel was the most opaque: decision management support. In part “resurrecting the term decision support,” Schlegel said automation and collaboration of the data behind this aspect of business is reserved for the leading edge of analytic maturity for the time being. However, as collaborative applications and semantic layers grow, the analyst says that there is promise for a boom in analytics to handle these typically manual yet critical business decisions. Schlegel gave the example of the buy-in going on in the insurance industry space when it comes to claims processing, a manual but repeated process where brainstorming over systems is showing promise to provide personalized, real-time responses.
In preparation for these analytics changes, Schlegel recommended:
* Map out pressing business problems and create a “finite list” of analytical enterprise teams.
* Within three months: create data discovery tool prototypes to address those business problems and subscribe an analytic industry service relevant to your business area.
* Within 12 months: assemble a team to tackle high-volume forecasting and granular segmentation. Then, roll out a support solution.
To register for a re-broadcast of this discussion, visit Gartner Research.
This story originally appeared at Information Management.
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