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Information Analytics: A Great Convergence is Underway

What, above anything else, has been the greatest deterrent, the most obstinate obstacle to the practice of knowledge management? It’s been figuring out ways to sift through all the repositories, all the data streams, all the documents, and all the communications out there in order to identify and deliver the nuggets of information meaningful to the decision-maker at the moment he or she needs it.

Lately, the practice of knowledge management has been converging, in a big way, with the data management side of the house. That’s because unstructured data—which has long been in the KM domain—has become a highly sought-after piece of business intelligence, and data professionals have been keen on roping in documents, emails, graphics, and everything else into their emerging data-driven worlds. The result is a sharing of enhanced analytic capabilities for both data analysts and knowledge management professionals, or better and faster ways to finally separate the wheat from the chaff.

Let’s face it, both KM and database professionals have been fighting the same battles for years. Both have been looking for more effective ways to identify new data sources outside and inside their corporate walls. Both have been struggling with opening up silos of information across their organizations. Both have been seeking ways to better capture and digitize the knowledge that engineers, executives, salespeople, and everyone else is carrying around in their heads. While all this has been going on, almost paradoxically, both have been challenged with containing the resulting flood of information, to help decision-makers quickly get to the exact nuggets of information they want.

A recent survey we conducted among 483 executives and managers who are subscribers to KMWorld found just that: Big data is as much a part of the world of KM professionals as it is for database professionals. A majority of KM managers and professionals, 57%, report they are challenged, to some degree, with addressing the requirements of managing big datasets (large volume, variety of formats, rapid velocity) at this time. However, they point out that there needs to be more collaboration between KM professionals and those working with big data technologies. Twenty percent say these two sides do not collaborate at all, and another 25% are simply not aware of what collaborative efforts may be underway. Another 35% say there is some level of collaboration between KM and data teams. In 14% of the organizations, the KM and data teams are actually one and the same.

With greater convergence comes even greater sharing of technologies and methodologies. For some time, the key technology initiative for knowledge management professionals has been text analytics, or the ability to sift through and analyze unstructured text to transform it into actionable business insights. With increasing convergence with the data analytics-driven culture, text analytics is evolving as a more powerful, more responsive, more automated, and broader category of information analytics. While traditional text analytics often was collections of manual or one-off types of endeavors, the next generation of information analytics employs linguistic, statistical, and machine learning techniques to rapidly deliver insights to decision-makers, and many front-end tools encourage a self-service approach. Much of this is available from the cloud. All of this is being imported from the data analytics side.

What’s important to keep in mind here, however, is that this new information analytics world we see emerging is more than simply throwing technology into the mix. The human and organizational aspects are key to ensuring success going forward. Information analytics require leadership from above, as well as a spirit of collaboration between KM and data teams.

Here are some ways to promote this convergence:

Start with the end user in mind. Talk to the ultimate consumers of your analytic output. As you map out what they are looking for, develop recommendations for appropriate technology solutions. Remember: business problem first, technology later.

Make analytics a top corporate priority. A transformation to an analytics-driven organization—for both knowledge and data—not only requires top executive sponsorship, but also a vision. Analytics is the key to new product creation, enhanced customer engagement, and greater overall performance. Proponents can’t simply talk about “analytics” and statistics, however—it all needs to be woven into a story that demonstrates how the business can move forward in today’s hyper-competitive economy.

Encourage collaboration. Building an analytics-driven culture is easier said than done, of course. But this should be the ultimate goal of analytics efforts. A good place to start is to initiate collaborative efforts between KM and data teams. By working together, KM and data leaders can build a compelling case for analytics across a range of business areas. Start on smaller projects to demonstrate its potential to the rest of the organization.

Think about motivation and incentives. You want your approaches and solutions to be adopted as widely as possible within your enterprise. What will it take to achieve that? Are end-user decision-makers aware of the KM resources available? How can you work more closely with the data management side to “sell” information analytics to management? Are there incentives that can help, whether related to professional development, or financial rewards tied to innovative use of analytics?

Measure results, at many levels. To get the attention of corporate leaders, it’s important to show measurable results. Tie information analytics efforts to formal key performance indicators (KPIs) to help focus analytics efforts on the right areas. “Soft” measures—such as end-user satisfaction with the resources available—are also important as well. The key is that decision-makers see how the availability of analytics to capture and spotlight organizational knowledge and data is making their jobs better, as well as increasing overall business performance.

We are at the beginning of a significant convergence of organizational talent and resources, which is now being brought to bear on the challenge of capturing and delivering all the intelligence that flows through organizations. The rise of knowledge about knowledge, and data about data, represents an exciting new frontier.

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