Consultants and analysts expect to see increasing numbers of companies across many industries using analytics to derive value from their unstructured information. That’s not surprising, given the approach’s many proven and potential benefits.

But experts also warn that when it comes to combining analytics and dynamic case management (DCM), it’s important to start with the right strategy.

Successfully implementing DCM and analytics requires addressing the following seven issues, according to Hurwitz & Associates, a strategy consulting, market research and analyst company:

1. Identify the business issues that you want to tackle. Consider where analytics can be most helpful to DCM and determine which business problems you’d most like to address.

Fern Halper, a partner at Hurwitz & Associates, recommends zeroing in on your most serious pain points to gain the maximum benefit from positive results: “Pick one with a high profile so you can showcase success,” she says.

2. State your requirements. Given the growing number of vendors and the wide variety of approaches in the analytics space, it’s smart to develop a clear “problem statement” to help vendors pinpoint where they can best add value for you.

Along the same lines, make sure vendor demonstrations are tailored to your specific needs, Halper advises. For example, different vendors handle different types of information or documents in different ways. For each demo, try processing samples that reflect your organization’s actual work requirement. Also try to determine whether a particular solution will require customization and, if so, how much.

But beware of showmanship, Halper warns. Rather than being seduced by technology, study the results and weigh all your options before making your final choice.

3. Figure out the potential deployment challenges. When deploying analytics in a case management environment, you’ll need to determine how to:

o Access and pre-process the data

o Merge structured and unstructured information

o Store the data

You should also determine whether you’ll need any modifications and, if so, whether your system is flexible or extensible enough to accommodate those needs. Other considerations include deploying taxonomy, determining the amount of information to be processed and evaluating how well a particular vendor solution meets your performance needs.

Finally, consider scalability. Keep in mind that pilot project may not, by itself, tell you how well the system will perform for you in a full-scale deployment.

4. Budget for possible consulting costs. Some deployments may be simple, straightforward and textbook perfect. But Halper points out that many vendors in this space offer consulting services, which she calls an indication that implementation isn’t necessarily easy. In narrowing the field, consider each vendor’s total offering, pricing and partnerships.

5. Seek solutions tailored to your industry. It makes sense to look for technology solutions designed for your particular niche rather than having to customize products (or build them from scratch). Look for vendors with strong experience in your industry; explore whether they offer technology that’s a good fit for your needs.

6. Assess your organization’s existing skills. Successfully using analytics in DCM usually requires investing in training, Halper warns. And that involves more than just acquiring some new standalone technology skills; users must understand how to look at information in terms of context and meaning. No matter how user-friendly a platform might be, it can’t overcome a shortfall of sophisticated expertise. Conversely, the right people with the right skills can help harness the analytics and empower DCM users with additional knowledge and functionality.

7. Prepare for cultural change. As with any technology initiative, you should seek buy-in from both executives and users. Make sure employees understand how analytics will help them do their jobs better—and help the business overall.

One other caveat: Vendors often describe even advanced analytics technology as increasingly easier to use--but that doesn’t mean it’s mistake-proof. “I don’t think people should have a false sense of security about it,” Halper warns. For example, even with the most user-friendly solution won’t serve you well if you misinterpret the results or rely on data that’s of poor quality or not well-integrated.

But if you do the job right, you’ll solve some high-profile business problems—and others are likely to notice and want to know how you did it. And that may be the biggest benefit of all, Halper says: “That is how innovation is propagated within and between companies.”

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