In my last post, I wrote about the first three steps in transforming your information management strategy from one focused on data collection and analysis to an environment designed to have a positive impact on the bottom line. In this article, I’ll discuss the final two stages and outline some considerations for optimizing their impact.
To recap, organizations must first focus on data capture, data quality and integration and data enrichment. Once these steps have been addressed, companies can turn their attention to the next stages along the path:
Once data units are established, organizations can begin to extract insights on what has happened in the past and what is possible moving forward. Analysis may reveal trends that can be capitalized on, bring to light hidden costs, or reveal new sales and revenue opportunities. Increasingly, organizations use data enrichment to understand why things happen or why consumers behave in certain ways, and with these insights, they create new revenue streams.
There are a number of considerations that must be made along the way. A company has to decide on the type of analytics they want to go after: descriptive, diagnostic, predictive and prescriptive. It also must decide on the presentation layer of analytics, how it will be communicated to end users: via a written report, interactive dashboard, analytical visualization, info application, etc.—or some combination of these. One type of presentation does not fit all use cases. Finally, the delivery vehicle for that information is important—will it find its way to the user through an online portal, a third party application, embedded in a third party application or some other way? Each of these three dimensions of analytics are critical parts of the planning process, as they affect how efficiently analytics can be used in decision-making.
With analysis complete, you now have a data asset, which has the potential to be monetized. The insights—or “aha!” moments—derived from it point toward how those assets can be properly utilized and allow organizations to discover new opportunities, but by itself, insight is not sufficient for monetization. The opportunity that is illuminated needs to make its way into the market somehow, either by operationalizing it or making it available to external parties.
Support operational decision making – The first way to monetize data and analytics is via internal, easy-to-use apps that support decision making within the organization. Those apps are intended to drive fact-based decision making, in turn, changing employee behavior and driving higher levels of performance. These self-service apps are drastically different from traditional business intelligence software. The goal is not to enable employees to perform analysis themselves, but rather to provide factual answers to business questions quickly, based on the user’s role. This is why all the analysis has to be built into the app.
Consider Expedia.com; the site democratized travel booking by allowing its customers to acquire information about trips and prices quickly and without any need for training. This is a self-service model that others would be wise to imitate. Most organizations do not know how to leverage the insights they’re procuring to build an operational application, and thus are missing out on a significant opportunity.
Put data in customers’ hands – Packaging information as a consumer product is the second way to monetize data. We call this the consumerization of analytics. To do this though, organizations must make exceptional ease of use a priority.
Some organizations distribute great volumes of data to consumers in static PDF documents (like a financial statement), but in that format the consumer is powerless to do pretty much anything with the data. This is another missed opportunity. Providing in-document analytics that allow customers to filter information or easily manipulate the data empowers the consumer to make better decisions about their personal business, which in turn creates more revenue opportunities for the organization. For example, when utility companies distribute interactive statements, their customers can make “what if” analyses and decide whether to buy smart meters.
Now that we’ve covered the operational side of the Data Value Chain, let’s briefly address culture. The transition from data collection to monetization also requires a cultural change; this may be difficult, but it’s crucial to the success of an organization-wide initiative. Think, for example, of the omnipresence of taxi companies up until a few years ago. Major taxi companies have dispatch software in place to map the location of their cars, but they didn’t think to use this data in innovative ways—yet Uber did. It was an industry outsider—a startup with zero cars, but with a culture that promoted the value of data.
So, are you ready to turn your organization’s crude data into a monetizable business asset as well? By following the Data Value Chain approach, you can ensure that data projects are seen all the way through to monetization, without simply ending at the insights step.
If you don’t act on insights you glean from analysis, and monetize data, other companies will. It takes a methodology like this—and an organization-wide commitment to it—to remain ahead of the competition.
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