What's Outcome-Based Data Management?

Companies don't gain business value just by gathering lots of data. They don't even necessarily gain value from analyzing the data. The true keys to success are choosing the right data to focus on, knowing what to do with it, and determining the best ways to apply analytics to solve business problems or address market opportunities.

To achieve all of this and get the most out of their information resources, enterprises need to create a coherent data management strategy that is designed to deliver data in ways that will actually impact business outcomes. Here's why.

Finding the Needle in the Haystack

These days, data is coming into organizations from so many sources and channels and in such enormous volumes that there needs to be a strategy in place to make the best use of the information. Without a plan, there's no efficient way to figure out what kinds of data you're going to use and how will it benefit your business. A key point of that is understanding what business outcomes the data can potentially deliver.

For example, let's say a software company is looking to build the best applications it possibly can: a sensible goal for any software company. When that company gathers usage data from current and prospective customers, it will want data that can help them decide which features to build into the next release and which features to retire. Once the product is released, the company will want the data to reveal whether it is building product features correctly or needs to tweak something. Are people using the new features and are they delivering the expected value?

Ideally, by gathering and analyzing its incoming data, the software company will receive definitive answers to these questions. For this company, and any type of business, it's about identifying the opportunity and then making sure to actually capture the right kinds of data.

Armed with this data, this company can now actually know whether they are building good features into their products or whether they need to go back and rebuild. They might also find out that most users don't even care about particular features-which can also be a useful insight.

A Lot of Data vs. The Right Data

I'd venture to guess that most organizations would find that they are either not collecting the right data, or not collecting it in the right format if they did some self-analysis. Maybe they're not digging deep enough to get to the insights they need from the data in order to make better decisions.

The problem is not that companies don't have the ability to collect data. In fact, many are overwhelmed by the amount of data they have. But this data and the analytics applied to it, unfortunately, do not help people make better decisions; it's not information that people can act on to bring value to the organization or its customers.

The good news is this can all be fixed via a solid, outcome-based data management strategy that takes into account what kind of data and analytics are needed by particular users; and how those users will act differently once they have the data so that they can deliver more value.

Even though this is about data, organizations should not make the mistake of assuming that the IT or analytics teams should take the lead in developing and maintaining the data strategy. They need to involve individual business users, whether they are in marketing, product development, customer service, or some other area of the business.

After all, these are the people who know the data best and will be enabled by it. They're the ones who are at the point of decision and they need to be able to use the data within their world in order to make those decisions. So, one of the most important things that enterprises need to do is identify the decision points within the organization and enable them to use the data they need to affect the desired change.

A lot of business users might not be analytically minded by nature. For that reason, investing in training so those people can actually use the data is a key piece of the data strategy.

By determining what kinds of data need to go to which users and in what format — and by preparing those users to best leverage the information-organizations can truly gain the business value that their data is meant to deliver.