How to unlock enterprise-wide value from data and analytics
Does your healthcare organization spend loads of time and money collecting and analyzing data without ever seeing the expected return?
Some 60 percent of data and analytics projects fail to meet their objectives. Part of the problem is that you can now track just about anything, which has caused our appetite for data to grow exponentially—often beyond what enterprise organization’s data and analytics teams can handle. Too often, talented people with the right tools can’t create meaningful outcomes because of cultural or organizational challenges.
Here are some telltale signs that your data resources are being wasted.
- Road to nowhere: When data and analytics teams are seen as order-takers, it can lead to a one-way stream of requests that overload resources and don’t reflect strategic needs.
- Garbage in: A lack of standards around how data requests are made leads to disorder and inefficiency.
- Static data in a dynamic world: Data is treated as a retrospective recording of historical measurements with little ability to draw insights or solve problems.
- Data distrust: Data silos lead to a lack of transparency around who is producing data, what data is actually being used and how they’re doing it. Over time, this can make business leaders start to doubt the accuracy of their own organization’s information.
In this environment, employees often try to satisfy their own data needs outside the company’s defined channels, which worsens the problem by creating more internal customers for the centralized data analytics team.
With growing demand for data, you need to organize your data and analytics teams to reflect big-picture goals. Data resources should be assigned based on your organization’s strategic and operational needs rather than the frequently narrow requests of individuals. The goal is to become an organization where data and analytics partner with the business to create value over the long term.
Your business objectives should drive any and all decisions you make toward organizing data and analytics teams. Data is not the end but rather the means to support the broader strategy.
The long road toward organizing your data and analytics strategy can be simplified as a three-step process.
- Organize your analytics resources around business processes.
- Put money behind products that will help the whole enterprise.
- Build a product-centric workflow that is transparent, manages the demand of data resources, and delivers on outcomes.
Mapping your data resources to business processes will help your organization get the most out of its people. It’s also an eye-opening experience for many, revealing the shared needs across departments. Arranging your organization in this way also reduces waste in the form of redundant data reporting. Your people will also have more time to generate insights and spend less time and effort curating their own data marts.
These newly formed “analytics centers” subsequently govern the demand and prioritization of analytic products and can help to assess what the major data needs of the organization are. A side benefit is that your data and analytics teams will be empowered. Rather than fielding requests, they’ll start working on products that help the company succeed.
Developing a long-term product roadmap for your data needs also requires someone to build consensus. The analytics product manager serves a critical role here, understanding the business objectives and translating them for technical teams.
When analytics centers are enabled, a company will see better return on their investment, as well as more manageable demand on their data and IT resources — without the overflow of one-off and redundant requests. The point isn’t to create a totally centralized data and analytics process. Rather, these analytics centers serve as spokes to the company’s enterprise data management and IT hubs.
The centers are also a resource to individual departments and teams, relaying their needs to EDM. This arrangement enables the data and analytics centers to filter through mountains of requests to find out what truly matters to the organization.
Spending more isn’t the answer. Start by identifying the strategic aim of data, organizing analytics resources around them and building products that add lasting value.