Have you ever been at an organization that spent loads of time and money collecting and analyzing data without ever seeing the return you expected?
The unfortunate fact is that 60 percent of data and analytics projects fail to meet their objectives. Part of the problem is that we can now track just about anything, which has caused our appetite for data to grow exponentially — often beyond what our companies’ data and analytics teams can handle.
Too often, talented people with the right tools can’t create meaningful outcomes due to a company’s cultural or organizational challenges.
Here are some of the 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.
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 silos lead to a lack of transparency around who is producing data, what data is being used, and how it is applied. Over time, this can make business leaders doubt the accuracy of their own company's information.
In this environment, employees often try to satisfy their own data needs outside of the company’s defined channels, which only worsens the problem by creating more and more internal customers for the centralized data analytics team.
The opposite approach is needed: In the face of the growing demand for data, firms must organize their data and analytics teams to reflect big-picture goals. Data resources should be assigned based on the company’s strategic and operational needs rather than the frequently narrow requests of individuals.
Forming the Analytics Dream Team
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 transparent, product-centric workflow that manages resource demand and delivers on outcomes.
Mapping your data resources to business processes will help your company get the most out of its people. It’s also an eye-opening experience for many, revealing the shared needs across departments. Arranging your company 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 datamarts.
These newly formed “analytic 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 to all of this 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 (EDM) and IT hubs.
The centers are also a resource to individual departments and teams, relaying their needs to EDM. This arrangement allows the data and analytics centers to filter through mountains of requests to find out what truly matters to the organization.
The bottom line
Remember, 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.