7 Elements of a Data Strategy
Even as companies make larger investments in data and analytics initiatives than ever before, age-old obstacles like siloed and untrustworthy data, inefficient data management practices, and a lack of meaningful insights continue to get in the way of unlocking your data’s potential.
A good data strategy framework is proven to help companies overcome those obstacles and define the path to become more data driven.
In this blog, we discuss the key components of a data strategy, including:
A data strategy is the foundation to all your data practices. It’s not a patch job for your data problems, and it addresses more than just data—it’s a long-term, guiding plan that defines the people, processes, and technology necessary to solve your data challenges and support your business goals.
Creating a successful data strategy requires business leaders to take a deliberate—and objective—look at the business through the lens of data and anticipate what needs to happen to bring about specific objectives the company has defined. Business leaders should consider:
It’s not enough to just have data—you need a strategy in place to realize your data’s value and to bring to bear meaningful outcomes aligned with your business goals. A data strategy enables your organization to be innovative, business users to be effective, and the business to be competitive. Without a data strategy in place, you can encounter common data challenges including:
We’ve helped hundreds of organizationswith varying levels of analytical maturity and technical needs craft their data strategy and make better use of data. Our extensive experience has resulted in identifying the following key components of a data strategy.
Data initiatives must address specific business needs to generate real value—otherwise, you risk prioritizing the wrong projects, missed insights, wasted time and resources, and even loss of interest and faith in data initiatives throughout the organization.
Tying your data strategy to your business strategy sets you up for success. When your data initiatives support company goals, you get business buy-in—which means more prioritization of data activities—and the whole organization wins.
Here are ways to align your data strategy with your business strategy:
With this information documented, you can begin to build a log of use cases that will be included in your data strategy roadmap (see Tip #6!).
You need to know your starting point—your current analytics maturity level—before outlining your desired future state. This helps you set attainable goals and take realistic, incremental steps to become more data driven.
According to Gartner, modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive. Here’s how, when, and why you should use each.
To get a full picture of your analytics and data maturity, you need:
With an understanding of your current state, you can identify where you have gaps, where there are known issues, and what you need to optimize—whether it be technology, processes, people, or all—to meet business objectives across the organization.
Your data and analytics maturity level is a tool to prioritize your projects and serves as a benchmark to measure progress as you increase capabilities and perform tasks from your data strategy.
It’s easy to get caught up in the hype and latest technologies and have the inclination to want to choose the “newest” tool in the market. It’s also easy to get overwhelmed by the ever-growing number of choices and decide to stick with what you have or take a single-vendor approach.
There are effective ways to cut through the noise of the market and choose technology that works best for your situation:
When choosing your tools and technology, remember that they are not standalone components, but rather integrated parts of your data architecture.
