There’s quite a difference between an organization that uses data and one that’s data driven. Driving business outcomes and growth with data insights requires a number of conditions to be in place: having data, bridging the distance between disparate sources, investing in data analytics software, getting employees to actually use the tools available to them and ensuring decision-makers actually make sound judgments based on the data insights they glean.

Difficulty with any part of that process can spell serious trouble for organizations in their efforts to harness data to improve how they operate. It’s like removing a gear from a watch; though it seems like just one component of many, its absence affects the movement of everything else necessary for keeping time.

Here are four hurdles enterprises must clear to harness data-driven decision making in the workplace.

Hurdle #1: Lack of Leadership Buy-In

As two management experts define for Harvard Business Review, two key things happen in companies with strong data cultures: Employees use data to inform important decisions and executives act on analytics rather than intuition and experience alone. It may sound straightforward, but getting buy-in from leadership on the ground floor often presents a challenge — particularly in more traditional companies trying to become data-driven rather than data-native organizations.

Leaders need to do more than sign off on investments in data analytics initiatives; they need to lead by example, showing everyone else within the company what it looks like to engage in data-driven decision-making. Leaders throughout the company should also encourage employees to back up their assertions with data, which helps get everyone in the habit of turning to data.

Another thing leaders can do to foster a data-friendly culture is reward employees for analyzing and acting on data — and celebrating wins in a way that makes everyone want to do the same.

Hurdle #2: Siloed, Disparate Data Sources

The more immediately and intuitively employees can access data insights, the more likely they are to actually do so. This is why siloed and incongruent data sources are a major barrier to becoming data-driven; they discourage employees from getting the information they need.

Plus, there’s the fact employees tend to be distrustful anytime multiple versions of the truth are floating around. If the marketing manager draws on one data source and the sales lead

draws on another source with different numbers for the same metrics and time period, people are only right to be distrustful of the information they’re receiving.

The latest wave of data analytics aims to bridge the gap between siloed and disparate data sources, pulling them all into one single version of the truth. This way, employees — regardless of title or analytics experience — can use a common interface to query billions of rows of data across multiple sources in seconds.

Hurdle #3: Legacy Data Analytics Tools

In the same vein, legacy data analytics tools can impede efforts to become data driven by making it clunky, if not impossible, for the “average” employee to query data. Why? Because the analytics tools of yesteryear typically require data specialists to act as gatekeepers of data, which significantly slows the speed to insight — and produces static reports rather than interactive charts.

Hurdle #4: Data Literacy Gaps

As CIO Dive cites, a recent report from Forrester revealed 41 percent of business leaders find the process of turning data into decisions “very or extremely challenging.”

It’s worth noting three of the things companies successful in this endeavor do are:

– Identify gaps in data literacy

– Instate a role-driven data literacy program

– Ensure data producers take user needs into account

It’s not enough to have a team of highly trained data scientists and analysts. While these power users are an important piece of the puzzle, data literacy efforts must include front-line workers as well — some of who may have little prior experience working directly with data.

Data-driven decision making in the workplace requires buy-in and culture-building efforts from leadership, widespread data literacy initiatives, highly accessible data analytics tools and a single version of the truth.