Self-service analytics has become quite the buzz word recently. As organizations look to become more data-driven, the promise of self-service is certainly alluring: the ability for all users, regardless of their role or skills, to analyze data and make smarter decisions. And even though self-service analytics tools have become increasingly user-friendly and require less IT involvement, many of these tools have not led to widespread adoption.

In fact, our own market research has found that the adoption rate of self-service BI tools is at 22 percent, even though users have access to these tools. There is a clear gap between the promise of self-service and reality.

So how do we get there? There are some key practices that we have seen successful businesses implement to find that self-service nirvana.

Understand Your User Personas

One of the main reasons that self-service tools have yet to be widely adopted within organizations is that most of the self-service tools on the market today are aimed at one type of person, typically either an IT technologist or a sophisticated data analyst. However, that one-size-fits-all approach doesn’t satisfy the unique needs and skill sets of business users who are also looking to leverage self-service analytics.

In order to drive self-service adoption, organizations need to stop focusing on the tools and start focusing on the analytics experience. In other words, stop having people work with tools and have the tools work for the people.

The first step, then, is to understand how your end users use data and what their decision environment looks like. Some users, for example, want to view static reports, while others may want to supplement existing dashboards and reports with their own metrics and measures, or may want a truly self-directed experience.

It’s like buying a car – there are hundreds of options depending on what your needs are and what you are trying to accomplish: minivans, motorcycles, sports cars, sedans, SUVs, among others. And within each of these groups, you have even more options: hybrid, manual, entertainment systems, remote start, etc. The car you choose is based on what you need it for, not what your neighbor needs it for.

By defining personas within your organization, you will be able to map the right capabilities to your users’ needs and abilities, creating the right analytics experience for everyone.

It’s only after you determined the personas, that you should consider tools. When looking for tools, you should strike a balance between optimizing the portfolio and optimizing fit to end-user needs. For instance, some users are going to want to deploy a highly scalable distribution of well-defined metrics in the form of dashboard and reports. Others will want guided data, secured distribution, and the ability to pick data and format visualizations to share. And some will want the ability to choose their own data sources, in addition to accesses corporate data. They want to collaborate to discover the best insights and push the results back up the chain for standardization as required.

Even with good definition of your personas and the right portfolio of tools, you’ll never get to 100% without some exceptions. Standards aren’t about eliminating the exceptions, but carefully managing them. You should have standard tools that fit 80% of use-cases, and then deploy specialist tools in very tight, limited deployments to meet the demands of exception use-cases.

Put Analytics in Context

Analysts have regularly said that the ceiling for BI adoption is 30%. However, as previously mentioned, our surveys have found that only 22% of business users have and use self-service analytics tools. This 8% gap is an opportunity for more end users to make data-driven decisions. And who knows, maybe by delivering the right type of self-service tools to the right users, you may go well beyond the “ceiling” of adoption.

How can you get there? The key is putting analytics in the context of the applications people use every day. Broad-based BI basically says here is anything you might possibly need to know, both now and in the future. But it needs to be about the information a user needs in the moment they need to make a decision.

People love their smartphone apps, because they were purpose-built to do one thing. They give users the information they need in the moment they want to make a decision, and it’s contextualized to the task. Think the RedFin or Zillow app, which is using your location to give you information on the houses for sale near you, in your price range, which are having an open house today.

Users aren’t going to want to go into the Zillow app and Google Maps and and a mortgage calculator, just to get the insights they need. They want all the necessary data they need to make decisions within their main application.

Sales managers, for example, rarely leave Salesforce. It would be a lot easier for them if all their data was embedded within that application: a variety of Salesforce data, marketing automation data, various Excel spreadsheets – all combined in their Salesforce views.

It’s also important to think beyond the data model. The user interface (UI) and user experience (UX) is important for driving broad adoption. Not all users are “pivot-table” compliant. You need to focus in on the target user, their needs and capabilities, and deliver analytics in a UI that is engaging and fits the end-user.

Adopt Agile Practices

Once you have the right set of tools in place and your users are set up, you’ll want to adapt your processes to adopt key agile practices to make your organization more effective at all aspects of BI.

Agile focuses on a high-level of business user involvement, and iterative communication of requirements. You should focus the data warehouse team on providing the data sets that the organization needs across all users. Key analysts, who are on the leading edge of business requirements can help shape that roadmap. And, the warehouse team can help educate the analysts on what data is available, and the nuances involved in any particular data set.

You should also use these same analysts to test out new data sets. Rather than trying to get data into the warehouse right away, push it into data discovery applications and deploy it to analysts first. They will iterate through several potential KPIs, dashboard presentations, and other analytics quickly. As those requirements stabilize, the technical team can start the work of automating and governing that data set. This approach avoids costly rework on the data side if the analysts find the requirements need to change.

Finding success in self-service certainly won’t happen overnight. It’s a cultural change, and it will take time. However if you take the time to understand your users, provide them analytics within the application they use every day, and then optimize the process with agile practices, you will certainly get there a lot faster.

This article originally appeared in Dataconomy.