When you look back just five years ago, the business intelligence (BI) space looked completely different than it does today. Traditional BI tools primarily used by IT reigned and self-service analytics tools, primarily championed by data discovery, were only beginning to be noticed. The real work of data analysis was left up to just a few power users. The BI reports, dashboards and tools that IT provided were clearly inadequate because the adoption rate remained below 30% for many years.

What does this mean? Today, organizations now expect their employees, regardless of role or industry, to use analytics tools on their own to make data-driven decisions, known as self-service analytics. For instance, sales representatives want to track their daily activity levels against goals; hospital administrators want to monitor readmission rates against industry standards; and truck drivers want to analyze their fuel efficiency.

This shift towards self-service analytics has even led to quite a major shift in BI market, which culminated (with a not so little amount of social media controversy and upheaval) when Gartner changed its underlying definition of what a BI and analytics platform is in the latest Magic Quadrant for Business Intelligence and Analytics.

Gartner shifted its focus to where it sees market evolving – an area that engenders a lot of confusion, because self-service capabilities are still being defined. Gartner is attempting to define this rapidly changing market and has laid out criteria for where it’s headed. There have been many changes in the past number of years to the Magic Quadrant, with 2016 being the largest. I’m sure we can expect changes to keep coming year after year, as Gartner estimates the total BI market at $16+ billion.

In short, self-service is key. It’s no longer acceptable for data analysts to be the only ones to use analytics tools. But most people are not data analysts and they don’t have time to rely on IT for help to get the analytics they need. Companies can overcome this adoption challenge when they start considering the analytics experience, not the tools, as the real answer.

When Did the Self-Service Evolution Begin?

The catalyst for this fundamental shift occurred when budgets began shifting away from IT to the line of business (LOB). Why? Because LOBs were increasingly being held accountable for achieving performance criteria with no excuses, so they were given the economic power to succeed. However, business users quickly became dissatisfied with the information technology (IT) team’s ability to provide them what they needed in a timely manner that could keep pace with their ever-changing needs. This wasn’t all IT’s fault; it was often a result of shrinking budgets and resources.

As a consequence, business users began trying to solve their own problems without asking IT for help. They tried Excel, but it was tough to create analysis and data visualizations. They found new data discovery tools that promised better data visualizations, and indeed they did. Unfortunately, those tools were still really targeted at data analysts or power users and largely difficult to use. They still needed help getting data (unless it is a simple spreadsheet). Power users would build dashboards to share with others, but IT needed to get involved again to set up, configure, and administer the systems. Now, IT is getting saddled with systems they may know very little about. Some problems get better, some problems get worse.

Fast-Forward to Today…

Self-service analytics tools have become increasingly user-friendly and require fewer technical skills, allowing users to run more sophisticated tasks with minimal or no IT support – SQL queries, interactive visualizations, and personalized dashboards, to name a few.

Even working with the data is easier because many tools can access more data, including cloud data for CRM, marketing, or financial applications. While power analysts and IT are still needed in many instances to connect and understand data structures, the tools have improved enough so that some business users may be able to do basic data blending and then analyze and manipulate that data on their own.

When done well, self-service analytics has actually started to improve the relationship and effectiveness between the IT and LOB teams in most organizations. Business users are now able to make informed decisions faster, and IT is less burdened, enabling them to focus on high-priority projects. Ultimately, IT can now support an organization’s needs with less effort and the business can get answers faster.

Shift in Perspective

But do new tools solve the adoption rate problem? Our own market research on the State of Self-Service Analytics Report (October 2015) finds adoption rate of business users at 22 percent even though they have plenty of tools to use. There is clearly a large gap between expectations and reality when it comes to using analytics.

This shift to self-service analytics means that if you are serious about closing that adoption gap and getting people to make data-driven decisions, you need to stop focusing on the tools and start focusing on the analytics experience. Stop having people work with tools and have the tools work for the people.

The basic issue is that tools assume everyone has the same skills, needs, or roles. Certainly at some point in your life you bought a hat or gloves or socks or sunglasses or some sports equipment with the promise: “One size fits all.” It never does. And once you buy it, you wish you never had. The same is true of analytics software: a “one size fits all” approach is a guarantee your adoption rates will stay low. Do yourself a favor and keep your money.

On the other hand, if you have software that recognizes every person is unique and tailors that experience to him/her, you’ll start to see adoption rates go up and people use analytics to make more informed decisions. The number one barrier to adoption, even with the new class of self-service tools, is complexity of use: they are too hard for everyone except power users or data analysts.

Don’t fall into that trap. Give people an analytics experience that matches their skills and needs. People will seek out the analytics to do their job and you’ll be on the path to high performing, data-driven culture.

This article originally appeared in InsideBigData.