We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.

Business Intelligence: The Trinity of User Adoption

Originally published January 14, 2010

If you are reading this, you probably already know why user adoption is so important. Maybe you’ve made an investment in business intelligence (BI) and are asking yourself, “Now what?” Maybe you’ve been up and running for a while but still struggle with broader user adoption. Or maybe you had good user adoption and then something bad happened and users disappeared. Whatever your reason for focusing on user adoption, in financially trying times, organizations have to ask themselves some very difficult questions. Certainly, good BI can be business transformative, but bad BI is just plain expensive.

It comes down to three letters: R-O-I, return on investment. If you have a lot of users on your system, the investment is generally considered a good one. But volume doesn’t always mean value, and your leadership may ask you to look beyond user numbers and report numbers and determine the ROI of your BI program. The good news is that all it takes is one user making a big discovery that saves the company money. That could be found in process improvement, product development or just operational efficiency. But big blockbuster discoveries aren’t very common. The most consistent way to find a solid ROI for your program is to have a solid base of users using the tool for everyday business decisions.
In order to build a good user adoption strategy, first we need to establish a common framework in which to discuss user adoption. This framework has been developed as a result of years of working with end users. The hallmark of a successful BI program is determined by how well users have adopted it; however, many BI programs struggle to get good adoption or, worse, pay no attention to user adoption as a success criterion.

Conceptually, user adoption is greater than the sum of these three parts. While you may feel strongly about one over another, these three work in harmony. Without one, the others are not strong enough on their own to hold up user adoption.

Figure 1:
The Trinity of User Adoption

Ease of Use

First, we need to understand the impact that the general user interface (GUI) has had on user adoption. In the last few years, Google has changed the way we look at the GUI. Nothing so simple yet effective has been produced before or since. Google makes it so simple to enter a search term and get 12,200,200 results back in .12 seconds. This alone has radically changed the way that everyone interacts with software. I call this “the Google effect” and it impacts almost all areas of BI user adoption because it fosters expectations about simplicity and performance.

Users have become much more sophisticated, but they also expect simplicity. Most BI user interfaces are not particularly elegant. While they have come a long way, there is still a lot of functionality that is provided on the primary landing page. Business intelligence tools are not search engines, and their interface will never be that simple. But we need to consider the design and development from a user experience perspective. Simplicity can be measured by counting the clicks. Research varies on how many clicks are too many, but the average user seems to get frustrated by about the tenth click.

The other impact from Google is performance. User interface aside, the real challenge is duplicating the performance that users experience with Google. A user once asked me, “If I can get 14,000,000 results back in less than a second in Google, why can’t my two-page report return in less than five minutes?” She was frustrated, and my inclination was to sit her down and draw on a whiteboard the difference between a healthcare multidimensional model, aggregation of data, etc. and Google pulling 14,000,000 results for “Chicken Recipe.” I decided against the lecture. Truth be told, we have made incredible strides in performance. When I started, all reports were run overnight and delivered the next day. But, we aren’t Google, and we may never have that level of performance consistently.


Just about every week there is an article on a major news syndicate talking about the impact of our lifestyle, stress and health. With all the tweeting, Facebooking, texting, emailing and calling we do, it’s amazing that we have any time to stop and concentrate. The attention scarcity that most of us suffer from has a big impact on business intelligence. If some users only run reports quarterly, what are the odds that they can remember everything over gaps of three months? They cannot remember any nuances associated with running reports, so your first line of defense is to ban all nuances. Make report running as clean as possible.

Attention scarcity highlights the need for training and user support within a BI program. A few years ago I was at a company that had a BI tool with little adoption and lots of complaints. They asked me, “Is the tool bad?” After having a couple conversations, I realized that no one was responsible for the tool. No one was managing what went in, what came out and how to address user concerns. Users were ignored, written off as difficult, or told there were no resources to accommodate the request. The tool wasn’t bad, their process was. They had no user support. This isn’t the Field of Dreams – if you build it, you’ve only done about 40% of the work, and they won’t necessarily come.


One theory in psychology postulates the larger the gap between a person’s perception and reality, the more likely that individual will be depressed. That theory also applies to the usage of a BI tool. If expectations aren’t met, there will be dissatisfaction among the ranks.

Expectation management is the key. The BI tool is just a tool, it is not Nirvana. It will not make you smarter, faster or thinner. You must build the trust of the user base from very early on by tailoring your communications. As you communicate, keep in mind that trust is result of four factors: accessibility, reliability, consistency and honesty.
  1. Accessibility. Let’s face it; if the software or the data are not accessible, not much else matters. Luckily, accessibility is not usually a big problem. But if you know that your ETL process is performance-challenged, or lacks the appropriate frequency, then you need to address the issue.

  2. Reliability. Reliability means that user can trust that the results will be accurate and dependable, over and over again.

  3. Consistency. If one thing can drive a data person crazy, it’s different results for no “good” reason. If the same query is run twice, the results had better be the same. Nothing deteriorates trust quicker than a well-informed and vocal power user.

  4. Honesty. Billy Joel probably said it best: “Honesty is such a lonely word. Everyone is so untrue.” No one likes to confess that during the data load someone accidently dropped a table. Or that some of the code was inadvertently dropping claim records that ended with a “1.” Maybe you don’t need to tell them the gory details, but you need to tell them enough so they understand any delay or change in the data. You need to be timely and forthright. They will know when you are dishonest, and it will degrade trust.
The trinity of user adoption provides a framework for the corresponding white paper entitled “Six Strategies for Improving User Adoption.” All six steps for increasing user adoption directly address one of these three concepts. For further details and to learn about the six steps to improve user adoption, you can download the white paper through my BeyeNETWORK channel  or here.

  • Laura MadsenLaura Madsen
    Laura leads the healthcare practice for Lancet, where she brings more than a decade of experience in business intelligence (BI) and data warehousing for healthcare, and a passion for engaging and educating the BI community.  At Lancet, she spearheads strategy and product development for the healthcare sector. She also works with key accounts across the country in the provider, payer, and healthcare manufacturing markets. Laura is the founder of the Healthcare Business Intelligence Summit, an annual event that brings together top hospitals, insurers, and suppliers in the healthcare business intelligence space. Laura is also the author of the popular book, Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics (Wiley, 2012). You may reach her at lmadsen@lancetsoftware.com.

    Editor's note: More healthcare articles, resources, news and events are available in the BeyeNETWORK's Healthcare Channel featuring Laura Madsen and Scott Wanless. Be sure to visit today!

Recent articles by Laura Madsen



Want to post a comment? Login or become a member today!

Be the first to comment!