In working with clients over the years, I’ve seen the same challenges emerge after the launch of a new data analytics solution – gaining user adoption. Invariably, every organizations runs into one or more of the following barriers:

• We used all the newest technology and knew everything about the data, but I’m not sure why the users wouldn’t latch onto the system.
• Even though we gave them all this great new data, they wanted to continue to pull the data to the side and create their own reports.
• They never really seemed to trust the data we were giving them.
• They felt like the data was the same as they were used to seeing and felt more comfortable in their original platform. I don’t think they saw the value in what we were trying to do for them.
• While the organization of the reports made sense, we later realized we had a lot of inconsistent data in the system, which really rendered these reports useless.
• We did the best we could to put together training material and training sessions, but for some reason it didn’t really matter. The users just didn’t seem excited to use the new system.

The Success of Your Data Analytics Project Depends on Your People!

Like all technology-driven initiatives, tapping into the potential of your data begins with defining your business needs. From there, you can determine what’s wrong in the current environment, how a new solution will benefit the organization, and what high-impact areas to prioritize first.

However, a critical but often missed, step is ensuring maximum user adoption post-launch. Often, I see organizations treating their data analytics initiatives as a technology problem, pushing forward with implementation without engaging the business along the way. As a result, the users tend to feel like the solution is being done to them instead of fitting their needs. They don’t understand what they’re looking at it, why this data should be important to them, and may not even trust the validity of the report.

Even when your data analytics projects finish on time and on budget, the technology behind the solution has no inherent value – it is how well your people are leveraging the solution that determines the success of a project.

Four Common Assumptions to Avoid at the Start of a Data Analytics Project

Without a doubt, IT can’t possibly be expected know everything that is import to a business user’s job. However, by avoiding some of these common assumptions, they can begin to break down the barriers that prevent users from embracing the new solution:

IT owns the data

Just because data lives on individual databases or in the cloud, the business may still have their own set of rules for what the data means to them, and their perspective is important to consider.

IT can just use existing reports to decipher calculations and business rules

There are several flaws with this approach.

• The calculations in existing reports may have deficiencies, and now those flaws are being reproduced in the new solution.
• They might be using one-off reports that were created for a specific need and likely contain different calculations, but have now become the “record of truth.”
• There could be hidden reports IT is not aware of.
• The current reports only show what the company is currently looking at, but fail to identify what additional insights are needed.

The business is represented because we’ve engaged with one person from the business side

When engaging with users to define calculations or rules, it’s hard to be sure whether you’ve engage with the correct people or established a true owner of the calculations. Different users or segments of the organization may have a different perspective. User B asks for a change to a calculation that is also being consumed by User A, thereby impacting that user as well. Down the road, User A identifies the number is no longer correct and asks for a change (back to the original). The cycle continues and both users lose faith in the system and the project team.

Everyone accesses data the same way

Along the same line, and perhaps more difficult to identify early on, is when different users across segments of the organization have different thoughts on how to capture data into the system (data governance), whether it’s granularity of data, methods of coding and categorizations, etc. When this is inconsistent throughout the organization, it is difficult to produce reports that can be consolidated or consumed across the organization.

While most companies recognize the incredible value of data analytics, the majority of projects fail to get off the ground. However, it is entirely possible to gain greater user adoption with the right approach. In part two of this series, we’ll explore the four key strategies to incorporate at the start of your your data analytics project to improve your success rate and realize the full potential of your data.