User adoption is the most common problem with any analytics project. According to the 2015 State of Self-Service Analytics Report, only 22 percent of business users have access to and actually use self-service BI tools when needed.
Many organizations fall prey to one or more common pitfalls. To improve user adoption and future success for analytics, avoid making these mistakes in your own organization.
Mistake #1: Only meeting the needs of a select group of analytics users.
If your end users don’t see themselves in the analytics solution provided, they won’t use it. Identify the different types of business intelligence users and their requirements, and then work with them to create a tool they can’t wait to use. Start by recognizing the different use cases in each department as well as the distinct needs of different users who may prefer either a defined or self-directed analytics experience. A solid understanding of your users and their needs will serve as the foundation for the analytics tools you build. Without this foundation, you’ll be flying blind and run a high risk of failure.
Mistake #2: Providing inconsistent or inaccurate data.
Even if the tool is exactly what your users need, if the data inside isn’t trustworthy, they won’t trust the project as a whole. By pre-configuring dashboards and reports, IT will be able to ensure users are pulling from a single version of the truth. Otherwise, you end up with inaccurate reporting due to high volumes and varieties of data or redundant information—and you risk compromising the value of analytics entirely. Audit the data in your analytics solution to ensure it’s the information users need and is governed from a data-quality standpoint.
Mistake #3: Discouraging the sharing of analytics.
Every business has users who will champion analytics and encourage more people to use the tools—but only if they can share their insights with others. Identify the people within your organization who understand the potential of analytics and empower them to publish new KPIs to the broader team. As the business feels itself getting smarter, more teams will see the possibilities of analytics and will start thinking about how they can make more data-driven decisions.