It generally takes quite a bit of convincing to get business leaders to sign off on a data analytics user experience initiative — especially when it involves upgrading from a legacy system to one offering the latest wave of advanced features.
Then it’s continually important to justify the initial investment by demonstrating how analytics are driving performance with factors such as reduced inefficiencies and increased revenue.
Far too many companies have deployed analytics systems with high hopes, only to find the results underwhelming. Why? Because employees weren’t adopting the tools made available for any number of reasons.
It’s not enough to have analytics available; your employees have to be willing and able to incorporate them into routine business decision-making.
Only then will enterprises experience the return on investment they’re hoping to see.
How Analytics User Experience Affects Outcomes
Despite many organizations’ best efforts, business intelligence (BI) and data analytics adoption rates have remained stubbornly low — often reaching about 30 percent of all employees within an enterprise, according to Gartner.
The research firm offers a few suggestions for companies aiming to boost adoption rates amongst their workforces: Deploy modern BI platforms, leverage mobile analytics and ensure embedding capabilities.
All of these suggestions underscore the need to facilitate positive, flexible and expedient analytics experiences for users. Otherwise, they’re simply less likely to use the tools at their disposal.
Imagine you’re trying to buy a pair of pants online. You visit the website of a brand you generally like, but the home page takes more than five seconds to load. The navigation controls are convoluted. And, to top it all off, the search bar fails to return the results you needed. If you’re like most people, you’ll probably just close out of the website in frustration and try another company. The likelihood you’ll return to the website in the future is also significantly lower, based on your poor user experience (UX).
Many of the same principles apply to analytics platforms. Complicated, glitchy or confusing interfaces discourage users from working with data, especially non-technical users with limited analytics experience.
The lower adoption rates fall, the fewer employees incorporate data insights into decision-making — and less frequently. This starts to eat away at the potential positive impact of data-driven decision making on the bottom line. And isn’t that kind of the point?
Power Positive UX Through Data Analytics
Let’s examine what constitutes positive analytics UX design capable of encouraging employee adoption.
Here are some pillars of positive analytics UX to consider:
- Choose a platform capable of serving power users (analysts and scientists) as well as casual business users at scale. A unified platform is also much preferable to a patchwork of multiple, disparate solutions.
- Make insights easily shareable throughout an org and beyond. Research shows deploying mobile analytics, accessible remotely on smartphones, can boost adoption rates. Ensure tools and insights are embeddable in shared workflows for best results. Self-service interfaces, interactive data visualization tools and collaborative dashboards are a must.
- Harness search- and AI-driven analytics in tandem. Search tools provide a paradigm for anyone to ask questions, generate visualizations and pull insights. Meanwhile, AI-powered analytics do the heavy lifting of mining data for relevant insights rather than needing to rely exclusively on the manual efforts of human analysts.
- Establish data trustworthiness and a single source of truth — otherwise users may be wary about believing their findings.
- Oversee data usage and security through strong centralized governance. As Strategic Finance Magazine points out, the major principles of effective data governance are accountability, standardization and quality.
Optimizing the modern data analytics UX is primarily a matter of understanding what users want and need — then delivering convenient, flexible, interactive and scalable data experiences.