How Scalability and Usability Can Spark Analytics Adoption

If your organization is struggling to fully embrace data analytics, you might not be approaching it from the right perspective: that of the business user.

Depending on who you ask, 38 to 50% of today’s workforce is a digital native – someone who grew up with technology and the Internet. By 2025, this number is projected to hit 75 percent. If the modern worker is so comfortable with technology, why is it so hard to get them excited about using analytics?

You’ve probably spotted the flaw in these statistics: it doesn’t account for the attitudes of the remaining 50% of the workforce, and it doesn’t factor in the difficulties inherent in organizational change management. However, we’ve found that focusing on scalability and usability – in conjunction with building the right expectations and developing a long-range, purposeful plan – can help speed up the acceptance of analytics tools into the daily workflow.

Scalability Requires Usability

If you Google ‘scalability’, you’ll get a pair of closely related definitions: “the capacity to be changed in size or scale” (i.e., availability) and “the ability to be used or produced in a range of capabilities” (i.e., adaptability). This perfectly reflects the importance of scalability to analytics adoption: you have to give people a tool that

1. they can use easily (availability and usability).
2. delivers information that’s relevant to their needs (adaptability).

Scalability actually starts with usability: how a system performs and how easy it is for non-technical people to use. For analytics to have long-term success, it must be able to adapt to the needs of its users.

First, the backend has to be robust enough to accommodate future workloads. It has to support data integration scenarios that can become quite complex. It shouldn’t limit the types or complexities of queries and it should be able to handle all kinds of use cases.

At the frontend, business users need a single, intuitive tool (dashboard, chatbot, etc.) that adapts to their usage patterns, business vocabulary, etc. and fits in with their expectations. Above all, analytics tools must be easy to use. If usability is lacking, there’s really no hope of achieving widespread analytics acceptance.

When tools are adapted to each use case, user adoption figures go up. People start using them as part of their daily work. Thus, insights become readily and quickly available. And that’s the whole point of analytics.

How to Foster Analytics Adoption

We can see how scalability and analytics adoption work together. Now let’s consider some of the other aspects behind fostering analytics adoption: planning, technical frameworks, and user testing. In our experience, the following actions can help accelerate analytics initiatives:

• Do your research:

  Some companies jump right into analytics without doing their due diligence: understanding the business goals and how analytics will support them; identifying and finding the data sources they’ll need; defining where and how they’ll implement analytics and how they’ll extend it, etc. Don’t be one of these organizations; get the whole picture of your business and the available data analytics options before you make any plans.

• Have a plan and a goal:

  Next, take the information from your research and use it to identify your goal and how you’ll get there. This will vary from company to company, but you know the saying: if you don’t know where you’re going, you’ll never arrive.

• Build the right framework:

  Now that you have something concrete to support and something to work towards, set up your framework. By this, we mean choosing the right technologies, tools, and systems to support both current and future workloads and provide a good experience to end-users. Companies also have to ensure they have high-quality, complete, and unbiased data; poor-quality data leads to poor-quality analytics.

• Consider the Cloud:

  Cloud computing has gained popularity because it’s a cost- and labor-effective way to cope with heavy computing tasks. It’s inherently more flexible and scalable than on-prem setups, and many vendors offer attractive price plans. All of this holds good for analytics uses, with the additional inducement that Cloud-based infrastructure can speed up implementation times and make analytics’ value more quickly apparent.

• Invest in UX:

  As we mentioned while discussing scalability, user experience is paramount for acceptance. But designing a tool that business users will like doesn’t happen in a vacuum: it’s important to have actual users test the analytics tool. Furthermore, it’s usual to have many rounds of testing. It will take more time at the outset, but the result will be a solution that’s useful and easy to use.

Commit to Your Analytics Journey

Finally, keep in mind that analytics is not a sprint. It’s a journey. The desired results don’t always materialize with the first attempt. It takes time for analytics to get fully incorporated into the daily workflow and for the analytics system itself to mature and produce its best results. However, you can bring analytics into your daily business routine – and reap all the benefits of becoming data-driven – in less time if you plan, prepare, and pay attention to your business users.

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