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How Companies Can Bridge the Last Mile in Data Analytics

How Companies Can Bridge the Last Mile in Data Analytics

Once upon a time, businesses had to rely on instincts and experience to inform their next move. These days, our digital world carries the answers to every imaginable business decision. But as we’ve learned in other facets of life, capabilities and achievements don’t always sync up.

When it comes to analytics, businesses believe in the importance of data initiatives, but admit they haven’t experienced the value they had in mind. This gap is caused when end users can’t interact with analytics fluidly.

Let’s discuss how companies can bridge the problematic last mile in their data analytics programs.

Promoting Data Literacy

A significant obstacle must be conquered before tech and infrastructure are decided: giving employees data skills. Most companies, however, overlook this necessity. What ensues is a relatively quick design process followed by drawn-out adoption that sometimes never materializes.

Reversing this order will most certainly yield more favorable outcomes. This is because placing importance on data makes adoption easier. Teaching non-technical users how to interpret data and communicate insights will also clarify planning and help organizations choose the right tech for their employees to leverage. Because planning an analytics ecosystem can be complicated, businesses will waste less time by focusing on the last mile first.

Designing Analytics Infrastructure with Context in Mind

The sky’s the limit when employees across the workplace can interact with data autonomously. But there’s still more work to be done. The merits of a data-driven culture may exist, but inroads still need to be built. This is when context comes into play. Historically, context didn’t matter. Insights were controlled by the data team and filtered down to end users via report requests. To bridge the last mile, though, findings need to reach end users in ways that fit their workflows.

Technologies like self-service analytics complement employees’ newfound data literacy by providing an easy way for them to search for questions and receive instant answers. Next-generation tools like ThoughtSpot not only allow for text-based queries but also provide voice analytics capabilities to mirror the conveniences of our everyday consumer lives. The ease with which employees can access insights undoubtedly makes them more effective in their roles, but simple sharing features also help strengthen entire teams and departments.

Incorporating AI to Accelerate Deeper Insights

Ad hoc queries aren’t the only cutting-edge tech that can heighten the everyday knowledge businesses glean. Data-literate company cultures and streamlined analytics processes uncover much of the value lurking in companies’ vast amounts of data. But in the age of artificial intelligence, there’s always more depth to be had. AI subsets like deep learning and machine learning help squeeze more value out of queries, as well as ensure users don’t miss something pertinent. Per McKinsey, AI techniques have the potential to create between $3.5–$5.8 trillion in value annually across nine business functions and 19 industries.

After all, even with a baseline level of data literacy, employees are only human. They’re bound to miss details from time to time, as are trained data professionals. Opting for an analytics tool that incorporates AI will ensure users know if a data set contains an anomaly, a causal or non-causal relationship, or a key indicator that’s not so obvious. With AI doing the heavy lifting, employees can trust that they’re receiving accurate results and move on to the next task.

Succeeding in analytics requires planning and executing on several levels. But if that last mile isn’t addressed, the value of analytics will be left in the lurch. When laying out the flow of your data program, address that last mile first by increasing data literacy, delivering knowledge to users with context, and using AI to get the full scope of insights. Then, and only then, will the gap between expected value and realized gain align.

Tags : Data Analytics
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