How Low-Code Analytics Will Democratize Data

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Click to learn more about author Dan Robinson.

I’d wager that most people who build websites today know very little HTML. That’s fine – most websites are built via low-code tools that hide code-level details behind GUIs. This lets website builders focus on creating products instead of worrying about CSS formatting. Web design has gone low-code, and other domains seem sure to follow. In fact, Gartner has predicted that low-code platforms will account for 65% of app development by 2024.

What’s Next?

What else is ready for a low-code approach? One clear answer is analytics. Today, any business with a website will collect or generate huge sets of data, and these data points aren’t just old-fashioned “big picture” items like Google search rank or average time spent on page. Today, customer behavior can be tracked down to the individual click, and that data can tell stories, if only it can be interpreted.

A low-code data solution democratizes analysis in much the same way that Squarespace or WordPress democratized website creation. What once required hours of expert data exploration can be accomplished by someone who never learned SQL, because today’s tools presume little prior knowledge and handle a lot of the complexity for you. 

Implementation vs. Installation

While low-code technology can simplify otherwise extraordinarily complicated tasks and is often designed for the non-technical business user, in many organizations it’s still the IT department that ends up doing most of the low-code work. 

To avoid this, organizations that use low-code analytics need to put some work in to make sure systems are implemented rather than merely installed — that employees get enough experience that they’re able to easily bring analytics into their regular workflows. How to do this? First, make training both hands-on and practical. As you walk employees — particularly less-technical employees — through the tools, make sure they’re working in contexts they’re used to or on projects they’ve worked on previously.

A product mindset is also essential for anyone looking to ensure effective low-code solutions. Simply put, your analytics program should evolve based on your employees’ usage of and response to the tools you roll out. Introduce the software, gather feedback from employees on its effectiveness and usability, and then iterate. If you neglect to iterate or fail to seek feedback, the business or less technical users may opt out. Put another way, you won’t achieve “toolchain-employee fit.”

Finally, the introduction, iteration, and expansion of low-code analysis cannot happen in organizational silos. In fact, proper implementation of low-code analysis should tear down silos and open up a business. When every team has the same insight into the business, organizational alignment usually follows. If only one team relies on analytics, that team may find itself pursuing goals that are mysterious to other teams. That often happens when analytics capabilities are limited to IT or engineering departments. With low-code, everyone has an unmediated view of the facts of the business; all teams are operating from the same baseline.

Conclusion

When implemented properly, low-code analytics are a high-yield investment. The world’s largest and most successful digital businesses already employ extensive analytics; their staff of engineers and data scientists have, all too often, given them significant advantages in optimizing and expanding their offerings. With the new low-code tools now available, there’s no reason for smaller firms to give up hope of competition. The world of analytics is now open to everyone; businesses should take heed and take advantage.

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