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Ad Hoc Analysis Statistics and the Role of Self-Service Analytics

By Michelle Gardner | July 11, 2017
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One of the paradoxes of technological innovation is that every improvement inevitably spurs end users to demand even more capabilities. For a while, accessing the internet from a mobile device was a revelation. Today, anything less than high-speed connectivity, robust functionality, and an excellent user experience is unacceptable to most device owners. The rules are always changing.

The same concept holds true for analytics. As soon as customers begin to get their hands on information that empowers them and increases their productivity, they naturally begin to want more. Basic embedded dashboards won’t hold them over for long. They’ll soon start requesting things like new and more complex data visualizations, the ability to customize dashboards, and real-time connections to new data sources.

>> Related: The 5 Levels of Analytics Maturity – From Basic BI to Sophisticated Differentiators<<

For a time, standalone self-service analytics tools that support free-form data discovery tried to solve the user expectation problem. But adoption of these traditional BI tools is steadily declining, in part because users dislike toggling between applications. In other words, your customers recognize that the time they spend moving between applications could be better spent actually doing their jobs.

Instead, users tend to send ad hoc analysis requests to application teams and IT departments. This leads to an unnecessary allocation of developers’ time and resources, taking focus away from their primary responsibilities. These time-consuming requests may delay software releases or keep application teams from developing features that can have positive impact on the company’s bottom line.

So how can organizations reduce the number of ad hoc analysis requests while still supporting user needs? Application teams should start by embedding those self-service analytics capabilities as deeply as possible in the application. This way, end users get what they need and the development team can focus on more important tasks.

In fact, according to a recent survey, more than 64 percent of respondents saw a decrease in ad hoc analytics requests after embedding self-service within their applications.

Embedded self-service makes jobs easier, both for end users and for IT and development teams. As the desire to reduce ad hoc data requests increases—along with user demand for access to do whatever they want with their data—it’s natural that end-user self-service is an important feature for analytic applications.

On the flip side, it’s likely that applications without self-service will see ad hoc requests increase. As more applications offer analytics to users, the desire for custom views will accelerate. This fact, coupled with increasing availability of self-service in applications, is making it clear to users that applications are capable of deep, sophisticated data exploration.

If other applications provide self-service, then why doesn’t every application? Teams that fail to embed self-service capabilities will see more ad hoc analysis requests from users for custom visualizations and information. As companies spend time and effort keeping up with these requests, they’ll find it nearly impossible to iterate on the core product—which will ultimately impact product release dates and ability to compete. The only solution will be to implement self-service.

Regardless of what business intelligence capabilities you choose to deploy, it’s important to remember that you will inevitably need a strategy to meet increasing user demands down the road. So start planning now for the future.

 

 

About the Author

Michelle Gardner is the Director of Corporate Marketing & Communications at Logi Analytics. She has over a decade of experience writing and editing content, with a specialty in software and technology.

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