There are many types of analytics tools out there today, all with different functionalities and features. While it can be easy to get overwhelmed by all this information, it’s important for business leaders to understand these tools before investing in them. Here’s the difference between search- and AI-driven data analytics.
What Is Search-Driven Analytics?
Of all the technologies driving the data world, search-driven analytics is one of the most prevalent newcomers to the field. In essence, the name really describes the function of search-driven analytics. It’s basically a way for users to run data analysis through a simplified search feature as opposed to having to deal with complex business intelligence tools.
At its core, search-driven analytics is all about simplifying data for users. While there are certain things that it only makes sense for an analyst or scientist to have on their plate, lower-level analysis can often be done by those without much expertise. Search-driven analytics is making this more possible than ever.
Why Is Search-Driven Analytics Important?
As mentioned in the previous section, search-driven analytics opens up data to more people who otherwise wouldn’t be able to generate insights. There are several reasons why this is incredibly important for enterprises looking to maximize return from their data. Here are some of the top reasons:
- Shorten time from question to action – The old way of doing analytics was long and convoluted. People would have to send off every query to the data team, then wait around while information was synthesized and put into reports. Doing this could take days or weeks from start to finish, and there’s no guarantee the information received after that time will actually be relevant anymore, or fully understood. In those cases, the process would have to start again. Search-driven analytics allow users to run basic analyses on their own, cutting out tons of waiting time—making business operations far more efficient.
- Fewer bottlenecks – It should come as no surprise there are bottlenecks when everyone within an organization is relying on the analytics team for answers. This doesn’t just slow everything down, it keeps an enterprise’s data experts from working on higher-level analysis that’s more fitting to their skill level. Giving people search-driven analytics can solve this, as only more complicated data problems will have to be sent through the analytics department.
- Develop a data culture – There has been a lot of discussion recently as to why a data-driven culture is important to enterprises today. It all boils down to the purpose of collecting and analyzing data, which, to research firm McKinsey, is ultimately about making better decisions. While this sounds so simple, basic truths like this can be overshadowed by complexity. Search-driven analytics puts data into the hands and workflows of more employees, which will inevitably integrate these ideas into corporate culture.
What Is AI-Driven Analytics?
The difference between search-driven and AI-driven analytics mainly comes down to who’s actually driving the ship. Where search-driven analytics are basically giving users more intuitive tools, the purpose of AI-driven analytics is to actually do the thinking. With AI-driven analytics, the program can actually leverage data in order to steer the questioning process itself. This is a massively powerfully ability, as it can help people arrive at answers in only a fraction of the time it would take otherwise.
Why Is AI-Driven Analytics Important?
AI-driven analytics is essential for enterprises in the modern economy because they take analytic capabilities to a whole new level. There are several ways AI-driven analytics is clearly benefiting business intelligence. These are a few of the top reasons:
- Insights become better with time – Since these tools are being powered by artificial intelligence, they actually improve as they incorporate more data points. This means usage will only lead to ever-greater outcomes.
- Tons of work happens in an instant – It can’t be understated how many human work hours are saved when organizations employ AI-driven analytics. Compressing that all into essentially no time is the perfect example of how AI-driven analytics are changing the business intelligence world.
Both search- and AI-driven analytics are important business intelligence tools with myriad use cases. Enterprises should work to understand how adopting these technologies can improve their operations.