Evaluating Data Analytics Software

The world of data analytics software is constantly changing. What was cutting-edge only a few years ago today might be run-of-the-mill. Enterprises need to stay on top of these trends to get the most out of their business intelligence. Here are some tips for evaluating data analytics software.

What Are Your Goals?

Before diving into any hyper-specifics regarding data analytics software, your firm needs to first do some groundwork. There are lots of great business intelligence analytics tools out there today. While obviously a blessing, as enterprises are better able to meet their needs today than ever before, it can also be a curse in disguise.

See, your organization simply doesn’t need every little tool and gadget in order to succeed. In fact, going overboard can distract from what really works, while being a massive cost sink when you end up with assets that aren’t producing ROI. Enterprises need to define their organizational goals—and also determine the scope of how they’re going to use data analytics software to achieve them—before shopping around.

What’s It Going to Cost?

There are going to be expenses involved with every kind of analytics investment. Whether it’s time, personnel, upfront capital, or operating expenses, you’re going to have to pay a toll somewhere in order to get places with your data. This doesn’t mean you should be lobbing money at things just because they’re expensive and seem like they can do a lot. As mentioned in the previous section, think about how you’ll concretely be able to use tools right now, not only how you might be able to at some point.

It’s worth looking into some of the differences between various types of analytics software in terms of its ability to deploy. Many enterprises today are warming up to cloud-based data analytics software for a few reasons—one of which is potential costs benefits. Since many cloud options come as a subscription, it’s easier for enterprises to match their needs exactly, which can help save valuable resources to be used elsewhere.

What Are Its Selling Points?

Every flavor of data analytics software is going to have its unique selling points. But which ones are actually going to help improve operations for your enterprise? Looking at how other similar organizations have benefited from adoption is one way to quickly assess the likelihood of a tool living up to your needs. This study from McKinsey highlights some of the ways AI-powered analytics can help boost the business intelligence of a variety of organizations. They highlight logistics as one industry that can massively benefit from the optimized nature of artificial intelligence in analytics software.

Due to the large processes involved with logistics, it would be impossible for a human to search through rows and find patterns on their own. AI-powered data analytics software, however, can search through massive data sets in almost no time.

Will It Foster Data Democratization?

The idea of data democratization has gained a lot of prevalence over the past few years. And there’s good reason for that. Data democratization is the concept of giving more people the ability to use business intelligence in order to improve business outcomes. When it comes to data analytics software and data democratization, self-service is the name of the game.

Self-service analytics allow users who don’t have a lengthy background in data to run basic queries on their own. There are some massive advantages to this. For starters, when people can run their own analysis, they’re able to get actionable results in just a few minutes, as opposed to waiting days or weeks to get an answer back from an analyst. Furthermore, analysts don’t have to fill their days fulfilling the request of others, and can instead work on more nuanced data problems.

How Easy Is It to Share?

There are tons of insights that can come from data analytics tools. So why get a tool that makes it a headache to share them? Look for analytics software that allows for easy sharing of visualizations and other materials. It’s additionally useful to incorporate embedded analytics, which allow you to place analytic readouts directly into workflows or wherever they’re most convenient.

Enterprises need to consider several things when looking at new data analytics software. While there are lots of options out there that will probably work for your firm, selecting the optimal choice can be game-changing.