6 Questions To Audit The State Of Your Company’s Analytics Infrastructure

6 Questions To Audit The State Of Your Company's Analytics Infrastructure

It’s no secret that everything businesses need to grow and accomplish their vision is theirs for the taking. But like anything that yields powerful results, the process doesn’t come easy. This describes the dilemma many organizations are facing when it comes to getting insights out of data.

But before enterprises can shore up their analytics processes, they need to understand what’s holding them back.

Audit the state of your company’s analytics infrastructure with these six questions.

In any analytics program, the goal is to collect raw data and turn it into interpretable information. Of course, many steps must happen in between collection and insight, but the length of time that it takes is the difference between being a reactive and proactive company. It’s also probably the determining factor for gaining an edge over competitors.

Just because data is abundant, doesn’t mean certain data sets aren’t sensitive. Companies collect all sorts of information and make sure it has the right architecture. At the same time, data needs to be accessed by relevant team members without delays or inaccuracies. Businesses need to lay out a centralized Governance framework that defines procedures, roles, and responsibilities related to how data is transmitted throughout the organization.

This is no easy feat, which is why many enterprises are leveraging artificial intelligence architecture like ThoughtSpot for data analytics, ensuring a single version of the truth, access at scale, and custom permissions down to specific data rows.

Innovations and operation-changing developments are rarely borne out of a vacuum. If they were, we wouldn’t need to be physically present at work. So, while collaboration is integral to making important decisions and fostering new ideas, a baseline needs to exist for productive discussions to occur. This is why it’s essential to develop a data literate culture. A company could do everything right, but if the data team is the only Business unit fluent in data analysis and terminologies, it’ll have a hard time getting value.

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