August 8

What Is Data Analytics?

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Data analytics, or simply “analytics” is the act of studying a set of collected facts and figures with the goal of better understanding your business patterns and history. That sounds like a mouthful, doesn’t it? But, put simply: it’s analyzing your data.

Using data analytics can help you get to the root of a business problem, predict future business direction, or even highlight areas where you can capitalize on improvement. You get a comprehensive view of your business: where it’s been and where it currently stands. You can even find patterns that point to where it may be going.

Analytics is a precise science that takes in and processes information. The first step is to collect all the data, but then you need to figure out what it all means. Data is useless if you can’t understand what it’s telling you.

However, analytics is not only about collection and analysis. It also involves management, organization, storage, and security. It also employs a number of subjects, such as statistics, math, and computer programming.

The 4 Types Of Data Analytics

There are a lot of areas to cover when employing analytics. Not all businesses have the same data needs, or even one company may have various data needs. Some issues call for different areas of analytics. 

There are four main types of data analytics; each one serves a unique purpose.

Descriptive Analytics

Do you remember learning about the 5 W’s in school? Who, what, where, when, and why. This type of data analytics is the what. It looks closely at the past so it can answer what happened. Essentially, descriptive analytics summarizes past events within your company.

When you approach data from this perspective, it can point to where your business stands today. It outlines the path you took that landed your business where it is at this moment.

The first step in descriptive analytics is aggregation; this is the collection and summarization process. Next, the data is mined. That’s when an analyst searches for patterns so that it can all make sense.

Diagnostic Analytics

This is the why of analytics. Diagnostic analytics looks for the reasons behind business events. It builds off descriptive analytics and roots out why business is where it currently is. When you use this approach, you ask how your company got to this point, and whether it’s a good or bad position.

Though you’re starting with descriptive analytics information, you may ultimately need a more extensive data set to get the complete picture. Once you have it, this type of analysis requires critical thinking.

Diagnostic analytics frequently involves identifying a situation that isn’t supported by data, an anomaly. If the data can’t explain what happened, the analyst must uncover other data to supplement their research and round it out with their best theory.

Predictive Analytics

Nobody can predict the future. Except for predictive analysts, of course!

Although descriptive analytics is the most used type of analytics, the area of predictive analytics is growing quickly (and is a form of analytics we’re very fascinated with!). This type gives businesses a solid look at what’s likely to come. Analysts here refer back to the data to identify common trends and determine if they will continue or change course. Paying attention to patterns is the most straightforward way to anticipate what is reasonable to expect in the future.

Predictive data models can be used in any industry. For example, predictive analytics can be used to call out when production lines are most likely to fail, or when employee performance might affect the churn rate. A business can have a better, more relevant plan for the future when they know what they’re preparing for.

Prescriptive Analytics

The final type of data analytics gives businesses a solid “to-do” list, helping to determine what needs to be done. It builds off of predictive analytics and is the basis for data-driven companies. It’s a highly complex approach to data analytics but can bring big rewards when done well.

When a company knows the likelihood of an occurrence, such as a drop in sales, it can take proactive measures. So, for example, if a company notices a trend that they see a decline in sales every time there’s a specific outside force, such as a weather event, they can get ahead of that and anticipate when they may need to boost marketing or offer discounts.

Why Data Analytics Is Important

Data analytics can be applied almost everywhere; its uses are insanely widespread. This is because so many industries use it, too: banking, retail, hospitality, energy, education, and transportation are only a few examples. Who doesn’t want to improve their company’s performance?

Today’s business world is super competitive. As a result, a company will likely fall behind without implementing data; businesses need to learn how to make data work for them.

Analytics gives businesses the chance to operate at a higher capacity. It’s a little like having insider information; only it isn’t illegal because it’s your information. Instead, analytics uses the information generated by your own company to enhance your business output and efficiency, and that’s always smart.

Benefits Of Data Analytics

Putting your company’s data to work means that you can improve how you do business and make your routines simpler. Analytics can pinpoint where and how your process can become more efficient. When that happens, you’ll see your company’s productivity increase.

It can also hone in on an irregularity or inconsistency within your system. There’s a critical time period you have to act on something that’s gotten off track. Before this issue becomes a big, unsolvable problem, you can determine the cause and correct it. Crisis averted.

Data can become information, which can become business intelligence. You can turn this intel into action. Make informed decisions and plans for your company that are likely to yield better results. For example, if your data indicates you’ll see a spike in sales, it would be wise to stock up ahead of time. That’s an informed decision; without that data, you might not have the heads-up to be ready for a run on your product. And if that happens, you lose a lot of valuable sales.

Conclusion

Data analytics may involve a lot of information and numbers, but it doesn’t need to be overwhelming. Knowing the right questions to ask for your business will direct you to which type of analytics to focus on; from there, it’s all about making the data take shape. If you use the right tools and are looking at the right information, it’s possible to shape your data into a story. 

It’s up to you to decide what to do with that story.


How Do Data Analytics And Data Integrity Make Your Business More Effective?

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