Where You Should Be Using Automation in Analytics




automation, analytics, raw data

In the early days of analytics, automation probably played too big of a role. The technology was new, and companies collected troves of information they didn’t need simply because they suddenly could. Analytics has become much more sophisticated. We know it’s dangerous to think that just because we’ve automated data we have all the answers. Raw data are meaningless until we do something with them. Here is where you should be using automation in analytics.

When we know how to use automation properly, we can turn data into information to start seeing patterns and stories.

From there, the information lends insight to solve business problems and ultimately, monetize the original data. Here are three factors to monitor when turning your business’s raw data into information.

#1: Data Quality

Like with many things in life, it’s not the amount of data that’s important but the quality of the data. Don’t make the mistake of thinking that loads of facts mean anything significant.

Data, and therefore the information they provide, typically fall into one of three categories:

  • Good data
  • Bad data
  • Missing data

Good data yield the insight you were looking for when analyzed properly. Bad data can be misleading or simply false, depending on if the facts are wrong or if they weren’t input properly (see below).

And of course, missing data don’t help you, but they can at least be collected. In fact, missing data are better than bad data overall.

Your job in managing your analytics system is to determine what kind of data will be most valuable in accomplishing your unique goals. For example, if you want to know about employee satisfaction, do you only look at current employees, or do you include past workers as well?

It’s tempting to say all employees, past, and present, need to be counted. But what if employees who left before a certain date were made redundant in a hostile takeover or were part of an earlier iteration of the company that didn’t even do what it does now? Maybe you need to put parameters around your data to screen out facts that, while not wrong, aren’t pertinent to your current question.

And of course the source of your data, not just what you choose to exclude or not, is essential too. Many companies think competitors’ data is more valuable, but often what you’re seeing is a deceptively small part of their total picture. Your own company is a rich, untapped vein of gold that you can mine for nuggets of insight.

#2: Input and Response Structure

Next, you need to examine your input and response structure. Like with quality, the data are only as good as how you can store them and put them in play.

Imagine you’re an airline with a website that gives customers all your flight information. First, you need to make sure there aren’t redundancies in how the data are entered. If your one flight looks like three in a customer search because of input errors, it gives the false impression you have more available flights than you do.

You also need to make the response fit the need. You don’t want customers getting extraneous information they don’t care about, nor do you want them waiting 10 minutes for search results as if you’re back at the dawn of the Internet.

Automation is great when it spits out figures in an instant, like ESPN giving Tom Brady’s stats. It’s not so wonderful when it grinds your business to a halt because it’s bogged down by too much data or the computer coding to manipulate data isn’t up to snuff.

Structuring data for effective use can be one of the most challenging aspects of analytics. But it’s well worth it when you get the results you desire in an instant and can use those results to make informed decisions.

#3: Data Availability

Does your business make data available, for the most part, to anyone who could use it? Or do you wall it off with passwords and firewalls that make employees throw up their hands in frustration?

Your data don’t have to be completely open, but employees shouldn’t have to jump through a million hoops to get what they need either. The democratization of data is one way to help encourage a new analytics system to gain traction.

Glean New Insights with Analytics

You can’t manage what you’re not measuring. Get your data in line with what you’re asking of your analytics, and watch your business glean pearls of insight you never thought were possible.

For more advice on how to turn data into information, you can find Guaranteed Analytics on Amazon.

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Original article: Where You Should Be Using Automation in Analytics
Author: Jim Rushton