A Quick Guide to Data Analytics for Managers

Many businesses and managers are starting to hear and take note of the buzz around data analytics, and given its importance in the modern business landscape, this is no surprise. As a manager, an understanding of data analytics, and what it can do for your department or team is very useful information. There’s a good chance you’ve already been using data analytics – if your company has a customer satisfaction survey, for example. It’s a powerful vehicle for decision-making, and it should be embraced by your company’s management team.

In this guide, we’ll look at what data analytics is, discuss the privacy of your data, and look at a couple of important analysis types and why you’d use them.

What Exactly Is Data Analytics?

Quite simply, data analytics is the process of collecting, analysing, and using the data collected by the business to assist executives, managers, and even employees on how best to guide and inform decision-making. The practice of data analytics is complex and there are many different strategies and ways to both collect and make use of data; data analytics is the field of work that does this. It’s becoming essential for companies looking for the edge over the competition or to stay competitive to make use of data analytics. Almost every aspect of the company can benefit from an understanding of the data available to it – from market research to customer service and even HR.

What Do Analytics Managers Do?

Many companies are opting to hire an analytics manager as they realise the importance of the role, particularly to inform high-level decision-making around all parts of the business. Employees who are proficient in these disciplines, usually because they have a digital marketing analytics degree from Aston University, offer unique insights into the operations of the business by creating strategies to not only collect as much data as possible from the day to day operations of the company but being able to make use of this data.

Typically, they’ll implement strategies and solutions to analyse data in the realm of customer experience, financial and product risks, and many other aspects of the business. If there’s something to be learned from data in the company, an Analytics Manager should be able to find a solution to do so.

User Privacy When Collecting and Storing Data

Your internal data is often already available to you, but if it isn’t this should be the first step to work on. You should be collecting data on sales figures, staffing, warehousing, distribution, logistics – almost anything you can think of. If you’re making use of one, many ERP software suites are already collecting this data for you, but there is always more data to collect. Storing this information is only a part of a much bigger set of discussions that should inform your data policy, and in the modern landscape, privacy should be one of the first things you tackle.

In a connected world a few years ago, data collection was quite easy. Privacy wasn’t the front and centre of our minds and we would freely part with our information. Today, collecting data comes with its own set of considerations, particularly with legislation being introduced like the European Union’s General Data Protection Regulation (GDPR). Your business and the way you collect, and store data should be done with care and responsibly.

A/B Testing

A/B Testing is the analysis technique that answers the question “which of these things works better for our use” in a particular case or implementation. In short, it’s a head-to-head analysis over what is more effective. The actual process is quite easy but understanding how to read the data and information collected from A/B testing is quite nuanced and requires some knowledge and understanding of statistics.

An example of A/B testing could be determining which product photos entice an online shopper to purchase something. You might run an ad with one product photo, and then run it again with another, then analyse which offered better sales results.

Regression Analysis

Regression analysis is often the use of data that most managers find the most intuitive to understand and use, but the execution can be far more complex than you realise. Understanding how to do linear regression is the most important thing you can do as a manager, and it’s likely that you’re already doing it to a degree. Regression analysis is the process of using your data to determine if there is a relationship between one or more variables.

To use an obvious example, if you’re managing a coffee shop, you might want to know if the weather has an impact on your sales. Do you sell more hot drinks on cold days, and if yes, which of the hot drinks sells the most. This might help you market your products more directly – if it’s raining you might advertise your hot chocolate on the sign boards around your store.  This is a simple form of regression analysis by using three variables – total sales, specific drink sales, and weather data.

Factor Analysis

The art of factor analysis can be used alongside many other forms of data analysis, and is a great way to determine what you should be paying attention to in your data analysis. As a business, you’re likely to have many variables for each collection point, and factor analysis can take all these variables and decide which ones make a statistically significant difference.

To borrow from our coffee shop example again – you might have data on how much sugar is used in each hot beverage sold, but factor analysis would likely determine that it isn’t statistically significant to sales numbers based on the weather, so can be ignored. The amount of sugar would, however, be important in determining what to charge for the beverage, so would be statistically significant in that analysis.

Data analytics is an enormous field that requires specialist skillsets to do well, but a rudimentary understanding of how to execute some of the simpler analytics and determining some of the lessons that can be learned from data is an important skill as a manager in any business size.