Data Analytics: Cutting through the noise and identifying what really matters to you

In this article featuring Tiffany Bayley, Assistant Professor of Management Science at Ivey Business School, we discuss the core principles of data analytics, the different types of analytics, and how leaders everywhere can identify which data is most important when strategizing for organizational goals.

Successful organizations have long understood that data can play a critical role in providing information and insight to support good decision making, improve operational efficiencies, and inspire new ideas. But today’s leaders have more data to sift through than ever before.

With more than 60 per cent of the world’s population now enjoying internet access,  we generate so much digital data that we needed to create a new word – the zettabyte – to measure it. One zettabyte is the equivalent of around a trillion gigabytes, and there are currently an estimated 44 zettabytes of data in the digital universe, with that number growing with every click.

Whichever way you measure it, big data is now really big. It’s changing the way we do business, creating new opportunities and challenges for leaders across almost every industry.

“There’s so much more data available,” says Tiffany Bayley, Assistant Professor of Management Science at the Ivey Business School and a faculty member of the Ivey Academy’s recently-launched Data-Informed Leadership program.

“Things that we didn’t think we could measure before, we now have the capacity to track,” she says.

For example, Bayley notes that in the past, historical sales data might have been the only information a store owner had available to forecast future demand. Today, even the smallest boutique probably has a retail website allowing the owner to track how long each customer spends on a page, the type of items they look at, how often they visit, whether or not those visits turn into a sale, how often items are returned, and more.

“There are so many more touch points with our customers,” says Bayley. “It’s created a new way of thinking. Can I use that data to predict what the customer is going to do next? Can I tailor a message to target specific groups of customers who exhibit similar characteristics? How can I work with clients to provide unique and tailored experiences?”

What is data analytics?

The process of analyzing raw data to find trends that can help answer those types of questions – and more - lies at the heart of data analytics.

“We often talk about the different stages of analytics,” Bayley says. Most data analytics initiatives begin with descriptive analytics, which uses historical data to provide insight into past performance. Descriptive analytics is used to measure things like return on investment (ROI) and to develop key performance indicators (KPI).

Diagnostic analytics can be used to dig deeper into why things have occurred in the past by identifying and examining any anomalies in the historical data.   

“Then we have predictive analytics which builds upon relationships we can see from historical data to try to predict what could happen in the future,” Bayley explains. Common predictive analytical tools include statistical and machine learning techniques like regression analysis and decision trees.  

Prescriptive analytics uses insights from predictive analytics to help leaders make data-informed decisions about what they should do next. Prescriptive analytics often uses optimization and algorithms to sift through massive amounts of data quickly and more efficiently than humans could.

“When we get to prescriptive analytics, even though the model is going to give you a course of action based on your predictions, it’s never meant to be the final decision,” notes Bayley. “It’s something for you to consider and evaluate, and then decide whether it’s feasible and something the organization would actually want to implement.”

The maturity of your organization and the decision you are trying to make will determine what type of data analytics to use. “You might just be using one type, or you might use them all,” she says.

Why is data analytics important?

Today’s leaders are not engaging in data analysis just because technology now makes it possible to collect and interpret the vast amount of data generated each day. The goal is to gain actionable insights into the strengths and weaknesses of every aspect of business operations - from marketing, accounting and human resources to forecasting demand and sourcing materials.

“It’s not that the data drives everything and you believe everything the computer presents to you,” Bayley says. But when combined with your own experience as a leader and the expertise of your team, data analytics can be a powerful tool to help better understand your current situation and past performance, and to shape future strategy.    

“Companies that use data to understand their customers and the landscape they’re working in are able to continuously evolve,” says Bayley. “In the long run, companies that don’t use the data aren’t going to be able to keep up.”

Banking, securities, and insurance, as well as manufacturing, retail, and wholesale are just some of the industries that have already been transformed by data analytics. But the use of data goes beyond just maximizing profits. Data analytics is also being used to improve healthcare outcomes, streamline transportation systems, and help protect the environment. It’s also making sure you can find and stream your favourite movies and music. It is helping your smart thermostat keep your house at just the right temperature.

How to find the data that matters

Data analytics may be reshaping how we live and do business, but Bayley says you don’t need to be a big organization to harness the power of big data.

“Any amount of data that you can analyze can be useful. Just being aware of what’s available and how it could be used is absolutely helpful to a company of any size,” she says. “It really boils down to having a purpose and knowing what you are trying to achieve.”

Regardless of the data you want to collect and the analysis tools you use, she offers these suggestions for leaders looking to make the most of their data.

Know your goals

Asking a computer to analyze data and produce statistical reports and trend lines is one thing. Understanding which data matters to your business and how to best capture the information you need is another.

That’s why it’s so important set clear objectives before you begin any data analytics initiative.

“Take the time to really understand the problem that you are faced with or the objective that you are trying to meet,” says Bayley. “If you don’t have a clear direction, you are really just exploring a huge amount of data and it can be difficult to parse out what’s important.”

Your goals will influence the data you collect, the type of analysis and tools you use, and the insights your data provides, she notes.

Once your objective is clear, make sure your team members understand what it is you are looking to achieve. “That vision has to be set out,” says Bayley. “It’s the leader’s role to ensure that everyone is on board and moving in the same direction.”

Define success

Success can be measured in many ways. Are you looking to increase profits? Acquire new customers? Improve inventory management? Streamline your supply chain? Improve your website performance? Or better understand your competition?

“Knowing what you are going to measure so you can say whether or not you’ve achieved that outcome is also something that should be laid out at the beginning,” says Bayley. “Then it comes down to, what data do we have? What data are available? How can we collect and organize it?”

Establish a process

Any data analytics initiative will only be as good as the data used. If the information you start with is incomplete, inaccurate, biased, or inconsistent, then the insights provided may be invalid.

“Make sure you have a thorough process for how you are going to gather your data,” Bayley advises. “And try to be objective in what you are doing.” Data analysis should always be a team effort, she notes. “It takes team discussions where there is diversity in thought. You can’t rely on a single person to develop something from start to finish.”

Depending on what kind of data is already available and what you are trying to measure, you may need to design a whole new process to collect and analyze the information you need.

“It really is an iterative process. You may start down one path and then realize that you are not getting the information you thought you would and that you need to revisit,” says Bayley. “It absolutely requires the ability and willingness to adapt.”

Understand the limitations

Although data analytics is a powerful tool, Bayley says it’s important to remember that it can’t generate new information. “It summarizes behaviour that is already seen,” she explains. “It is limited by the information we give it and the models that we provide to the computer.”

Meaning, sometimes data can provide us with a lot of information, but it’s still important to consider what information is missing from the picture and what uncontrolled factors could be contributing to the data presented.

“You have to be critical about the data you are collecting to put into the model, and how the model was built,” she cautions. “Ask yourself whether the output matches your expectations, if it’s meaningful, and whether it makes sense.”

This article was written by Nicole Laidler. Nicole is a Western University graduate, BA '03, MA Journalism '04, and an award-winning journalist and content creator. To see what else she’s been writing lately visit www.spilledink.ca.

Additional Resources:

Number of internet and social media users worldwide as of January 2023 - Statista
Data is the new Smart - PWC Advisory Outlook

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