Data analytics: Is it working for your company?

With data analytics, entrepreneurs can go beyond what they think they know about their industry or their business, not to mention their competitors (Shutterstock/GaudiLab)

“Organizations have no idea that they’re sitting on a treasure trove of data,” says data scientist Michael Albo. 

He should know. The CEO and co-founder of the Data Science Institute (DSI) works with SMEs and multinationals to analyze, and more importantly, analyze data to identify business opportunities—an effective strategy for anyone interested in fully participating in the digital and data-driven economy.

Companies use data analytics for many purposes, including developing new marketing policies, calculating the probability of converting prospective clients, and identifying which clients are likely to cancel a service or purchase additional products based on their search histories or past interest shown.

With data analytics, entrepreneurs can go beyond what they think they know about their industry or their business, not to mention their competitors. 

“Entrepreneurs often have an accurate, but limited, view, factoring in only five or six variables,” explains Albo. “All too often, they simply end up with colourful graphs and charts. Nothing more.”

THE DATA MARKETPLACE

By taking massive amounts of data into account, algorithms can make a big difference. The company provides the primary data (from Enterprise Resource Planning, Customer Relationship Management, or other systems), which is then enhanced by combining it with external data from free open databases (e.g. Données Québec, Ontario’s Data catalogue or the Government of Canada’s Open Data).   

But there’s also a huge subscription database market, Albo adds. “People would be surprised to know what data is being sold,” he says. “There’s search or usage data, of course, but also images, videos (including surveillance) and audio recordings.” Without wanting to elaborate further, Albo said that data brokers or companies sell pretty much anything that can be recorded.

In fact, some organizations have turned this data into a second business model, explains Philippe Nieuwbourg, a French independent analyst specializing in information technology. 

“In the past, everything was based on products sold,” he says. “Today, a company’s value is derived from the data generated by its products, which could ultimately become free.” Just think about all those inexpensive, connected devices that are everywhere. [See Big data collection raises key questions about privacy, ethics] 

“Just like anyone can buy anonymized private information on a specific subject from Google or Facebook,” Albo adds, “entrepreneurs can buy anonymized credit card data from competitors to know, for example, when transactions take place and for what amounts.” 

USING DATA EFFICIENTLY   

Of course, “anonymization” comes with its share of challenges, as became evident at last fall’s Canada FinTech Forum in Montreal. For instance, when you cross-check certain information, anonymity can be lost. Take, for example, Canadian postal codes, which in some cases correspond to a single household. 

The more specific the data, the more expensive the sample. But the benefit justifies the cost—and some have been leveraging data analytics for some time. For example, to validate future investments, hedge funds no longer simply analyze a series of securities prices, but examine a wide range of information on those securities as well. So before investing in a retail chain, analysts will look at satellite photos of parking lots to count the number of cars at different times to predict customer traffic, estimated revenue and other data. 

“There’s been a shift to purely predictive models,” says Albo. “Some can even predict dips in corporate results, when shares can be bought at a discount.”

MOVING FORWARD

In an increasingly tech-driven economy, data provides nuances that were once hidden, affording a competitive advantage to businesses that are first to utilize the information. 

Take Canadian company Lasik MD for example. With more than 30 laser vision correction clinics nationwide, the business was looking to boost the occupancy rate of its expensive facilities. Lasik MD turned to DSI to develop an algorithm to increase its patient conversion rate. Because the first consultation is free, there are many potential candidates for surgery—the trick is to convert these prospects into patients. 

Using close to 300 variables—ranging from vision condition, surgery type and financing method to patient age and gender—the DSI team created a model that predicts with almost 90 per cent accuracy which patients will ultimately get surgery. 

“By maximizing clinic occupancy through a higher conversion rate, Lasik MD increased its profitability, thereby raising the cash needed to expand and acquire competitors,” explains Albo. “Good data analysis produces such reliable information that you can use it with your eyes closed.” 

That’s why Nieuwbourg says it’s important to focus on the data.

“It’s pointless to invest in artificial intelligence or data science if you haven’t invested in your data first—that includes collection, but management and protection as well,” he says. “Companies need to start with the basics, such as source, storage, structure, legislation and possible uses. Those that don’t take care of their data will decline in value.”

LEARN MORE ABOUT DATA

You can purchase the CPA Canada Guide to Audit Data Analytics, which is designed to assist and encourage practitioners to make use of technology-based data analytics in the audit of financial statements

And read CPA Canada’s report on How are big data and AI transforming accounting and finance? to learn—from a CPA’s perspective—about how these disruptors may impact the profession.