In the Digital Age, nothing in the business world is more important than data. Alex Toma (@alexbreakline), a digital marketing specialist, writes, “Data has always been fundamental to the success of digital.”[1] Most business leaders understand that so-called “big data” is an asset for their company’s operations. The World Economic Forum has declared big data an asset class like gold or oil. Staff writers at Gadget go even further. “With the Digital Revolution in full swing,” they write, “data is to business what oxygen is to mankind. It is a crucial resource — one that enables companies, especially smaller ventures, to make better business decisions, grow their customer base, keep their competitive edge and secure funding.”[2] I’m not sure why they stated data is “especially” important for smaller ventures. Data is important for every size of business.

The Gadget staff is right about one thing: Big data does help businesses make better decisions. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[2] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”

Good decision-making starts with good data

Simply talking about “big data” or data in general, doesn’t really provide the nuance companies need to understand which kind of data is most useful for their needs. Gabriela Gavrailova, a Product Marketing Associate for Devs, notes, “[Each type of data contains] useful information that you can mine to be used in different projects.”[3] She explains what constitutes different types of data.

• Structured data. “Structured data is fixed-format and frequently numeric in nature. So, in most cases it is something that is handled by machines and not humans. This type of data consists of information already managed by the organization in databases and spreadsheets stored in SQL databases, data lakes and data warehouses.”
• Unstructured data. “Unstructured data is information that is unorganized and does not fall into a predetermined format because it can be almost anything. For example, it includes data gathered from social media sources and it can be put into text document files held in Hadoop like clusters or NoSQL systems.”
• Semi-structured data. “Semi-structured data can contain both the forms of data such as web server logs or data from sensors that you have set up. To be precise, it refers to the data that, although has not been classified under a particular repository (database), still contains vital information or tags that segregate individual elements within the data.”

Gavrailova adds, “Big Data always includes multiple sources and most of the time is from different types, too. So knowing how to integrate all of the tools you need to work with different types is not always an easy task.”

The right data might range from thin to thick

We live in an age when many people believe “more is better.” This sentiment can be traced all the way back to an ancient Greek philosopher named Eubulides, who noted, “A quantitative change in the number of grains of sand leads to a qualitative change in being a heap or not.”[4] However, quantity is not always better than quality. Stephanie Overby (@stephanieoverby) writes, “Most business leaders have a reasonable understanding of big data, but some significant misunderstandings persist. The first, and perhaps most damaging, is the assumption that all big data has business value.”[5] Todd Wright, head of data management at SAS, told Overby, “The term ‘big data’ leads many to assume that value is derived simply from the sheer amount of data that an organization holds, and the organization that has the most data wins. The true value comes from how an organization can get a broader view of their customer and business by tapping into different and previously unused data sources. That in turns leads to more educated and informed decisions with the use of analytics.”

Kimberly A. Whitler (@KimWhitler), an Assistant Professor at the University of Virginia’s Darden School of Business, explains, “‘Big data’ isn’t as important as ‘smart data’ or the ‘right data.’ Companies are getting excited over the notion of big data, but it’s ultimately only as good as the insights you get out of it.”[6] According to Andy MacMillan (@apmacmillan), CEO of UserTesting, companies sometimes even use the right data to do the wrong thing because they don’t include “thick data.”[7] To explain what “thick data” is, he writes about a friend who went into his local supermarket only to discover the store’s floorplan had been completely rearranged. He continues:

“Here’s what I’m guessing happened: The large chain used big data analytics to extract insights about shopping patterns and decided it could create a more appealing environment and raise sales by, say, putting the fresh flower stand near the entrance and positioning the pasta and tomato sauce aisle near the wine. That’s fine, but here’s what apparently didn’t happen: The company failed to use thick data — non-numerical, qualitative knowledge of customers’ emotions, goals, and behavior. And that’s how it missed the realization that resetting the store during a global pandemic, forcing already on-edge shoppers to spend time and stress hunting for items, wasn’t exactly a great idea. … Far too many companies rely solely on what big data is telling them about their customers, and fail to see threats and opportunities right in front of them. It’s a mistake to lean too heavily on quantitative data to understand buyers’ desires, needs, and expectations while forsaking the rich qualitative information derived from common sense, intuition, and talking directly with customers.“

MacMillan’s point is a fair one. It also underscores the importance of knowing what data is important for making business decisions. It’s a lot more complicated than simply gathering data and letting a computer loose on it. MacMillan concludes, “Despite all of big data’s benefits, it tells only half the story. What it can’t do is capture context — the many nuances around customer experience that can come only from observing and interacting with customers first-hand. Big data can be terrific at providing the what, when, where, and how of customer interactions with a brand, but not the why.” In some, but not all, cases, it’s true that big data doesn’t capture context. Many companies do extract context from unstructured social media data or web comments. Overby concludes, “Business leaders should understand that having more data from more sources is of little to no value without a plan for how the data will be used and a goal for what they want big data to accomplish. … Do they want to predict customer behavior? Map manufacturing trends? Improve sales with better targeting and messaging? Make better hires? Only then can they create a big data strategy — including people, process, and technology ­— to achieve those aims.”

Footnotes
[1] Alex Toma, “Why Data is the New Kid on the Digital Block,” Business2Community, 7 July 2020.
[2] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[3] Gabriela Gavrailova, “Big Data: What Is It and How Does It Work?” Business2Community, 10 December 2019.
[4] Nils Barth, “Who said, ‘Quantity has a quality all its own’?“, Quora, 11 September 2015.
[5] Stephanie Overby, “How to explain big data in plain English,” The Enterprisers Project, 16 October 2019.
[6] Kimberly A. Whitler, “Stop Focusing On Big Data And Start Focusing On Smart Data,” Forbes, 20 August 2019.
[7] Andy MacMillan, “Why Big Data Isn’t Enough for Businesses–They Need Thick Data Too,” CTOvision.com, 20 October 2020.