Big Data Analytics are Table Stakes for Digital Age Businesses

Stephen DeAngelis

February 08, 2019

Business executives are always looking for a competitive edge and many have turned to advanced analytics to find that advantage. In the digital age, they often gamble their company’s future on the decisions they make, which is why advanced analytics have become table stakes in business. Table stakes is a gambling term. It refers to a rule requiring a player to bet no more money than he or she had on the table at the beginning of that hand. Sam Ransbotham (@Ransbotham), David Kiron (@DavidKiron1), and Pamela Kirk Prentice (@pamelakprentice) assert companies today almost uniformly come to the table with advanced analytics capabilities. They write, “Competitive advantage with analytics is waning. The percentage of companies that report obtaining a competitive advantage with analytics has declined significantly. … Increased market adoption of analytics levels the playing field and makes it more difficult for companies to keep their edge.”[1] In other words, if you don’t come to the table with advanced analytics you’re probably going to lose because your pile of chips is going to be much smaller than your competitors’ stack.

What do you want from your data?

Data is often described as an asset as valuable as oil or gold. Yossi Sheffi (@YossiSheffi), the Elisha Gray II Professor of Engineering Systems at MIT, asserts data is a company’s most valuable asset. He explains, “The well-worn adage that a company’s most valuable asset is its people needs an update. Today, it’s not people but data that tops the asset value list for companies.”[2] Data, however, is most valuable when it’s analyzed. The question is, analyzed for what purpose? Cory Doctorow (@doctorow) explains, “Patterns emerge in every large dataset, without necessarily being representative of a wider statistical truth. … Big Data is still a useful statistician’s tool, and can be examined to gain intuition that leads to new hypotheses — but those hypotheses then need to be investigated with statistical rigor.”[3] Gary Smith, the Fletcher Jones Professor of Economics at Pomona College, adds, “Good research begins with a clear idea of what one is looking for and expects to find. Data mining just looks for patterns and inevitably finds some.”[4]

What you want from your data determines what kind of analysis you need to apply. George Karapalidis (@gkarapalidis), head of data science at Vertical Leap, explains there are four different types of analytics that can be applied to data depending on the type of data and the desired result.[5] They are:

1. Descriptive analytics. Descriptive analytics can help discover what happened in the past. As Karapalidis puts it, “Before we learn where to go, we need to know where we came from. That’s the key question descriptive analytics solutions tackle.”

2. Diagnostic analytics. Diagnostic analytics can help explain why something happened. Karapalidis writes, “Diagnostic analytics tools help you uncover the root cause of some problems.”

3. Predictive Analytics. “Predictive analytics,” writes Karapalidis, “‘joins the dots’ between the accumulated and analyzed data points, conveying what and why something happened, into models suggesting what can happen next. It indicates the probability of certain outcomes with high accuracy and takes the guesswork out of your decision-making process.” Mark Dunn, Director of Nexis® Data as a Service at Nexis Solutions, adds, “As machine learning and predictive analytics become more sophisticated, companies can base decisions on evidence, and deep learning will push the boundaries even more, with better problem-solving and language comprehension.”[6]

4. Prescriptive analytics. Prescriptive analytics informs you what you should do to achieve a particular outcome. It’s a type of analytics made possible by the emergence of cognitive computing technology. Karapalidis notes, “Prescriptive analytics is yet to move from the margins to the mainstream. It’s an emerging area of analysis attempting to answer the complex question of ‘what actions to take if I want to get outcome A?’ Prescriptive tools come up with multiple future outcomes based on your current/past actions; match those futures with your goal and advise you on the action you need to apply.”

Of course, to extract value using analytics you need the right data for the right model. Obtain the right data, identify a specific problem, and apply the right analytics and you get the results for which you are looking. It’s not as easy as it sounds. Dunn explains, “While most companies recognize the importance of implementing big data initiatives, many still struggle with the execution. Many challenges are organizational in nature — from attracting and retaining data specialists to breaking down organizational data silos to make better use of internal datasets. Nearly 70 per cent of companies have made establishing a data-driven culture a priority, according to a 2017 Harvard Business Review article, but only 40 per cent are hitting the goal.”

Big data analytics use cases

The editorial team at insideBIGDATA observes, “Data intelligence is so comprehensive that you can find a plethora of ways to use it in your everyday business.”[7] To make their point, they identify nine different ways big data analytics can be used to improve business operations. They are:

1. Creating a 360-degree customer view. The team notes, “All organizations want to learn everything there is to know about their customers. Using contemporary analytics, you can discover even the smallest details ranging from demographic features to historical interactions with your company.”

2. Gaining market insights. “Data analytics is so powerful,” the team writes, “it can keep feeding you with real-time insights day after day. Combining valuable information coming from your sales, customer service, and marketing teams, you will be able to determine the best timing for future activities such as product releases or new marketing campaigns.”

3. Detecting new market opportunities. Much of the data being generated today is unstructured. This data is sometimes referred to as “dark data” because, in the past, companies have had a hard time collecting and analyzing it. Today, it’s much easier to leverage that data. The editorial team explains, “Social platforms, emails, websites, and other communication channels represent quality sources of information from which you can detect new market opportunities. This can also help you to design ancillary products or to discover a brand new target group.”

4. Customizing offers. This activity is often referred to as targeted marketing. The editorial team observes, “The Internet is flooded with user-related information, so you can exploit it to create tailored offers that perfectly match the needs of each and every prospect.”

5. Enhancing customer service. On the digital path to purchase, the customer is king. The team notes, “With descriptive analytics at your disposal, you can easily figure out the requirements of your customers and solve their problems before they even ask you!”

6. Improving products and services. “Data analytics is not strictly customer-centric,” the editorial team writes. “On the contrary, you can also use it to analyze products or services, detect pain points, and eliminate features that your customers don’t like.” Analytics can also be used to improve preventive maintenance by predicting potential equipment breakdowns.

7. Mitigating threats. The team notes, “Big data allowed companies — financial organizations in particular — the chance to improve security systems and eliminate a lot of previously unnoticed risks and safety threats. It doesn’t make online businesses 100% safe, but it definitely cuts fraudulent activities to the bare minimum by detecting suspicious behavior and unusual requests or transactions on time.”

8. Optimizing Workflow. HR departments use the state of the art analytics to monitor employee behavior and understand how they work and spend their time throughout the day. … Besides that, HR managers can analyze historical data to improve candidate selection, recruiting, onboarding, compensation packages, lifelong learning, and retention.”

9. Analyzing competitors. The team writes, “Data analytics can follow all sorts of indicators in real-time, including prices, products, online reviews, marketing initiatives, etc. That way, you can predict their future moves and strategic plans, which is great if you want to stay one step ahead and outdo your rivals.”

Although not exhaustive, that list provides a taste of the value that can be unlocked when the right data is analyzed by the right model for a specific purpose.

Concluding thoughts

The insideBIGDATA editorial team concludes, “Data analytics is the cornerstone of the contemporary business. It represents the only way to make accurate and data-driven decisions, thus helping you to maintain the highest level of business performance in the long run.” In other words, big data analytics is table stakes in today’s business environment.

Footnotes
[1] Sam Ransbotham, David Kiron, and Pamela Kirk Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review, 8 March 2016.
[2] Yossi Sheffi, “What is a Company’s Most Valuable Asset? Not People,” Supply Chain @ MIT, 20 December 2018.
[3] Cory Doctorow, “Big Data’s ‘theory-free’ analysis is a statistical malpractice,” boingboing, 11 January 2019.
[4] Gary Smith, “The Exaggerated Promise of So-Called Unbiased Data Mining,” Wired, 11 January 2019.
[5] George Karapalidis, “Examining the four types of big data analytics,” The Drum, 18 December 2018.
[6] Mark Dunn, “Unlocking the potential of Big Data,” ITProPortal, 12 December 2018.
[7] Editorial Team, “9 Ways to Utilize Analytics for Your Business,” insideBIGDATA, 9 December 2018.