Got Data?

Stephen DeAngelis

September 27, 2021

Nearly two decades ago, the dairy industry launched a famous advertising campaign with the logo “Got milk?” The campaign featured advertisements in which people found themselves in desperate need of a glass of milk. The first Got Milk? advertisement was created by a young Michael Bay, before he became a celebrated filmmaker. The commercial featured a historian who received a call from a radio quiz show. All the historian had to do to win a $10,000 prize was answer the trivia question “Who shot Alexander Hamilton in a famous duel?” To set the scene, Bay placed the historian in a room full of memorabilia dedicated to the duel. The excited historian correctly answered the question by saying “Aaron Burr”; unfortunately, his answer was muffled thanks to a mouthful of peanut butter sandwich and no milk to wash it down. Unable to understand the historian’s answer, the DJ promptly hung up on him.

 

In the Digital Age, businesses are often asked, “Got data?” However, most businesses are choking in data. The more important question is, “Got analytics?” Frank Anthony Wilczek (@FrankWilczek), an American theoretical physicist, mathematician and a Nobel laureate, observes, “Big data doesn’t interpret itself. Making mathematical models, trying to keep them simple, connecting to the fullness of reality and aspiring to perfection — these are proven ways to refine the raw ore of data into precious jewels of meaning.”[1] Today, the amount of data businesses must analyze is so great, it can only be analyzed fast enough to be useful by some form of cognitive technology (aka artificial intelligence (AI)).

 

Extracting Value from Data

 

“For over a decade now,” writes journalist Phil Britt (@Phil_Britt), “companies have been capitalizing on the three V’s of big data: volume, variety and velocity. The ready availability of customer data — in theory — translated into a goldmine of insights for businesses.”[2] He added the caveat “in theory” because a recent report from Teradata concluded, “Even though companies collect ever-increasing amounts of data on customers, many still have a difficult time discerning how to use that data to provide better [customer experience]. More specifically, though 82% of respondents said their firms are trying to collect more types of customer data, 61% went on to admit that capturing and making sense of digital customer data is difficult for them and 55% said understanding customers across all touchpoints and lifecycle stages is challenging.”

 

Analysts from Dell, ask another important question: “Does your organization extract enough value from valuable data?”[3] They go on to note, “In sectors including finance, manufacturing, telecoms, and pharma and healthcare, many organizations struggle to manage their ever-increasing volume of data and convert it into insights that help them innovate, create value, and lead their competition — in essence, to turn their big data into big decisions.” They call this the “data paradox.” In a study commissioned by Dell, Forrester analysts found, “67% of organizations … said they need more data than their capabilities can provide, yet 70% are already bringing in data faster than they can process and analyze it. The pandemic has only intensified this dilemma as the growing on-demand economy generates more data for organizations whose skills, culture, and infrastructure cannot always keep up.”

 

Like many analysts before them, the Dell analysts insist, “To tap its greater utility, organizations need to rescue their data from traditional silos and legacy infrastructure and share it widely, opening the way to greater predictive accuracy and more meaningful discoveries.” Bertrand Moingeon (@bertandmoingeon), a Professor of Strategic Management at HEC Paris, asserts, “Organizational silos are without a doubt the most widespread managerial structure, even though all management textbooks warn against them. This is true for all kinds of organizations, be they businesses, public bodies or non-profit organizations.”[4] Visionary leaders understand that silos are barriers to growth because data is the lifeblood of a digital enterprise. Restricting the flow of data from throughout an organization can be crippling. As Mark Adams, regional sales director for UK and Ireland at Cohesity, bluntly states, “Data is — and will continue to be — the key to digital business.”[5] If that key is going to open up opportunities for digital enterprises, data must be available to everyone who needs it.

 

What Kind of Analytics Does Your Business Require?

 

Making data widely available is important to the modern enterprise and so is making the right analytics available to the right people. Software architect Lee Atchison (@leeatchison) observes, “There are many types of analytics that modern applications need to monitor and examine. The purpose, value, accuracy, and reliability of those analytics vary greatly depending on how they are measured, how they are used, and who makes use of them.”[6] He adds, “There are essentially three classes of analytics with radically different use cases.” He calls these Class A, Class B, and Class C analytics.

 

Class A analytics. According to Atchison, “Class A analytics are metrics that are application mission-critical. Without these analytics, your application could fail in real time. These metrics are used to evaluate the operation of the application and adjust how it is performing and dynamically make adjustments to keep the application functioning. The analytics are part of a feedback loop that constantly monitors and improves the operational environment of the application.” The people most likely to use Class A analytics, Atchison writes, are those dealing with automated systems. The analytics, he explains, “are used internally by systems and processes. They are used to dynamically and automatically update critical operational resources in order to keep a system healthy and scaled appropriately.”

 

Class B analytics. “Class B analytics,” he writes, “are metrics that are not business-critical, but are used as early indicators of impending problems, or are used to solve problems when they arise. Class B analytics can be important for preventing or recovering from system outages. Class B metrics typically give insights into the internal operation of the application or service, or they give insights into the infrastructure that is operating the application or service. These insights can be used proactively or reactively to improve the operation of the application or service.” He adds, “Class B metrics are mostly consumed by operations and support teams, along with development teams, as part of the incident response process. They can provide immediate assistance to teams in identifying and fixing problems, and generally help in preventing problems before they occur.”

 

Class C analytics. Atchison explains, “Class C analytics involve metrics that are used for offline application analysis and longer term planning purposes.” As you might imagine, “Class C metrics are mostly consumed by business planners, product managers, and corporate executives. They are used to drive longer term business decisions, business modeling, product design, and feature prioritization.”

 

Atchison concludes, “Using the right metric for the right purpose will increase the usefulness of your analytics.” William Beresford (@wberesfo), co-founder and Chief Strategy Officer for Beyond Analysis Group, suggests there are “five areas where the benefits of implementing big data technologies and putting data to work can typically be found.”[7] They are: 1. Identifying opportunities for growth; 2. Developing product design and innovation; 3. Shaping the customer experience; 4. Generating operational efficiencies; and, 5. Enhancing risk management.

 

Concluding Thoughts

 

To be successful in the Digital Age, your enterprise needs to understand what kind of data needs to be collected, what type of analytics needs to be applied to that data, and who needs access to the generated insights. As Britt notes, “An ongoing challenge organizations face today is what we call ‘better data, not big data.’ … To identify better data, you need to develop a well-scoped overview of the business problem [you are trying to solve].” Scrutiny of how businesses collect and handle data is intensifying. That means having data can be a liability if not handled well. Got data? is only the first question businesses need to answer. How they use the data is the more important question.

 

Footnotes
[1] Frank Wilczek, “Big Data Doesn’t Interpret Itself,” The Wall Street Journal, 5 September 2019.
[2] Phil Britt, “Are You Making the Most of Customer Data?” CMS Wire, 21 June 2021.
[3] Dell Staff, “Is Your Organization Extracting the Value in Your Valuable Data?” Harvard Business Review, 29 June 2021.
[4] Bertrand Moingeon, “Transversal management: how to break out organizational silos,” LinkedIn, 1 April 2017.
[5] Mark Adams, “Why the future of data management requires us to remove fragmented thinking,” Data Center Dynamics, 10 May 2021.
[6] Lee Atchison, “The importance of classifying analytics,” InfoWorld, 19 July 2021.
[7] William Beresford, “How businesses can grow with data analytics and predictions for the future,” ITProPortal, 10 August 2021.