“What’s the value of my data?” According to big data expert Bill Schmarzo, “[That is] today’s most critical yet hard to answer question.”[1] Why is that question so hard to answer? After all, everybody assumes data is valuable. Yossi Sheffi (@YossiSheffi), Director of the MIT Center for Transportation & Logistics, asserts data is an organization’s most valuable asset. “The well-worn adage that a company’s most valuable asset is its people needs an update,” he writes. “Today, it’s not people but data that tops the asset value list for companies.”[2] The World Economic Forum has declared data an asset class like oil. And, like oil, data’s greatest value is obtained once it is refined. The refinement process involves both data science and business analytics. Business writer Paramita (Guha) Ghosh explains, “With the rising importance of data as the new oil of a global business environment, data analytics strategy has become a core component of most business operations.”[3]

Unlocking Business Value

Schmarzo dives deeply into the subject of how one can determine the value of data and a full understanding of how he determines such value can only be achieved by reading his articles in their entirety. Nevertheless, he provides some important theorems that underpin his approach. They are:

• One cannot determine the value of one’s data in isolation from the business.

• The value of one’s data is attributed to its ability to improve, increase, reduce, optimize, or rationalize the organization’s key business and operational use cases.

• Organizations don’t fail due to a lack of use cases; they fail because they have too many.

• An incremental use-case-by-use-case approach greatly simplifies the data valuation determination and unleashes the economic value of one’s data and analytics.

Schmarzo makes it clear that the value of data is only unlocked through analysis. But who should do the analysis? Freelance writer James Daniels asserts, “In various business environments today, the term data science seems to carry with it a particular misconception, whereby, it seems to be erroneously equated to business analytics.”[4] He goes on to explain the differences between data science and business analytics; although, his explanation could leave one believing it’s a difference without a distinction.

Data science, he notes, requires data scientists who, “under a business structure, … use data to find solutions and predict outcomes for the business.” On the other hand, Daniels notes, business analytics require business analysts. “Business analysts,” he writes, “serve as a link between business and information technology services, especially since they utilize data analysis to produce insights and make business choices. As a result of this, business analysts work at almost all levels of a business, with their duties comprising of setting project goals and objectives.” The two biggest differences Daniels sees between data science/data scientists and business analytics/business analysts involve data structure and training. He explains:

“While data science primarily makes use of unstructured data, and structured data when it needs to, business analytics needs structured data. Data science is a superset of business analytics and a data scientist can easily transition into business analytics, but the reverse is not the same, as a business analyst will need to learn a lot to transition. In the practice of data science, there is a need for a lot of coding and generally good computer skills, however, this is not needed in business analytics. Data science mostly tackles general and unstructured questions, with no clear-cut answers, but business analytics deals with specific business-related questions that need direct answers and results. Data science requires a vast availability of data to operate, whereas business analytics do not, as they can function with the business aspect alone.”

Thanks to advances in embedded analytics platforms, business leaders no longer need to worry about whether a data scientist or business analyst best fits their company’s needs. As Daniels implies, historically companies have had to assemble a team of at least three experts to address business challenges that can be addressed through analysis. Those experts were:

• A business domain expert — the customer of the analysis who can help explain the drivers behind data anomalies and outliers.

• A statistical expert — to help formulate the correct statistical studies, the business expert knows what they want to study, and what terms to use to help formulate the data in a way that will detect the desired phenomena.

• A data expert — the data expert understands where and how to pull the data from across multiple databases or data feeds.

Having three experts involved dramatically lengthens the time required to analyze, tune, re-analyze, and interpret the results. The Enterra® approach embeds analytical expertise in a cognitive computing system — the Enterra Autonomous Decision Science™ platform. This approach empowers the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Entrepreneur and tech expert Henry Peter (@hpeter5), a member of the Forbes Technology Council, sees great benefits from this approach. He states, “A creative way of leveraging business intelligence and insights … derived from machine learning … is to connect those insights to actionable layers of business operations. This includes notifications and an automated flow of actions that can inform many of the business stakeholders, leading them to act on the gained insights.”[5]

The Analytics Payoff

Schmarzo may be correct when he claims determining the value of data is difficult; nevertheless, that hasn’t stopped business leaders from pursuing advanced analytics solutions. Finance journalist Umme Sutarwala (@umme_sutarwala) reports a study conducted by IDC concluded, “Global big data analytics (BDA) spending [will] expand at a nearly 13% Compound Annual Growth Rate (CAGR) until 2025.”[6] She suggests there are good reasons business executives are spending on BDA and boosting income. They are:

• Managing operational risks. According to Sutarwala, today’s digitally-driven world has opened up businesses to new operational risks like fraud, data breaches, and ransomware attacks. As a result, she notes, “An entire company can be compromised with just a few keystrokes.” Analytics can help detect and mitigate unusual activity. Analytics can also help manufacturers with other types of risks, like monitoring equipment for impending failures.

• Improved performance. Sutarwala asserts, “Data is what fuels productivity in the workplace. Data has been proven to drive efficiency in both individual and team performances. … Furthermore, data enables management to discover areas for improvement while also fostering an accountability and transparency culture. Senior employees can back up their strategies with data analysis.” Enterra’s advanced analytics solutions have helped clients achieve ROIs of over 1000%.

• Better sales and marketing. Over a century ago, John Wanamaker, the late department store magnate, lamented, “Half the money I spend on advertising is wasted. The trouble is I don’t know which half.” Advanced analytics can help companies better understand their sales and marketing spend. Sutarwala notes that today’s advanced analytics platforms can monitor operations, fraud, compliance, and customer behavior along with other pertinent data. Analyzing all of these variables can help improve sales and marketing efforts. Sutarwala notes, “Even though customer analytics is the most crucial of the lot, focusing on the other elements has resulted in a substantial rise in revenue per customer.” Advanced analytics solutions, like the Enterra Shopper Marketing and Consumer Insights Intelligence System™, are capable of analyzing many more variables than was possible in the past.

• Enhanced situational awareness. The business landscape is constantly changing and relying on historical data to make future decisions can be risky. It’s like steering a ship by looking only at its wake. This became abundantly clear during the pandemic, which is why my company created the Enterra Global Insights and Decision Superiority System™. As Sutarwala notes, “In order to meet these problems, businesses are turning to Artificial Intelligence (AI) and automation.”

Concluding Thoughts

Technology writer Sofia Peterson observes, “Big data analytics has a greater use in the business world than we credit it for. It provides deep insights into trends and patterns, which can help the decision-makers get immediate answers to their problems. It helps them maximize their brand value by using tools created by big data analysis, machine learning, AI, data mining, and much more.”[7] She adds, “The use of such valuable applications helps businesses understand what their customers want and how their competitors are gaining ground, which is valuable for organizations to identify underlying operational inefficiencies.”

At the very least, Schmarzo concludes, “Organizations can realize three effects or benefits from the sharing, reuse, and continuous refinement of the organization’s data and analytic assets.” Those benefits are: 1) A reduction in marginal costs in each subsequent business and operational use case through the reuse of data and analytic assets; 2) growth in marginal value as the reuse of the data and analytic assets shrinks time-to-value and de-risks each subsequent business and operational use case; [and] 3) accelerated growth in economic value through the continuous refinement of the analytics data and analytic assets, which ripples predictive improvements through all the previous use cases that used those same data and analytic assets.” Isn’t it time you unlocked the value in your company’s data?

Footnotes
[1] Bill Schmarzo, “What’s the Value of my Data? Today’s Most Critical Yet Hard to Answer Question,” Data Science Central, 25 July 2022.
[2] Yossi Sheffi, “What is a Company’s Most Valuable Asset? Not People,” LinkedIn, 19 December 2018.
[3] Paramita (Guha) Ghosh, “Developing a Data Analytics Strategy,” Dataversity, 14 June 2022.
[4] James Daniels, “The Difference Between Data Science And Business Analytics,” MIT Tech News, 15 June 2022.
[5] Forbes Technology Council, “14 Creative Ways Companies Can Leverage Business Intelligence And Machine Learning,” Forbes, 21 October 2020.
[6] Umme Sutarwala, “Four Ways Big Data Analytics Can Add to Profits,” Enterprise Talk, 16 June 2022.
[7] Sofia Peterson, “5 Hard-to-Ignore Benefits of Using Big Data Analytics in Business,” The Hack Post, 3 January 2022.