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Big Data Analytics and the Future of Business

July 28, 2021


You read a lot about the Digital Age, digital transformation, and big data. The Digital Age has also been called the Information Age; however, there is a slight difference between the two depictions of the age in which we live. The Digital Age describes a world in which information is digitized. And digitized information is perhaps a company’s most valuable asset. Journalist Adilin Beatrice explains, “In the digital world we live in, data is increasingly becoming the most valuable asset for organizations.”[1] Yossi Sheffi (@YossiSheffi), Director of the MIT Center for Transportation & Logistics, asserts big data already 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] 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.”[3]


Of course, not all information is digitized, which is why the Information Age has a bit broader meaning than the Digital Age. Increasingly, however, its information locked within datasets and can be mined for insights that is the focus of today’s businesses. New knowledge and actionable insights are what give digital enterprises a competitive edge. Adlina A. Rahim (@RahimAdlina) explains, “Big data doesn’t answer critical questions that enterprises have, but rather, it provides them with new information that can prompt new questions that enterprises haven’t thought of asking. Once enterprises have a better idea of what they want to find out, they can turn to [business intelligence] tools to deliver the insights, and even make predictions.”[4] Better insights result in better decisions; and, as Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), report, “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.”[5]


What is Big Data Analytics?


Rebecca LeBoeuf, a content writer at Southern New Hampshire University, writes, “Data analytics focuses on collecting, inspecting, cleaning, summarizing and interpreting collections of related information.”[6] Staff writers at CIO Review write, “The field of big data analytics is vast and incredibly flexible. Big data can be structured or unstructured, and it typically refers to massive  amounts of data that must be processed. Big data, according to Data Management experts, is a massive overwhelming volume of information.”[7]


Susan McKenzie, senior associate dean at Southern New Hampshire University, told LeBoeuf, “Organizations are gathering, analyzing and leveraging more data than ever before to ensure that their decisions are data-driven. … The era of big data has arrived and changed the role of analytics in every aspect of our lives. This creates the need to (expand) traditional data-handling tools and storage to process and store the data based on volume, speed, structure, accuracy and value.” As noted above, data can be turned into new knowledge insights by leveraging advanced analytics. Mitul Makadia (@mitulmakadia), founder of Maruti Techlabs, adds, “The more you understand your data, the better business decisions you are able to make. As the name suggests, big data analytics refers to managing and analyzing large data sets. Big data analytics helps in extracting insights and uncovering patterns from large pools of complex data.”[8]


The Importance of Big Data Analytics


Vivek Kumar, the Content Lead at Analytics Insight, notes, “Big data analytics enables businesses to harness their data and use it to identify new opportunities, it leads to smarter business moves with efficient operations and higher ROI.”[9] He goes on to describe the four primary types of analysis businesses can use to benefit their operations. They are (starting with the most advanced type):


Prescriptive Analytics: “This data analytics concept prescribes what action to take to remove future problems or capitalize on a promising trend. Prescriptive analytics essentially provides an organization with a laser-like focus to answer a specific question. It also helps them to determine the best solution for a future opportunity or avoid future risks.”


Predictive analytics: “[Predictive analytics] uses big data to identify past patterns to predict the future. Predictive analytics draws its power from numerous methods and technologies, such as big data, data mining, statistical modeling, machine learning and assorted mathematical processes, among others. By utilizing this model, an organization can use past and current data to reliably forecast trends and behaviors.”


Descriptive analytics: “This data analytics method provides insight into what has happened historically and will provide businesses with trends to get in-depth detail. Descriptive analytics defines as a preliminary stage of data processing that creates a summary of historical data to yield meaningful information and possibly prepare the data for further analysis.”


Diagnostic Analytics: “With this analytics technique, historical data can be measured against other data to answer the question of why something happened. Essentially, data scientists turn to this technique when trying to determine ‘Why’ behind something happened. Diagnostic analytics can be beneficial in the sales cycle, for instance, to categorize customers by their likely product preferences and sales cycle.”


The CIO Review staff observes, “Almost every industry now recognizes the value of big data analytics. The relevance of big data in every sector is undeniable, ranging from trends, market analytics, user preferences to competitor analysis and business analysis.”


Concluding Thoughts


A couple of years ago, Asim Rais Siddiqui (@asimrs) Co-Founder & CTO at TekRevol, rhetorically asked, “Does your business really require big data analytics?”[10] His unqualified answer was, “Yes.” Siddiqui concluded, “Effective incorporation of big data analytics, once paired with management wisdom, has the potential to transform businesses for the better. To keep up with the pace of today’s business world, big data analytics is no longer an option, but a necessity.” As Mankins and Sherer asserted, decisions matter and advanced analytics help companies make consistently better decisions.


[1] Adilin Beatrice, “Big Data vs. Data Science: Knowing the Difference by Undoing the Knots,” Analytics Insight, 5 March 2021.
[2] Yossi Sheffi, “What is a Company’s Most Valuable Asset? Not People,” LinkedIn, 19 December 2018.
[3] Staff, “Big data is oxygen to business,” Gadget, 4 October 2019.
[4] Adlina A. Rahim, “How big data is empowering better business intelligence,” Techwire Asia, 24 March 2020.
[5] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[6] Rebecca LeBoeuf, “What is Data Analytics?” Southern New Hampshire University, 3 June 2021.
[7] Staff, “Importance of Big Data Analytics in Different Industries,” CIO Review, 25 June 2021.
[8] Mitul Makadia, “10 Key Technologies That Enable Big Data Analytics For Your Business,” Customer Think, 4 June 2021.
[9] Vivek Kumar, “Big Data Analytics: What Is It and Why [It’s] So Important?” Analytics Insight, 22 November 2021.
[10] Asim Rais Siddiqui, “Does Your Business Really Require Big Data Analytics?” Customer Think, 26 August 2019.

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