The future is analytical

“We live in a world flooded by data”! There is nothing new in this sentence and maybe you are even bored with this claim. Major magazines have discussed this subject on its covers. There’s no forgetting the 2010 cover of The Economist magazine with the heading “The Data Deluge”. Also, the Harvard Business Review which presented the “Getting Control of Big Data” headline in 2012.

The human species evolved using calculations, measurements, and counts. The truth is that data has always been part of us as a society and companies have made and continue to make their decisions based on them. Even though this data was often inaccurate, outdated, or portrayed as a sample of the population, organizations around the world used them to create some logical reasoning that supported the executives’ decision.

But what has changed since then? Perhaps three things impacted this change:

1. The processing capability of our computers. In 1994, the fastest computer did not reach 1 giga flops*/s and currently it passes the 100 peta flops/s. There is no longer a need for departments to provide a dedicated computer 24 hours a day in the unique task of processing the data base.

*Flops = Floating point operation per second (INSTITUTO DE MATEMÁTICA E ESTATÍSTICA — USP, 2019).

2. Artificial Intelligence becomes a reality for companies. Back in 1956, John McCarthy was dedicated to producing intelligent machines. His dedication and other scientists over the years has led experts to claim that by 2050, the intelligence of machines will equal a billion human brains (Kurzweil, 1999).

3. According to Statista, more than 3.6 billion people currently have access to the Internet. This number represents an increase of 249% compared to 2015 (Statista, 2019). Every day this number of the population shares their information, buys products, sends messages, shares opinions, among other numerous tasks in the virtual world at the same time.

These developments have allowed organizations to process large volumes of data in real time and even predict upcoming customer actions. Companies around the world and from diverse markets are able to bring all the studies of statistics together and take all the calculations to another level. Now, they have the power to process and analyze data in real time, in the online or offline world. As Jim Sterne states in his book, Artificial Intelligence for Marketing: “Artificial intelligence is a machine pretending to be a human. Machine learning is a machine pretending to be a statistical programmer.” (Sterne, 2017).

Since systems are doing the heaviest and most tedious work, for humans there is the most important and difficult task: to analyze. With all this data circulating in companies and powerful systems to interpret them, companies continue the great challenge of analyzing and making data-driven decisions, like the big executives of years ago. The amount of data does nothing to impact our ability to analyze. Companies do not become more analytical because they now have more data at their disposal. In fact, the opposite is happening, people are not being able to tell stories through the data or make decisions precisely because now this volume of data bases is much larger.

Since systems are doing the heaviest and most tedious work, for humans there is the most important and difficult task: to analyze.

Great scholars on this subject have been warning companies about the importance of being analytical. As an example, Peter Sondergaard who warns us that “information is the oil of 21st century, and analytics is the combustion engine” (Kotorov, 2020). Data without analysis is just a tangle of numbers and disconnected texts that say nothing about anything. To reinforce the importance of analytics in organizations, in the early days of 2019, LinkedIn shared a survey with the results of hard and soft skills that will be more in demand by companies. Analyzing the hard skills, in the third position appears the analytical reasoning skill and the business analysis appears in sixteenth place.

Nowadays, in many cases, business intelligence (BI) still involves analysts writing SQL queries to analyze large data sets so that they can provide intelligence for non-technical executives. But specialists believe in the fourth wave of BI. In this fourth wave, artificial intelligence (AI) will democratize analytics so that any line of business specialist can supervise more insightful and prescriptive recommendations than ever before. In other words, the traditional order of BI will be inverted (Sloane, 2021).

Currently, the traditional method of BI starts with a technical analyst investigating a specific question, analyzing the data, and present the insights for business decision-makers. In the future, the AI-enabled platforms that will define the fourth wave of BI will start by crunching and blending massive amounts of data to find surface patterns and relevant statistical insights. However, a data analyst applies judgment to these insights to decide which patterns are truly meaningful or actionable for the business. After digging into areas of interest, the AI will suggest potential actions based on correlations that have been seen over a more extended period — again validated by human judgment.

More and more businesses are increasingly feeling the strain that business complexity and data proliferation are putting on their traditional BI processes. The time to make decisions empowered by AI is now. AI advancements are coming online in conjunction with the growth of cloud-native vendors.

The role of human capital in a world of data is becoming increasingly clear. The Artificial Intelligences that empower the data, are demanding more and more from the analytical ability of humans. And so, the job market has excellent programmers who do not understand how data impacts business and excellent business professionals who do not understand which data is correlated to another. It is necessary to develop the analytical profile of the team so that the company can transform data into decision making.

If companies want to stay alive in a world flooded by data, having an excellent computer with high processing capacity and the most modern BI system on the market is no guarantee of survival. It is mandatory to have a team with analytical capacity to understand the data and make assertive decisions for the company. The positive side of this scenario for companies with low analytical power is that hard skills — such as analytical reasoning — are easier to define, learn, and measure than soft skills.



Kotorov, R. (2020). Data-Driven Business Models for the Digital Economy. Business Expert Press.

Kurzweil, R. (1999). The Age of Spiritual Machines. New York: Penguin Group.

Sloane, S. (2021, April 03). Venture Beat. Retrieved from

Statista. (2019, April 12). Retrieved from

Sterne, J. (2017). Artificial Intelligence for Marketing. New Jersey: John Wiley & Sons, Inc.

Written by Ligia Galvão, Msc

Especialista em Inteligência de Mercado. Atualmente é mestranda da Pace University de Nova York no curso Customer Intelligence & Analytics.