What is the Big Data Moment of Truth Model and its Decision Making Quadrants?

In this new era where Big Data is starting to have a big impact on economy; something I called Bigdatanomics in my doctoral thesis (SARSAR, 2015); practitioners are seeking new levers to drive fast (h1), proactive (h2) and or better decisions (h3).  


Figure 1: Big Data Driven Decisions Model


BDMOT (Big Data Moment of Truth) Is the art of mastering data insights to make fast (h1), proactive (h2) or strategic (h3) business decisions by using big data analytic tools throughout the full cycle of the decision-making process.

The best way to understand the impact of this Big Data model is to combine it with the ZMOT theory model (developed by google).

The following figure describes the idea regarding the impact of BDMOT on ZMOT:


Figure 2: Big Data Moment of Truth influence (BDMOT on ZMOT)

From the figure above, we can say that wining the Zero Moment of Truth relies on mastering the data insights to make business decisions. In other words, we assume that nowadays, winning the ZMOT relies on Big Data Analytics.

It is clear that in the near future, practitioners and leaders will rely on big data analytic tools to win BDMOT and consequently drive better decisions. 

By referring to the multiple ascertainments and findings developed in the research conducted with the Paris school of Business (SARSAR, 2015) . A Big Data Decision-making quadrant (figure 3) was derived. A multi variants’ quadrant based on the type of decisions (h1, h2 or h3), the difficulty of the decision (less risk to high risk) and the hierarchy (Low Management to Executive management) of the decision maker.


Figure 3:  BDMOT Decision-making Managerial Quadrant


  • The quadrants show how BDMOT is involved in the Decision-making process with regards to hierarchy, authority and financial impact.
  • Leaders and managers make a range of decisions from low risk (Operations & tactical) to high risk (strategic).


So, When you compare the financial impact of a decision to the level of authority of a decision maker, the result illustrates that the higher the level of authority (hierarchy) the less the decisions are automated (less logarithmic).

When the decision is strategic, BDMOT plays a role in providing reliable insights and multiple scenarios so that the decision maker can use his intuition and experience to make the best choice.

For example:

  • BDMOT allows practitioners narrow the segmentation of customers and therefore deliver much more precisely tailored products or services.
  • BDMOT might help organizations manage more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days and therefore expose variability and boost performance. In fact, some leading companies are using their ability to collect and analyze big data to conduct controlled experiments to make better management decisions. 
  • BDMOT allows practitioners (organizations) test hypotheses and analyze results to guide investment decisions and operational changes. In effect, experimentation can help managers distinguish causation from mere correlation, thus reducing the variability of outcomes while improving financial and product performance.

For tactical and day-to-day decisions, BDMOT can play a role in automating decisions based on accurate financial and marketing inputs.

Hereafter a detailed view on the Decision-making quadrants:

Quadrant A: Low Management & Low Risk

The quadrant “A” is where practitioners might rely on Big Data analytics to have a highly automated decision-making process. Some of the applications could be algorithmic pricing, Location-based data, algorithmic marketing, and preventive alerts ...etc.

Quadrant B: Low Management  & High Risk

In this “B” quadrant, the financial risk is very high but top executives are less involved. In this case BDMOT can play an important role in mitigating the risks automatically and provide Business and financial insights. So that, managers can choose safely the best decisions.

For example, now casting, the ability to estimate metrics such as consumer confidence, immediately, something that previously could only be done retrospectively, is becoming more extensively used, adding considerable power to prediction. Similarly, the high frequency of data allows users to test theories in near real-time and to a level never before possible.

Quadrant C: High Management & Low Risk

In this “C” quadrant, risk is low and managers & executives in the top hierarchy decide by unlocking significant value and minimizing risks. In essence, major business decisions that have strategic consequences need nowadays to involve better big data insights to inform human expertise.

Quadrant D: High Management & High Risk

In this quadrant “D” we find high-level executives making financial & strategic decisions. In fact, for strategic and big decisions, big data needs human involvements to make sense of it. However, the process of analyzing data also introduces a lot of biases that managers and executives bring to bear, particularly when looking at big data sets. With a large enough data set, it is possible to find correlations that much among almost all the variables. An unwary executive may only notice the correlations that match their existing beliefs (confirmation bias), or they may only look for correlations that they have seen recently or many times before (availability bias).

Even before these biases are applied, the design of a dashboard will undoubtedly rely on a judgment made by someone else, lower down in the company, about the relationships between data. This is not wrong, but users relying on this pre-packaged data should know what assumptions have been made, when they were made, and why.

Ultimately, all data are historical so even big data is no predictor of the future. Half of executives agree that relying on data analysis has been detrimental to their business in the past.

We can recap by saying that major business decisions that have strategic consequences (especially quadrants D & C) need to involve better insights to inform human expertise. The impact of a major decision not informed with evidence-based insights is usually life threatening for most businesses requiring significant investment and probably even a change of leadership in order to salvage the company.


SARSAR (2015, july). Big Data Moment of Truth (BDMOT): The impact of Big Data and related technology on Business Decisions. Executive Doctorate in Business Administration, Paris School of Business