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Data-driven decision making


By Eugenio Ciamprone

In order to be present and competitive on the market, it is vital for a modern company to base the decision-making process on data analysis, rather than base decisions on observations or intuitions.

Data-Driven Decision Making is precisely the process by which management focuses its strategies and decisions on the basis of measured and verifiable data and numbers. In fact, first and foremost, the paradigm shift has to come from top management, who must first believe in a data-driven business approach, that is based on data analysis and evaluation.  But of course, the value and reliability of decisions based on data depend on the quality of the data, the correct analysis of the same and the right interpretations. It is only in this way that a data-driven business approach can help organizations to obtain competitive advantages and cost reduction, and increase efficiency.

Implementing a data-driven decision-making process allows management to make decisions based on collected data and information, on their analysis and interpretation. This is the first step towards the digital transformation of a company. However, this cannot happen without the correct use of Big Data and Machine Learning techniques.

According to the definition published by Gartner, Big Data is “high-volume, high-speed and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”. The use of innovative Machine Learning techniques, in fact, enables you to automate the learning process and provide scenarios and solutions in order to support decision-making processes, autonomously.

Currently, thanks to the evolution of Artificial Intelligence and Business Intelligence platforms, management can extract and view data and analysis, consult reports and make data-driven decisions.  Companies that want to implement a decision-making process based on data collection and analysis must follow a path and move forward by stages: first of all, they must identify the goals and the most important business areas in the digital transformation strategy. Once these goals have been identified, they will have to establish which data to analyze, how to obtain and collect them.

Following the identification of the size and composition of the dataset, the Data Preparation phase takes over. Here, data are organized and “cleaned” to be qualitatively ready to be analyzed. Only later, with Data Modeling, the data analysis techniques that best fit the reference framework and produce the best performance are identified in order to transform the results into feasible decisions and insights.

  1. Benefits of a data-driven decision making
  2. The role of Data Modeling in decision making

Benefits of a data-driven decision making

What are the benefits of basing the corporate decision making on the evidences that data analysis reveals?

First of all, it will be easier to make a decision once you have analyzed the data. And moreover, the decisions made according to the results of data analysis are part of the company’s strategy, or rather, to pursue a data-driven transformation path, and hence enables entities to follow that approach without having the worry about whether or not having made the wrong decisions. The decisions made based on the data will not always be correct, of course, but the data analysis tools allow us to constantly monitor the economic impacts, and not those of our choices, in order to guide any corrective actions.

Entrusting the decision-making process to data analysis also offers competitive advantages on the market, in terms of time and costs, as well as the identification of business opportunities before competitors, or the detection of threats to the business before it is too late.

As already mentioned, two consequences of a company’s implementation of a data-driven decision-making process are the ability to make more quickly choices and with the most recent data. And this leads to an improvement in operating efficiency and cost reduction.

The role of Data Modeling in decision making

The company’s implementation of decision-making processes based on data analysis cannot be separated from the correct use of Machine Learning techniques. Data Modeling is a particularly important phase and it also consists in the choice of algorithms to be used within a specific context that enable strong performances in the achieved results.

Furthermore, Data Modeling is the representation of the rules and relationships that distinguish the analyzed data set and, therefore, it is a fundamental phase for organizations that have decided to use data analysis as a strategic business asset on which to build decision-making processes.

It allows you to automate operations, reduce costs and increase the efficiency of activities, thanks to the access to clearer and more detailed information in short times.

In conclusion, data modeling allows the company to organize and combine data of different origins and nature, in order to extract the maximum value out of it and transform it in immediately actionable insights and actions.