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How Data Mining Helps Companies In Decision-Making Processes

The ability to make quick and informed decisions is the distinctive feature of a data-driven organization, capable of optimizing processes and innovating the business through advanced analytics and machine learning techniques. Data mining is essentially the quid of a modern company that competes in the digital economy.

What Is Data Mining, And What Is It For

But what exactly is meant by the term “data mining”? It is a set of techniques and tools to extract practical value from large volumes of information, the so-called Big Data. Analytical technology, machine learning algorithms, and data visualization allow us to dissect phenomena, discover hidden evidence and find solutions to problems. In short, thanks to data mining, it is possible to guess what usually remains unknown to a more superficial examination.

By selecting and appropriately correlating information, it is possible to understand events with a depth of detail, identify behavioral patterns and then formulate predictions. The goal is to move from a descriptive use of data (to monitor phenomena and evaluate performance afterward) to a predictive and prescriptive approach, which allows to draw up future business strategies based on analytical insights and suggestions from artificial intelligence.

Companies that adopt a correct data mining system to extract real value from information can optimize and automate existing processes based on insights. Not only that, but they can also monetize information and innovate business models, leading to a natural metamorphosis of the organization and the very nature of the company.

How To Enable Data-Driven Decisions

The business transformation process presupposes that every single technique or strategic plan is rethought in the light of analytical evidence. All business units and professional figures have access to information and analytics. The data-driven enterprise can only be achieved if the information guides any tactical and operational activity involving the entire company team. Various professionals must therefore be able to participate in the analytical process:

  1. business analysts, who need ready-to-use data to solve specific problems;
  2. the data & IT engineers who manage the systems supporting the analysis;
  3. the data scientists responsible for the construction of the models;
  4. analytics leaders, who must present insights to stakeholders.

Therefore, to improve decision-making, different types of actions and processes are needed to:

  1. select sources and clean data;
  2. easily create machine learning models through libraries and scripting functions;
  3. explore the data and view the results through intuitive interfaces and graphics;
  4. quickly migrate models from the development laboratory to the production environment;
  5. monitor all analytical processes and maintain models.

Examples Of Data-Based Decision-Making

Suppose the ultimate goal of data mining is to meet the entire organization in practice. In that case, data-driven decision-making can be applied to a series of projects ranging from predictive maintenance to churn analysis with great returns. Thanks to the Internet Of Things sensors, which collect information from machinery and industrial processes, it is possible to determine the risk of imminent failures through analytics and then decide how to plan maintenance interventions.

Artificial intelligence and, in particular, sentiment analysis tools also make it possible to identify any customer dissatisfaction with the brand. The company, therefore, has the opportunity to prevent abandonment by the consumer, for example, by activating appropriate promotions and special offers. AI technologies also make it possible to optimize the prices of offers automatically, thanks to market analyses and customer history: in this way, personalized proposals will be obtained quickly, with satisfaction on both sides and more significant opportunities for signing contracts.

The analytics and machine learning technologies also suggest the most relevant products for each customer by improving up-selling and cross-selling activities based on the consumer’s individual preferences. The ability to know each customer’s tastes, drawing from the enormous pool of information from different sources (social media, Customer Relationship Management systems, mobile apps, etc.), also allows you to plan targeted marketing campaigns, ad hoc communications, and more effective support services.

In short, the certainty of data-driven decision-making makes it possible to optimize business activities, invent new operating models, rationalizing processes, and ultimately increase organizational efficiency, strategic effectiveness, the quality of the offer, and customer satisfaction. In this way, positive results are obtained in terms of loyalty and significant economic savings, substantially increasing the company’s profitability.


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