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Data mining: techniques and tools for transforming information into knowledge

Data mining holographic image

Data mining: techniques and tools for transforming information into knowledge

The growing technological evolution, coupled with the increased use of computerized systems in homes and organizations, has brought with it an incalculable amount of information. Digital data has not only grown exponentially, it has also become an essential asset for understanding consumer behavior, which is fundamental to the success of many businesses around the world, and that is data mining.

The concept of knowledge discovery in databases, today called Data Mining, is not new. The process of digging through data with the idea of finding correlations, discovering connections and common categories with the objective of predicting trends, has been used by renowned statisticians since the 1960s.

In the last 10 years, the need to store such large amounts of information has motivated the development of more efficient systems with greater capacity. Technological advances have taken data processing to another level, which has allowed us to evolve from the old rudimentary practices that took excessive time for analysis, to being able to explore a collection of data in microseconds to analyze them in an automated way in search of discovering relevant insights and generating new market opportunities.

analysis of business reports

Why is data mining important?

We are currently in a historical moment that has been called the “information age”. Today, almost every activity we perform in our environment generates data that is a very important input for strategic, tactical and accurate decision making in important sectors of the economy, among which are: traders, manufacturers, banks, telecommunications providers, insurers, retailers, industry, construction, among others. For all of them it is of utmost importance the use of data mining as a strategy that, correctly implemented, allows them to filter the confusing and repetitive noise of data, understand what is relevant and what is not in order to make good use of the information and accelerate the pace of strategic decision making. Its implementation ensures certain effectiveness in the planning of prices, promotions, risks, competition analysis, use of social media and all the communication or transfer channels they use, and that if they do not have the necessary attention, could affect their business models, revenues, operations and relationship with customers, consumers or their audiences in general.

There are numerous areas where data mining can be applied, practically in all activities and sectors that generate data. But to do so successfully, one must first understand the concept very well and acquire the necessary knowledge for proper data mining, in the good sense of the word. This requires training, knowledge of statistics, programming, business, consumer behavior and market research or psychology.

The first thing to understand is that it is based on three intertwined scientific disciplines: statistics (numerical study of data relationships), artificial intelligence (human-like intelligence exhibited by software and/or machines) and machine learning (algorithms that can learn from data to make predictions).

Data mining process

A data mining process can be basic by contemplating a small number of stages, but it can also be deep, depending on the size of the company and its needs.

As in any process, for its implementation it is key to have a logical order to execute it in an orderly manner and obtain results that benefit the business. Although there are several techniques and methodologies for each specific case, it is recommended:

  1. Data integration and collection phase: decide which will be the main data input, i.e., useful sources of information with considerable amounts of data and that give the possibility of unifying them in a common format.
  2. Selection, cleaning and transformation phase: The data collected may contain errors in their values, or some of them may even be incomplete. At this point it is necessary to correct, detect patterns, trends, outliers and discardable data that do not contribute to our analysis in order to define meaningful data in terms of prediction and calculation variables.
  3. Data mining phase: decide what the methodology will be based on the established objectives: grouping, classifying, search and discovery of unsuspected and interesting patterns under predictive and descriptive models that estimate future or unknown values, and identify patterns that explain or summarize the data, respectively.
  4. Evaluation or interpretation phase: the previous phase should result in the identification of patterns. Finally, the final data obtained are validated, compared and interpreted, and the most satisfactory ones are chosen according to the results obtained.

When we talk about data mining we refer to a market of great opportunities. Here are some examples of applications and uses in the most relevant industries of today’s economy:

  • Retail and banking: customer segmentation, sales forecasting, risk analysis.
  • Marketing: analysis of parameters such as customer age, gender, tastes, behaviors, etc., to direct personalized loyalty or recruitment campaigns.
  • Media: records of interests, activity and behavior of audiences, such as monitoring views, retransmissions and content scheduling.
  • Medicine and Pharmacy: history, tests and therapy patterns to prescribe more effective treatments.
  • Supermarkets: product purchasing patterns to make decisions on how to position products in aisles and on shelves.
  • Astronomy: identification of new stars and galaxies.
  • Mining, agriculture and fishing: identification of areas of use based on satellite image data.

We can conclude that, in order to survive in today’s market, organizations must be highly competitive. To be competitive, they must know how to make the best use of their resources and be very agile when it comes to making decisions at the right time by using data mining to search for and obtain concrete information and optimize their economic activities.

analyzing data

Every day the problem of being able to control and interpret the large amounts of data that we constantly generate grows. Fortunately there are many types of analysis that can be done to obtain information, the technique used and the impact of the analysis will depend on the type of problem you want to solve or opportunity you want to find, the most important thing is to adopt this concept as an opportunity to be informed and open to changes in market or consumer behavior and thus be able to generate a greater volume of business.

That is why at Interfaz as an expert company in digital transformation and development of innovative solutions, we offer strategic partners our team of trained professionals with the objective of optimizing their business management, through the implementation of an appropriate methodology of data mining for the collection, cleaning, analysis and transformation of data into new business opportunities.

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