Data Mining: The Age of Technology means that practically all of us interact in digital environments, generating a huge amount of personal and business data. In this sea of inputs, someone must work by searching values for some meaning or context that transforms the combination of raw data into information.
You may have already heard that data is, today, a corporation’s most valuable asset. But the truth is that they are just the gateway to what adds value. Without the ability to use these small loose pieces to craft entire puzzles, they have virtually no impact.
Want to know more about this subject? So, continue reading and enjoy!
What is data mining and what is it for?
You can easily understand what data mining is from the simple example we just used. Imagine that, in the world of technology, new puzzle pieces are being generated all the time. However, they all go into the same container, being mixed with thousands of other pieces.
What data mining does is precisely search piece by piece, analyze, and group them so that they can make some sense. At the end of the process, some coherent picture needs to be assembled, or the data will just be loose pieces.
This is how companies have the opportunity to make their data management, generating insights through the combination of data, which can point to a series of valuable information, such as:
- market tendencies;
- consumer behavior patterns;
- probability of failures in internal processes;
- projection of the behavioral profile of employees, etc.
What are the 4 main steps of data mining?
You must imagine that the “sea of data” that exists today is almost infinite, right? It is not for nothing that this area is called “Big Data”. After all, the field brings together an absurd amount of elements that can be transformed into information.
Therefore, data mining is divided into well-defined steps that help organize and extract the most consistent information possible from this global database. Check it out!
1. Determination of the objective
The first step to preparing for data mining is to define the objective you want to achieve. It is necessary to keep in mind what information should be extracted that will contribute to the business strategy.
Is the company looking to solve a specific problem? Does it want to explore a new market niche? Is the focus on improving decisions that affect the internal environment? Is the idea to expand the market and gain another type of client? This all needs to be defined before starting work.
2. Elimination of redundancies
Within the availability of data, many of them are repeated or end up reaffirming already existing conclusions. In this case, they can hinder the conclusions of the analysis or, at most, reiterate any findings. An interesting idea is to separate this data, check its sources and exclude duplicates, which could affect subsequent analyses.
It is also interesting to determine parameters for data mining that make it possible to gather useful information for the objective defined in the previous step. Everything that is not directly related to it can be discarded, so as not to harm the focus of the evaluations.
3. Data cleaning
A second screening stage is essential to qualify the data that will be analyzed and, consequently, the information extracted from them. In it, miners must delete all faulty, error-prone, duplicate, or misaligned elements with the established parameters.
This phase is essential to ensure that mining is more objective and efficient, considering only inputs aligned with the insights that the business is seeking. If the company wants to enter the B2B universe, for example, considering data from individual consumers will only distort its results.
4. Data mining
The fourth and final stage is the most definitive of all. She is responsible for finally analyzing all the remaining data and identifying patterns or correlations between them. In this case, miners can work on hypotheses freely, but always considering the goals defined by the company.
It is worth remembering that it is not enough to just obtain some insights with data mining. It is necessary to prove its consistency with the recurrence of the data presented and concerning the parameterization stipulated in the previous phases.
How to use data mining?
Data mining can apply to basically any business objective. Furthermore, it is a great instrument for making better decisions in various departments of the company. Below we list some examples.
Have you ever wanted to better understand your customer’s purchasing behavior? consumer? What if someone told you that it was possible to predict your audience’s consumption preferences just by looking at the pattern of their latest purchases? Of course, there is an important combination of mining and statistics in this, but, yes, commerce can benefit from this type of analysis and stipulate much more specific strategies based on it.
Companies facing internal problems — such as excessive rework, waste, and even low productivity— may just be inefficient in their processes. Data mining can help identify which specific points are negatively affecting business performance and which behavior patterns are perpetuating procedural slowness.
HR can greatly benefit from the use of data mining in combination with artificial intelligence for:
- identify who are the best candidates in a selection process;
- discover which employees have the best behavioral profile for a given position;
- predict which actions can promote team motivation and improve the organizational climate.
Data mining is also a great resource for businesses that have a consumer credit policy. It helps to identify which are the safest customer profiles to provide credit and which are those with a high chance of eventual default. With this, the business can increase its chances of closing good deals without putting its cash flow at risk.
Big Data VS Data Mining
The concept of Big Data consists of a large collection of unstructured data, constantly produced around the world. It works like a large bank that brings together all these loose parts. Which are available to treat and organize using a variety of techniques, always to make them productive.
Imagine Big Data as a very voluminous library of books, whose items are all disorganized, but available to anyone who wants to consult them. From this, the librarian (or miner) can create his organization by mining complex data or opt for simpler approaches.
It is as if the librarian could use a specific cataloging method to order all the books in a collection or separate the books into smaller categories and then address each of their literary genres. Data mining generally works with smaller, parameterized groups. Which reduced to answer much simpler and more specific questions.
Now you know the initial steps to understand how to do data mining. You can take advantage of this information to use this valuable resource in favor of future decisions for your business. Take advantage of the tips and seek specialized help to optimize your results.