Information is power! In such a competitive market, who has the data in hand is able to make a difference. Whether to predict results or to beat the competition. In this scenario, we can highlight the so-called data mining as a great differential.
This process is explained as a great technological tool that allows the generation of information capable of supporting the execution of companies' actions. With data mining, it is possible to determine rules or standards for the performance of customers.
One study by Harvard Business Review and Snowflake Computing showed that privileged data is what supports companies to survive.
In fact, 89% of retail companies admit that they use differentiated information, through technology, to understand the customer's profile and what they need.
These insights, generated via data mining, expose consistent standards and bring useful information to the company, making it more competitive.
Do you want to know how to use data mining in your routine and its benefits for your company? In this article, we will detail this technique and show why it is a great differentiator in strategic decision making.
What is data mining?
Data mining, in literal translation, would be something like data mining. This term was first seen in 1990 in communities that discussed databases.
He explains how a process capable of analyzing a large amount of raw data and extracting a consistent pattern from itThis automated process allows the company to find relevant insights of the company and the customers themselves.
Data mining uses algorithms that act in depth in the process of big data, filtering the information, according to a pre-determination of the company, for strategic decisions.
Given that, with the information generated by data mining, it is possible to act on issues such as cost cutting, increased revenue and the relationship with consumers.
What is the difference between BI, Data Mining and Big Data?
A frequent question for those who work with data analysis is to differentiate three of the main information strategies in the market: Business Intelligence (BI), Data Mining and Big Data.
Therefore, before going on to data mining techniques and their advantages, it is important to understand the differences between these three items. Check out the focus of each one of them.
- Business Intelligence (BI): data capture, analysis and organization of information. Refers to actions already taken. The main objective is to support management for further data analysis. Manual data analysis is necessary for decision making.
- Data Mining: It is data mining, that is, it allows a more detailed and accurate analysis of the information. Automatically identifies customer rules and standards.
- big data: It focuses on working with a large set of data, whether unstructured or structured, and it is a more complex process in relation to data mining. While data mining works with specific analyzes, big data is continuous and for a long period.
Therefore, the truth is that whoever manages to work the three together will always have privileged information at hand for intelligent decision making.
We already talked on the blog about how to implement Business Intelligence in the management of the company, read here.
How to apply data mining?
Having diverse market data at hand is a great advantage over competitors, however, the most important thing is to know how to use it and what information is relevant.
Therefore, below we highlight the main steps for the application of data mining to help you in this process.
Determine the company's goals
The first step, in order for the data mining flow to be truly effective, is to determine which objectives and questions to be answered in the process.
Because, when raising your goals within the previous planning, it is easier to define what data mining needs to offer you in an intelligent way and that provides a basis for your decision making and data analysis.
Therefore, first define the problems that you want to solve and what you are looking for with data mining.
Define data sources
The data sources are fundamental in this process, as it is important that the information collected is of quality. So don't just focus on sources that are easily accessed by the company.
If you need to do market research, collect relevant information and rely on data available in CRM, BI or Big Data.
At that moment, gather as much information as possible to be able to answer your questions outlined in the first step.
Analyze the data and clean it up
Certainly from this step you will have a huge amount of data at hand to work. That is where your data mining starts, that is, data mining to separate the relevant from the irrelevant.
It is that process of formatting and cleaning information based on your standards and the questions to be answered. Whether eliminating duplicate, wrong data or information that has nothing to do with what you are looking for.
In this case, the manager, together with the technology, determines the parameters so that the algorithms are able to sift through the information and consequently deliver what is worthwhile.
Mining the data
After an accurate analysis of the data, filtering of the information, cleaning and storage of the same, the process of hands-on data mining begins.
From there, the learning algorithms start to offer insights style=”font-weight: 400;”> the company and identify patterns and correlations based on the data offered to the system.
The tool uses the algorithms to cross the patterns of behavior and information and after this data transfer it is enough to compare them with the business objectives that you set out at the beginning.
Based on this concrete and solid information, through data mining, decision making in the relationship with the customer is easier. Having customer behavior data in hand, possibilities of cost cutting, opportunity to increase revenue and so on.
What are the main techniques of data mining?
Data mining brings together a set of techniques that allows mining to be done correctly and facilitates the identification of valuable information and the patterns and correlations you seek.
Below are some of the main ones.
Neural networks are nothing more than a mathematical model that gathers several information that are interconnected with each other using artificial intelligence.
It got its name because it is a representation of the human brain that has millions of neurons capable of processing information and communicating.
In neural networks these “artificial neurons” are based on a learning over time style=”font-weight: 400;”>, based on previous experiences, and foresee scenarios of demands, product sales and much more.
They are configured to precisely define data patterns in the data mining by linking the information entered and configuring the algorithms.
Decision trees serve to identify data and create branches that delimit the profile of a target Audience. This table representation is similar to a flow chart or diagram.
Let’s say you’ve entered items like location and salary at the top of the tree and want to mine those points more.
In the salary range you want to find people who earn more than £ 3 thousand and enter this data. Automatically in the decision tree a new branch will be opened for that group that gains this value and another branch for the others that invoice other values.
In terms of location, you only want people who live in São Paulo and you put that information in the system, then a branch will be created with people who live in São Paulo and the rest of the people will go to another branch.
This means that you can define an infinite number of attributes and in the sequence the system will present you with a new branch based on what you requested.
This is perhaps one of the most common techniques used in the data mining process. It is used to identify possible patterns of behavior and trends related to a specific data set.
This algorithm detects and presents the rules that a data usually follows. We can quote the retail where items that are bought together are presented, white pants with black jeans or cell phone X with cap Y, among other situations.
Time series analysis
It is common, even called the basis for all other techniques, but it requires the expertise of those who will use it.
The data mining technique called time series analysis uses mathematics and statistics to define patterns behavior of the data in question and results prediction.
Evaluating and presenting data, for example, of sales variations in seasonal periods of a company, irregular or cyclical variations.
How it is a statistical work it is essential the participation of people who understand the subject and know the company and the market in depth.
What are the benefits of data mining?
Data mining plays an important role in decision making and that is why anyone who adopts this type of data analysis model is able to not only automate processes, but make them more assertive. Below we list some of the main advantages of it.
- Facilitates sales forecasts, demands and more;
- It allows decision making in an automated way;
- Generates insights into customer behavior;
- Streamlines daily data analysis processes;
- Reduces possible costs due to immediate correction of processes;
- It improves the relationship with the client due to concrete data on their profile;
- Offers qualified information and data.
Data mining: why adopt it?
Data mining is a practice that is becoming increasingly common in the routine of organizations. After all, data collection has become a major competitive differentiator in the market.
Whoever has relevant information about the company and in relation to customer behavior, generated by data mining, has power in hand, a source of immeasurable value.
With data mining, decision making is facilitated and every choice is based on hard data and always with a strategic bias.
Because, with qualitative data, generated by this mining, it is possible to better understand the customer's profile and how he acts in each situation. Making the company more competitive and assertive in all its decisions.
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