Data mining: what is and how does it help companies?

Data mining, is a discipline that combines statistics, artificial intelligence and databases to discover hidden patterns and relationships in large volumes of data.

Nowadays, companies generate and collect a large amount of data on a daily basis, from customer transactions to production records, and the ability to analyze and extract useful information from this data has become an invaluable asset for strategic decision making.

In this article, we will take an in-depth look at what data mining is, its main techniques and tools, and how it can transform the performance and competitiveness of your business.

What is data mining and what is its purpose?

Data mining is an automatic or semi-automatic technical process that is responsible for analyzing large amounts of dispersed data and information in order to make sense of it and turn it into knowledge.

It looks for anomalies, patterns or correlations among millions of records to predict results and, according to the SAS Institute, is one of the world's benchmarks in business analytics.

Another important fact to know about data mining is that a 2017 study revealed that 90% of the world's data is post-2014 and its volume doubles every 1.2 years. That is why this is an important practice for almost 80% of companies that apply business intelligence, according to Forbes.

Data mining also helps in other aspects such as:

• Cleaning data of noise and repetitions
• Extract relevant information and use it to evaluate possible outcomes
• Make better and faster decisions

Why use data mining

Companies can use data mining for knowledge discovery, to increase customer confidence, find new revenue streams and keep customers coming back.

It is very effective for business planning and operations management and its main benefit is to identify patterns and relationships in large volumes of data from multiple sources.

In the following list we detail some of the main reasons why you should implement this practice:

Informed decision making

Data mining allows companies to analyze large volumes of data in order to identify patterns. This is very useful for making decisions based on concrete data and not on assumptions.

Identifying market opportunities

By analyzing sales data, customer behavior and market trends, it is much easier for you as a company to find unattended business and market opportunities; thus adjusting your marketing and sales strategy.

Improve customer relations

Data mining helps organizations understand their customers better and learn about their specific preferences and needs. This information is valuable for personalizing offers, improving customer satisfaction and building customer loyalty.

Optimization of operations

By analyzing operational data you can identify areas of inefficiency and optimize internal processes, reducing costs and improving productivity.

Prevent fraud

Data mining also serves as an effective tool for detecting fraudulent activity and by detecting unusual patterns, it helps you prevent fraud and minimize losses.

Developing new products and services

Analyzing customer feedback data can provide you with valuable insights to develop new products that better meet customer needs.

Forecasting and planning

Data mining helps accurately forecast demand, sales and other critical variables, resulting in more effective strategic planning.

How data mining works

There are many approaches to data mining and it depends on the type of questions being asked and the content of the database organization that provides the raw material for the search and analysis.

So, here are some of the steps that need to be completed to prepare the data so that you know how data mining works.

Understand the research area

The business decision maker must have a general understanding of the environment in which they will be working and know the types of internal and external data that will be part of it. This person must have an intimate knowledge of the organization and the areas involved.

Collect the data

You should start with internal systems and databases and link these through data models and other relational tools or bring them together in a data warehouse.

This includes any data from external sources that are part of operations, such as sales or field service data, Iot social media data.

Seeks and acquires the rights to external data including demographic, economic and market intelligence such as industry trends, financial benchmarks from trade associations and governments.

Data preparation

Use subject matter experts from your organization to define, categorize and organize the data. This part is often called "data wrangling" or "munging". Some of them may need cleaning to remove duplicates, inconsistencies, incomplete records or obsolete formats.

User training

Finally, for data mining, be sure to provide formal training to future data miners and have supervised practices as they begin to familiarize themselves with these tools.

Types of data mining

There are several ramifications of data mining, here we tell you about them:

Process Mining

This aims to detect, review and improve business processes by extracting knowledge from event logs that are within information systems. These help companies to visualize and understand what happens in processes on a daily basis.

One example is an e-commerce company using process mining to identify that the payment verification stage is a bottleneck in its order management, allowing it to automate this stage and significantly reduce delivery times and errors, thus improving customer satisfaction.

Text mining

This type of data mining consists of using software to read and understand text. It is used by scientists to automate the discovery of knowledge in resources such as web pages, books, emails, reviews and articles.

For example, a market analytics company uses text mining to automatically analyze thousands of online product reviews, identifying patterns of satisfaction and common problems, enabling manufacturers to improve their products and services based on real customer feedback.

Predictive mining

Uses business intelligence to predict trends. This type of data mining helps business leaders study the impact of their decisions on the future of the business and make more effective choices.

Example: A retail company uses predictive mining to analyze historical sales data and forecast product demand in different seasons, allowing them to optimize inventories and plan more effective marketing campaigns, which increases sales and reduces operating costs.

How data mining helps companies

Let's look at some of the examples of data mining in today's industries and how it benefits them:

Marketing

In the marketing area, data mining is used to explore larger databases and thus improve market segmentation. By analyzing the relationships between parameters such as customer age, tastes, among others, it is easier to guess the behavior and make personalized loyalty or recruitment campaigns.

Retail

They use shopping patterns to identify product associations and decide how to arrange them in different aisles and on shelves. Data mining also helps to detect which offers are most valued by customers.

Banks

Banks make use of data mining to better understand market risks, it is applied to credit scoring and intelligent anti-fraud systems to analyze transactions, card movements, purchase patterns and customer financial data.

Medicine

Data mining in the area of medicine helps to have more accurate diagnoses, since, by having all the patient's information, such as physical exams and previous therapy patterns, more accurate and effective treatments can be prescribed.

Television and radio

Many broadcasters apply real-time data mining to their online TV and radio ratings. They collect and analyze anonymous information from the channels' views, broadcasts and programming.

By analyzing this, personalized recommendations can be transmitted to listeners and viewers, allowing you to learn about their interests and better understand their behavior.

Education

Education professionals can use data mining to assess students, personalize lessons, and make learning more playful. Likewise, by analyzing student data, they can determine what students need and help them improve.

Manufacturing

They can use data mining to provide real-time and predictive analysis of overall equipment effectiveness, service levels, product quality and supply chain efficiency.

How London Consulting Group helps with data mining for businesses

At London Consulting Group we facilitate a comprehensive understanding of daily operations through the information generated, enhancing agile and effective decision making both internally and externally.

We implement the right technology tools to avoid generating unreliable or delayed data and offer customized solutions, precisely tailored to the specific requirements and objectives of each business, ensuring that data mining tools are aligned with your needs.

We also establish an organizational culture that emphasizes and effectively harnesses the power of data to make informed decisions and define strategic breakthroughs.

By focusing on data management, data discovery and visualization, and advanced analytics, we provide organizations with the foundation necessary to effectively manage and utilize high-quality data.

Contact us to begin the transformation of your organization and improve your decision making skills.