A very common terminology that we have heard is data mining. It may come to everyone as something that is unique or innovative. However, the concept of data mining has not been something recent, but there is a history behind it. It can be easily said that the concept of data mining has been in existence for over a century. However, it has come into the limelight only in the 1930s. The first usage was done by Alan Turing when he used a universal machine to conduct computations that are performed by some of the modern-day computers.

Since that day, there has been a constant evolution in the field of data mining, and we have come far ahead. Today we are seeing organizations leveraging the power of data mining and machine learning to automate their processes around sales, operations, marketing, and other departments.

• Data Mining Definition
• History of data mining
• How does data mining work?
• Data mining methods
• Its importance
• Pros and Cons of data mining
• Use Cases and Examples
• Techniques
• Tools
• The Future of Data Mining

What is data mining?

It is nothing but a process of analyzing a huge quantum of data and thereby bringing out intelligence from that quantum of data, to help organizations solve business challenges, manage, and mitigate risks and thereby capture new business opportunities.  The name is derived from an analogy of searching for precious stones from a mountain of ore.  The process of mining and data mining both involves scouting for valuable things by sifting through large amounts of information.

This process is used in multiple facets of a business such as sales, marketing, product development, research, training, and development. If used effectively, it can do wonders as it helps in gaining valuable insights about customers, thereby generating effective strategies resulting in improved performance output and better revenue.

Data Mining History

If we look at history, one of the first articles that published the word “data mining” was by a gentleman named Michael C. Lovell in the year 1983.  At that time, Lovell and some other renowned economists believed this method could lead to wrong conclusions.

However, by the 90s, the concept of extracting value from data and forming patterns had gained popularity. In the year 1996, Teradata, NCR, and another set of companies executed a project that led to standardizing of data mining technologies. This work comprised of CRISP-DM process, which stands for Cross Industry Standard Process for Data Mining.  The entire process was split into six steps such as:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

By early 2000, businesses were able to see the value out of data mining and this process just took off exponentially, making the industry itself a highly lucrative one.

How does data mining work?

The fundamentals of the data mining process are to ask a business question, search for data that will help in answering that question, and finally preparing that data set for analysis. It must be noted that success in subsequent stages will be completely dependent upon the efficacy of tasks done in the earlier stages. If the quality of data is compromised, then this can result in poor output. Hence, all those who are in data mining must consider data quality as the TOP PRIORITY.

Data Mining in 5 steps

Typically, professionals follow a structured methodology with repeatable processes that deliver desired results. Let us look at these 5 steps

Step 1: Business Understanding

Here, you need to define what is the business objective of the project by mapping it with the current business scenario. Along with that, you also need to define the parameters of the project.

Step2: Data Understanding

Once the problem statement is defined in step 1, then it is important to identify the right data set that will help in addressing the problem statement. It may require you to get these data from multiple sources.

Step 3: Data Preparation

Once the data sources are identified and data is collected, prepare the data in the required format, in alignment with the business objective. If there are any issues such as data duplicity or missing data points, it needs to be fixed immediately.

Step 4: Modeling of Data

Once data is prepared then you can start running different algorithms on that data to study various patterns

Step 5: Evaluation

Once data modeling is completed then you can start evaluating whether these results (as an outcome of the modeling exercise) are able to achieve results or not. This process is executed in an iterative manner along with the data modeling step to ensure that the best algorithm gives the right result.

Once all the steps are completed, then a final presentation is made to the decision-makers to show the outcome of the project.

Why is data mining important?

As much as data mining is a process that is diligently followed by different professionals, it is important to know the significance of data mining.

It is clear that it is the process of capturing large chunks of data and gather meaningful insights from that data. Hence, there is a significant surge in the demand for data providers, further creating a demand for professionals such as data analysts and data scientists.

Since this process involves the conversion of data into insightful information, it helps organizations to make decisions and define strategies for growth. It allows organizations to run specific marketing campaigns and help in predictions. It also helps in getting specific insights about customer behaviors, which is why it is important to run these data mining projects.

Advantages of Data Mining

If we look at businesses today, they are constantly flooded with data with large volumes of data from a plethora of sources. It is no more a choice for organizations to be data-driven in today’s business scenario. The success of a business is critical to the way they extract information from data and use that intelligence to their own benefit.

To put it in simple terms, data mining gives organizations a chance to optimize the future, by analyzing their present and the past. It helps in delivering predictions on what could possibly happen next.

For instance, through data mining, you can get a forecast, of which customers are potentially profitable customers, looking at past profiles of other customers. This way, as an organization, you can focus on specific offers and deals for such customers who are likely to increase your ROI.

Additionally, you can also use data mining for

• Increasing revenue of your organization
• Getting insights into customer segments and their preferences
• New customer acquisition
• Creating more opportunities for cross-selling and up-selling
• Improving customer loyalty and customer retention
• Keeping a track of operational performance

By applying its techniques, businesses can take decisions that are based on intelligence derived from these data.  Thanks to modern data processing technologies such as artificial intelligence and machine learning, organizations can turn around large volumes of data in minutes.

Data mining challenges

Along with innovation and evolution, comes a series of challenges that this method and this industry faces. Some of these challenges are as follows:

• User Interface

  The output of data mining can be useful if it is readable and understandable to the user. Because this method involves working on large volumes of data, there is a challenge in the way the data is presented visually. This is something that the industry and its players need to work upon.

• Security & Social Challenge

  For every organization to make a decision, they require data that is shared by a service provider. With sharing comes the point of security of data. It consists of information of individuals, profiles of customers, and many confidential data. Falling into wrong hands can be disastrous.

• Process Challenges

  There are challenges arising out of the actual methodology of mining. Questionable processes come with challenges such as:

  1. Availability of diverse data set
  2. Management and control of noise in the data set
  3. The versatility of the mining process as a whole

  New challenges will keep on cropping up as and when the industry keeps on evolving.

Data Mining Use Cases and Examples

Globally, there are many organizations that have to achieve staggering results by implementing data mining tools and techniques. Let us look at few use cases and examples

• Groupon

  A primary challenge of the company was to process the huge volume of data it already had, for its shopping service. By implementing data mining, it was able to align its marketing activities with the expectations of customers.

• Dominos

  Said to be one of the largest pizza companies in the world, it collects huge chunks of structured and unstructured data coming from sources such as retail outlets, point-of-sale systems, social media channels, and many other sources. Through data mining, they were able to gain tremendous insights into their customers and thereby improving their customer experience, resulting in improved business performance.

  These are a few of examples for your reference. If we try to dig deeper, there will be many such used cases where data mining has brought about significant transformation across businesses.

Data Mining Techniques

It has been observed, in some of the recent data mining projects that there have been a variety of data mining techniques used for better efficacy. Some of these techniques are as follows

• Classification
• Clustering
• Regression
• Outer
• Sequential Patterns
• Prediction
• Association Rules

Data Mining Tools

One thing is clear – it is a powerful methodology that can literally transform organizations. However, a possible roadblock in the selection of a platform can be around finding one that meets the expectations of all stakeholders. There are a lot of options available ranging from open-source platforms to more proprietary solutions.

Organizations who gain the maximum benefit from data mining would select a platform that will have the following parameters:

1. The platform has incorporated some of the best practices for the industry the organization belongs to.
2. Is able to manage the complete lifecycle of data mining – right from exploration to production
3. Can be aligned with other enterprise applications that include BI systems, ERP applications, CRM systems, and other financial systems
4. Meets the requirements of IT departments, data scientists, and even analysts. It also delivers comprehensive reports and dashboard elements for better visualization.

Many data mining tools come with flexible and scalable architecture with relatable databases and open APIs thereby helping organizations attain competitive advantage.

The Future of Data Mining

All we can say is that the amount of data is going to increase exponentially, making the future of data mining as bright as a shining star. As we have seen the evolution of techniques of data mining, we will also be seeing improvements in technologies that will extract insights from data. To cite an example, IoT and wearable technologies have changed humans into data extracting machines. And this is just the beginning.

Final Thoughts

An important point to be noted here is that it does take a considerable amount of time to get the right set of valid data. However, it takes even more time to derive meaningful information from the data set.

The industry itself is growing tremendously and it is a technology-driven sector. Today, every organization needs good quality data that they can use for various objectives.

There are many service providers who are dedicatedly working on.