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Organizations are getting piled up with raw data every day. Irrespective of the quality, the quantity of data keeps on increasing. Having the data available with you will not make any sense until you can extract the exact information from it. As per grand view research, in 2019, the global alternative data market size was valued at USD 1.06 billion and they have predicted that from 2020 to 2027. A compound annual growth rate (CAGR) will be 40.1%. With this hefty amount of data becomes available to the organizations. It has become crucial for them to use it to its optimum limit. The data is of no use if it can’t provide any information. During this time, data is a real treasure and Data Miner with techniques. Who can help in deriving the information from the data is a King. And his job is known as Data Mining.

What is Data Mining?

Data mining is a process used to extract useful information. From raw and scattered data to develop a meaningful insight for further business strategies. This knowledge largely supports increasing customer loyalty, recognizing hidden profitability, and reducing client churn rates. Organizations can adopt various data mining techniques to turn raw data into actionable insights.

The Approach for Data Mining

There are numerous techniques and various best practices implied in Data Mining to outline the desired output. Depending on the needs of your company, data mining can be used to do everything. From predicting buyer’s behavior to finding the best leads for your business. 

There are 1 billion interactions every month on LinkedIn.

Now that we know what is Data Mining, let’s find out how Data Mining works.

The collected raw data from various sources is stored on the servers. The data is then compiled and is segregated and analyzed to derive meaningful information for assisting in critical decision-making using numerous data mining techniques.

Processes involved in implementing Data Mining

With chunks of data piling up on the On-Premises or On-Cloud servers, it’s becoming a challenging task to put data into simple steps as shown in the above figure. The entire implementation life cycle is itself a huge and tedious process.

Companies assign dedicated Data Mining experts for these services.

First, data is collected on the database servers in varied formats, like numbers, words, images, videos, etc. This data is then segregated and stored. 

Next, a person with good data understanding and mining capability is given the responsibility to understand the business statement.

Now, the Data Miner has to understand the data collected. And analyze how this data can be prepared for a solution. A perfect data model is planned and evaluated. And then the processes of Data Mining are deployed as planned, by selecting a perfectly eligible tool. This tool further helps the Data Miner and the organization to visualize the patterns. And come up with their perspective strategy.

Various techniques involved in Data Mining

Data Mining techniques make data more polished and dependable for data miners to achieve the desired results. Let’s find out about these techniques in detail;

Click here to get in-depth information on Data Mining Techniques.

1. Data Cleaning – In this step, entire raw data is collected, cleaned, and formatted to make it ready for various analytical methods/tools.
2. Classification – Classification is one of the most intricate data mining techniques that encourages you to stock different attributes together into noticeable categories. The classification can also take into account, the level of user interaction involved in the data mining procedure, such as query-driven systems, autonomous systems, or interactive exploratory systems
3. Sequential Patterns – This is one of the few data mining techniques that uncover a series of events in a logical sequence. 
4. Clustering – Clustering analysis is a data mining technique to identify similar data. This technique basically groups data based on their similarities together.
5. Regression – Regression is a straightforward white box technique that reveals how variables are related. Regression techniques are used in aspects of forecasting and data modeling.
6. Association Rule Learning – Association Rule Learning is one of the best types of data mining techniques whose primary purpose is to track patterns. This technique is based on if/then statements. These statements reveal the association between the individual data sets.
7. Outlier Analysis – It is not just about recognizing the overarching pattern to get a clear understanding of your data set, understanding the anomalies/outliers in the data is equally important. Once organizations find irregularities in their data, this analysis helps them understand why and what of the anomalies. This in turn helps the organization to prepare for any future occurrences to best accomplish business purposes.
8. Prediction Analysis – It is considered as the most valuable data mining technique because it’s a process that businesses use to project their future data needs.
9. Data Visualization – Information is represented in a visual form to give a cutting edge to the collected data. Now-a-days, dynamic visualization is getting utmost importance as the information gets streamed in real-time basis, with logical coloring scheme which helps in defining trends and patterns in the data. The organization defines color-coding based on various industry-specific KPIs.
10. Neural networks – AI and Deep Learning often is accompanied by a Machine Learning technique called Neural networks. The name is derived from the concept of having different layers which resemble the way neurons work in the human brain. Neural networks are one of the prominent and precise machine learning models utilized today. Although it can be a powerful tool in data mining, organizations must take caution when using it: some of these neural network models are amazingly complex, which makes it tough to comprehend in what way, a neural network may regulate an output.
11. Machine Learning (ML) and Artificial Intelligence (AI) – ML and AI represent some of the most advanced developments in data mining. Deep Learning, an advanced form of Machine Learning, offers highly accurate predictions when the task is to be performed on data at scale. Thus, its usage is seen while processing data in AI deployments like computer vision, speech recognition, or sophisticated text analytics using Natural Language Processing. These data mining techniques are good for defining value from semi-structured and unstructured data.

Benefits and examples of using Data Mining 

Many organizations have their on-cloud and on-premise servers loaded with data, but they are not able to get knowledgeable insight from it. Since the adoption of Data Mining, organizations can reap benefits out of the mysterious data.

Let’s see below how it has been a value add to many businesses;

1. Optimize marketing campaigns – Data mining helps businesses realize which marketing campaigns have a maximum probability to generate the most engagement, categorize customers, push custom-made advertisements, and enhance their promotion spend.
2. Innovate – Data mining supports companies to figure out what appeals to their customer and what innovation is needed for their product/service. Understanding that the social platform LinkedIn is used for professional purposes, Microsoft has come up with the in-mail feature to assist their business users.
3. Better Decision Making – Rather than merely relying on your intuition or experience, insights generated from your private business data can help you make better decisions. For example, D-Mart a departmental store isn’t flourishing just because of its discounts and product availability, but its reach in all the major cities, as well as nearby villages, has made its users choose over other departmental stores.
4. Reduce Employee Attrition – Data mining not only helps improve external market performance but can also be used to understand employee behavior, predict attrition, and evaluate HR policies.
5. Brand recognition – Data mining collects and processes a great volume of unstructured information (such as comments, posts, tweets, images) shared on social media, which is further used to review your digital presence. For example, selecting a brand ambassador based on his influential presence in the market holds utmost importance.
6. Logistics and Delivery – Taking all the factors into account through various data, which involves history and real-time staff, weather, and traffic analysis, companies predict their delivery time. For example, Dominos 30mins delivery is not applicable on all the orders on all days, at times it does vary depending on various situations.
7. Market Basket Analysis – This feature enables the organizations to predict their customer behavior and act more swiftly and smartly. By recommending the necessary product to purchase looking at their existing cart. For Example, Amazon, AJIO, Myntra suggest to you what else you can buy or other people have purchased on the basis of items in your cart.
8. Detect Potential Fraud – Data mining helps businesses detect fraudulent activity and anticipate potential fraud. For example, the data of all the doctors and their registration details with the insurance company. Which help them detect the fraud and valid claims. 

Threats involved in Data Mining

• The collected data may not be complete and accurate.
• Data Mining needs huge databases and data collection that are difficult to manage.
• The data mining process requires domain experts that are again difficult to find.
• Integration from heterogeneous databases is a complex process.
• The complexity of the Data Mining tool increases with the data.
• The Data Mining process directly deals with personally identifiable information. So, it is reasonable to say that privacy and security concerns are at stake.
• The organizational level practices need to be modified to use the data mining results. Restructuring the process requires effort and cost.

Conclusion 

A generous amount of data is getting poured on the servers of the organizations every day. Businesses have to invest time and money to get the best insightful information out of it. Data mining helps businesses envision patterns and trends amid raw data. These insights can lead to faster, more informed decision-making.

The more data you collect from customers the more value you can deliver to them. And the more value you can deliver to them the more revenue you can generate. But the only catch here is, you should be able to make sense of the collected data.

But as we see that data is a precious stone in the mine. It has to be handled with care and security.

Consulting a robust firm like Oriental Solutions, helps you flourish in the data-driven world. Get expert Data Mining services, from the best professionals in the market. So contact us @ http://orientalsolutions.com/contact.php or call at 044 2498 6018

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