Data mining in today’s world gives businesses a 360 view of the ins and outs of a company. But how? Let’s build an understanding of this by drawing a map from scratch and strengthening our grasp on data mining, its’ techniques, and processes along with how many industries are harnessing it to compete in today’s world.
What is Data Mining?
Data Mining is a process of discovering patterns, trends, and significant information from large batches of data sets and converting those outcomes into insights and predictions. Due to this very nature of its process, it is also known as Knowledge Discovery in Data i.e., KDD.
How does Data Mining work?
Data mining is a complex area that works by using various methods, techniques, and algorithms. Which serve several analytical capacities and take in hand a wide range of organizational needs. Adding further, it works to reach a workable decision by asking different questions and employing different levels of human rules.
To not confuse the process of data mining, first, let’s go through the types and techniques.
What are the types of Data Mining?
Mostly, data mining main types come under two categories
- Predictive Data Mining
As the name says, Predictive Data Mining pulls out predictive trends from the data – to know what may happen in the future.
- Descriptive Data Mining
Descriptive Data Mining is all about turning the data into relevant and significant information.
Both above types have further sub-types. However, they are more techniques than sub-types. There are various data mining techniques, but the following techniques are the major ones:
- Classification Analysis
- Regression Analysis
- Market Basket Analysis
- Clustering Analysis
- Summarization Analysis
- Anomaly Detection
- Neural Networks
- K- Nearest Neighbor (KNN)
- Decision Tree
- Prediction Analysis
Let’s have a look at all of the above in detail as well.
Data Mining Techniques with Examples
Classification analysis allots data points to groups or classes based on a particular question or problem to tackle. This technique is generally extremely helpful for retailers who aim to study consumer buying behaviour.
For example, if a packaging company wants to offer a coupon discount on a specific product, it might use classification analysis on the following list to make the best strategy and decision.
- Inventory level
- Sales data
- Coupon redemption rates
- Consumer behavioural data
This technique recognizes and evaluates the association among variables. To clarify, this means recognizing factors that are most important or insignificant. As well as how factors are interacting. In general, this analysis predicts and forecasts.
For instance, regression analysis plays an essential role in:
- Weather analysis and prediction
- Sales and promotion forecasting
- Financial forecasting
- Effect of Delivery speed (one variable) on customer satisfaction and small-value orders (other variables)
Market Basket Analysis
Market Basket Analysis, aka Association rules, looks for relationships between variables and establishes further significance within the dataset by connecting parts of data.
For instance, if a company wants to plan, promote and forecast sales of different products together, market basket analysis proves beneficial. That is to say, it looks into the company’s sales history and checks products purchased together.
Cluster Analysis clusters the characteristic object i.e., that contains the same characteristics. Not to confuse with classification Analysis which assembles the objects into predefined classes, clustering analysis stores objects into classes where objects define those classes. In other words, Clustering searches for similarities within a data set, separating data points that share common traits into subsets.
For example, segmentation of products based on purchase behaviours, need state, or likely preferences in marketing communication i.e., clustering products as “Haircare” or “dental care” instead of classifying as “shampoo”, “conditioner”, and “toothpaste”.
Summarization Analysis allows saving a data group or a data set into a more understandable but compacted form. This technique is one of the most known and useable forms of data mining.
For example, creating graphs or calculating averages from a data set.
Anomaly or Outlier Detection
Anomaly detection, aka outlier detection, is a part of data mining to pinpoint unforeseen events, observations, data points, or items in your dataset that deviate considerably from the rule. These anomalous data points can give an indication of critical events, such as a technical glitch, or prospective openings.
For example, by looking into following and analyzing the data associated with them, one can take out deviations from the normal trend.
- Web page views
- Daily active users
- Cost per lead
- Cost per click
- Revenue per click
- Bounce rate
- Churn rate
- Average order value
Thus, these deviations, aka anomalies, in return, let us know if this is a technical glitch or a prospective opening. In other words, the benefit is yours whether you find a glitch or an opportunity. If you wish to learn more about anomaly detection, read our article on Anomaly Detection in Power BI-3 steps tutorial
Neural Networks map the data through supervised learning. This means mimicking the interconnections of human brain through layers of nodes. Where each node is made up of inputs, weights, a threshold, and an output. So, data is processed through nodes. That is to say, if the output exceeds the provided threshold, it activates the node and passes the data to the next layer in its network. This technique of giving a threshold helps to determine the accuracy of the model.
K- Nearest Neighbor (KNN)
K- Nearest Neighbor (KNN) is an algorithm that works on the assumption of the proximity of data points to the other data. In simple terms, it classifies the data points by assuming the data points that are close to each are more like each other than other data points. Thus, this supervised technique helps to predict the characteristics of a group based on individual data points.
A decision tree asks for input from a series of questions and predicts an outcome or classifies the dataset based on responses given and a set of decisions. As the name states, a decision tree makes use of a tree-like visualization to show the possible outcomes of these decisions.
Prediction Analysis, as the name says, utilizes historical data and builds graphical or mathematical models to predict future outcomes. Not to confuse with regression analysis, this technique seeks to support an undetermined figure in the future based on the data at hand.
What is the Process of Data Mining?
Data analysts commonly go through some extra tasks alongside the data mining process. These tasks are listed below. Without these tasks, data analysts can perform the job of data mining but generally, run into issues midway through or at the end. So, it is a better practice to adhere to the following steps:
Following are the phases in the process of data mining
- Understanding the Business and its Objectives
- Understanding the Data
- Preparing the Data
- Building the Model
- Evaluating the Results
- Implementing and Monitoring the Changes
1) Understanding the Business and its Objectives
This is the hardest but most important phase where data analysts and business stakeholders need to do mutual work to define the business problem. And for data analysts, here comes the extra effort to understand the business by answering the following:
- What are business objectives?
- For Problems, the analysts need to find answers to
- What problems are we trying to crack?
- What data do we need to solve the problem?
- And for goals, the analyst needs to answer the following
- What goals does the company need to reach through data mining?
Many tend to rescue time at this step or skip at all, but it is where the project gives erroneous outcomes or vague answers to questions.
2) Understanding the Data
After defining the problem, an understanding of proper data is taken into consideration. The data to be collected needs to be relevant to the problem and can come from multiple sources. The goal of this phase is to certify that the data precisely involves all the required data sets to deliver the objective.
3) Preparing the Data
So, it is where the data is mined, and the data analyst gets a hand with the information. It is the most time-consuming phase of the process and consists of three steps known as ETL. That is, data is extracted from multiple sources and compiled. It is then transformed through analysis, which may involve scrubbing for outliers and duplicates, standardizing, evaluating for errors, and subjecting to logical testing. Also, it is where the data is checked for its size, which may slow down the computation, analysis, and loading. In the final step of this phase, transformed data is loaded into the database for use.
4) Building the Model
In this phase, the relevant data set is subjected to an appropriate modelling technique to precisely answer the questions built in phase one. These modelling techniques include statistical methods, mathematical techniques, or algorithms, which were discussed above in data mining techniques. To clarify, it is a frequent practice where a data set is subjected to multiple modelling techniques to answer a specific question or fulfil a specific goal.
5) Evaluating the results
This is a human-directed phase where the efficiency of a model in answering the questions is evaluated by running the project. So, this phase determines if the model output is meeting the objectives or not. In the event of an erroneous or contrary outcome, either different data is prepared, or a different model is created.
6) Implementing and Monitoring changes
After evaluation, the company implements the strategies using the knowledge obtained through data mining. This phase involves the company’s management, which not only implements but also monitors the changes. Afterwards, it decides if the information or findings were strong and relevant enough to change course or not. Whatever the outcome is, the company reexamines the effects on business and keeps the process of data mining in the loop to tackle any business problems or opportunities.
Benefits and Applications of Data Mining
Some of the applications of data mining in different industries as well as within organizations are
- Telecom, media, and technology companies mine their voluminous customer data to predict customer behaviour. Thus, enabling themselves to offer highly targeted campaigns.
- Insurance companies with data mining can deal with fraud prevention, risk management, and customer churn.
- Data mining enables education institutions to make use of their student data to predict student performance and achievements, introduce intervention programs, and identify students for extra coaching.
- In the manufacturing industry, aligning supply plans is equally important as quality assurance and investment in brand impression. To clarify, data mining is way more helpful in augmenting the uptime and aligning the production line with the schedule by predicting asset depreciation and foreseeing maintenance.
- For the banking industry, the ever-growing clientele, and incessant transactions are the heart of their financial system. Data mining, for such financial services companies, provides a better position for risk and compliance management, fraud detection and prevention, and investment management.
- For the retail industry, data mining is a core process to get hidden insights from a gigantic customer base, forecasting sales, optimize product prices, and run targeted campaigns that generate a major impact on customers.
- Human Resource Management always remains in need of different data mining techniques to process employee retention, promotions, and salary ranges, driving the company towards goals and getting employee satisfaction surveys.
Limitations of Data Mining
Following are the five major drawbacks and limitations of data mining
- Data mining tools are complex and demanding technical skill sets.
This can be difficult for smaller companies acting as a stumbling block to entering data mining.
- It does not guarantee 100% results
There are multiple ways to analyze data and firms can infer and implement decisions from solid data, but still not be able to acquire benefits as expected.
- Demands large databases
Data mining is powerful when done on a large dataset which obviously demands a large database.
- Expensive Subscriptions
Data tools, including infrastructure, databases, and data mining software to share, store and analyze data, come up with heavy subscriptions. This is way too costly for small companies to afford.
- Security and privacy concerns
Many companies share their data with other companies providing data mining services which put them at risk of data and personal information breakage. Even in-house data analysts stay at tight spots for the security and privacy of sensitive information.
There is no doubt about the fact that data mining plays a core role in today’s world. Data mining enables every industry and sector to harness the power of their big data and sustain their businesses and customer needs with smart decisions. When weighing benefits and applications against limitations, the greater good of benefits and applications makes it quite evident to invest in such data mining tools rather than affording long-term setbacks that will lead businesses nowhere in today’s market.