What is data mining?

How data mining works

Data mining helps in answering those questions that cannot be handled by basic query and reporting techniques. Data mining is marked by several key identifiers that are explored in more detail below:

Automatic recognition of patterns

Data mining models are the basis of data mining and automatic recognition refers to how these models are executed. Data models use established algorithms to mine the data over which they are built. However, most models can be generalized to new data. Scoring is the process of applying any model to new data and assessing the appropriateness of fit.

Predicting most probable outcomes

Several data mining forms are predictive in nature. One example of this would be a model that predicts individual income based on education and demographic. Each of the predictions made comes with some probability to indicate the possibility of each one coming true.

In other cases, predictive data mining can result in the generation of rules. These are certain conditions that imply a specific outcome. One example of a rule would be one that specifies that if a person has a college degree and lives in a particular section of town, their income is likely to be above the average in the region. Such rules come with associated support – the percentage of an area’s population satisfies this rule.

Place spotlight on naturally occurring groupings

There are also forms of data mining that show natural groupings within large data. A particular model may focus on a population segment within a specific income range, which in turn holds a good track record in driving and rents out cars for holidays each year. Such information can be useful to rental agencies as well as insurance companies.

Types of data mining

There are several types of data mining, including the following

Linear regressions

With linear regression, a business can predict a continuous variable’s values with the help of one or several independent inputs. This method is often used in the realty business to predict home values based on variables such as square footage, year of construction, and zip code location.

Logistic regressions

In this variation, one or more independent inputs are used to predict the probability of a categorical variable. You will see this utilized in banking systems that use it to predict the chances of a loan applicant defaulting on loans based on their credit score, income, gender, age, and a host of other personal factors.

Time series

These are forecasting tools where models make use of time as the fundamental independent variable. Retailers often make use of this model to be able to predict the demand for products and work on their inventory accordingly.

Classification / regression trees

Classification or Regression Trees are predictive modeling techniques where the value of both the categorical and continuous target variables can be predicted. The model creates binary rule sets based on this predicted data to classify and group the largest proportion of target variables that are alike under new observation heads. With these rules, the new groups that are created go on to become the predicted value of the new observations.

Neural networks

Neural networks are designed to work in a manner that is similar to the functioning of the brain. Just like stimuli causes the firing of neurons in the brain that enable action, neural networks use inputs with a threshold requirement. These inputs will ‘fire’ or ‘not fire’ its node based on magnitude. These signals of firing or non-firing combine with other such responses that may be hidden in the multiple layers of the network. The process goes on repeating until an output is created. The benefit is a near-instant output, and this technology is used extensively in self-driving cars for efficiency.

K-nearest neighbor

This is a technique that relies on past observations to categorize new ones. Rather than models, K-nearest neighbor is driven by data. Here, there are no underlying assumptions made about the data. Neither are there any complex processes that are used to interpret data inputs. New observations are classified by identifying the closest K-neighbors and assigning the majority value.

Unsupervised learning

This is where underlying patterns are observed based on data that comes from examining unsupervised tasks. Several recommendation systems use unsupervised learning to track general user patterns and give them personalized recommendations for better customer interaction. Some analytical models that are used in unsupervised data mining include:

• Clustering
• Association analysis
• Principal component analysis
• Supervised and unsupervised approaches in practice

Why is data mining important and where is it used?

The volume of data that is being produced each year is phenomenally huge. And, what is an already gargantuan figure is doubling every two years. The digital universe is made up of around 90 percent unstructured data – but this does not mean that the more volume of information, the better the knowledge. Data mining aims to change that, and with it, businesses can:

• Sift through a lot of repetitive information in an organized manner.
• Extract relevant information and make the best use of it for better outcomes.
• Quicken the pace of well-informed decision-making.

You will find data mining central to the efforts in analytics across a wide variety of sectors. Here is a look at how some of them are using it.

The communications industry

The communications industry, marketing or otherwise, is highly competitive and deals with a customer who is being pulled in several different directions. Using data mining methods to understand and sift through vast amounts of data helps this sector create targeted campaigns that ensure a larger number of successful sales and customer interactions.

The insurance sector

This sector often has to deal with compliance issues, a wide range of fraud, risk assessment and management, and customer retention in a competitive market. With data mining, insurance companies are in a better position to price products well and create better options for existing customers while encouraging new ones to sign up.

The education sector

Data-driven views of a student’s progress enable educators to provide them with better-personalized attention where needed. Intervention strategies can be built early on for groups of students who may need them.

The manufacturing industry

A breakdown in the production line or a dip in quality can result in huge losses for any manufacturing industry. With data mining, companies will be able to plan their supply chains better. This means that early detection of possible breakdowns can be spotted and dealt with, quality checks can be more intense, and production lines face minimal disruption.

The banking industry

The banking sector relies heavily on data mining and automated algorithms that help make sense of the billions of transactions that take place in the financial system. With this, financial organizations will get a bird’s eye view of market risks, detect fraud quicker, manage their compliance with regulatory requirements, and ensure they get optimal returns on their marketing investments.

The retail sector

With the astronomical number of retail transactions taking place, there is a lot of data that the sector can use for better insights into their consumer. Data mining helps them develop to improve their customer relations, optimize their marketing campaigns, and forecast sales.

The process of data mining

As outlined below, there are four basic steps in the data mining process.

Defining the problem

The first step in any data mining project is to understand the objectives and requirements. This has to be specified from the business perspective and should have a basic implementation plan in place as well. If the business issue is being able to sell more – the data mining problem will be ‘what kind of customer is likely to make purchases of the product?’ The implementation begins with creating a model based on data such as earlier customer relations and attributes, including demographics, family size, age, residences, and more.

Data gathering and preparation

The second phase covers data collection and exploration. An examination of the data collected will give you an idea of how accurate the fit is to be a base to address your business issue. At this stage, one may decide to do away with some data parameters or bring in a few new ones. Here, data quality issues can be addressed and scanned for possible patterns in the data.

The data preparation phase covers tasks such as table, case, and attribute selection. It also includes data cleansing and transformation, duplicate removal, standardizing input titles, and other data checking.

Model building and evaluation

In step three, various modeling techniques are chosen and applied, and parameters are calibrated to the optimum levels. At this beginning stage of model building, it is best to work with a smaller, well-thought-out set of data. Evaluating again at this point, how the model addresses the business issue is a good idea. Any forms of improvement can be added at this stage.

Model deployment

In the final deployment stage, insights and actionable information can be derived from the data collected. This knowledge can then be deployed within a target environment. Deployment can include the applying the model to any new data, extracting model details, integrating models in applications and more.

Challenges of data mining

Without a doubt, data mining is a powerful process, but it does come with its share of challenges, particularly since it deals with growing quantities of complex big data. Collecting and analyzing all this data only continues to grow more complicated. Here is a look at some of the most significant challenges associated with data mining:

Big data

There are four major challenges when it comes to big data:

• Volume: Large volumes of data involve challenges of storage. Additionally, sifting through such large amounts of data involves the problem of finding correct data. Processing is slower when data mining tools deal with such volume.
• Variety: At a given moment, vast varieties of data are collected and stored. Data mining tools have to be able to handle the many kinds of data formats, which can be a challenge.
• Velocity: The speed at which data can be collected these days is much higher than it once was, which can potentially pose issues.
• Veracity: The accuracy of these vast volumes of data can be challenging, especially considering the factors of volume, variety, and velocity of the data. The main challenge in this case is balancing the quantity data with the quality of data.

Over-fitting models

These are complex and make use of one too many independent variables to arrive at a prediction. The risk of overfitting increases with a rise in volume and variety. The result is the model begins to show natural errors in a sample instead of displaying underlying trends. Lowering the number of variables results in an irrelevant model, while adding too many restrict the model. The challenge is finding the right moderation of the variables used and their balance in predictive accuracy.

Cost of scale

With an increase in volume and velocity, companies need to work on scaling up models to utilize the full benefits of data mining. For this, companies need to invest a range of heavy-duty computing power, servers, and software. This may not always be an easy budget allocation for companies.

Privacy and security

Storage requirements are constantly on the rise, and companies have turned to the cloud for their needs. But with this comes the need for high-end security measures for data. When data privacy and security measures are undertaken, a range of internal rules and regulations need to come into force. It requires a change in the manner of working, and this is a steep learning curve for many.

Relevant data is critical to the functioning of any business in these competitive times. Data mining helps organizations strategize better. Data mining is the key to helping businesses gain that edge. Doing it right is what matters the most.