When to Use Data Mining vs Predictive Modeling

According to a white paper by Splunk, about 55% of an organization’s data is dark. This means that it is either unknown or inaccessible. Nevertheless, nearly all the business leaders think that data is “very” or “extremely” valuable to success.

And even though 92% of industry leaders are willing to learn new data skills, only 57% of them are enthusiastic to work more with data, with nearly the other half saying they are “too old” to adapt to new tech.

With data playing a crucial role in business cycles across industries, technologies like data mining and predictive analytics have become very relevant. Benefits of combining data mining and predictive modeling in enterprises are many, and businesses big or small are looking forward to leveraging the combined benefits of both to uncover new insights from data, and make game-changing decisions.

While data mining and predictive analytics are two collaborative technologies that represent different ways of extracting data, the combined result can play a crucial role in helping businesses make sense of their ever-expanding datasets and make decisive decisions.

This post gives you an overview of data mining and predictive modeling in business analytics and their uses across industries.

How Data Mining and Predictive Modeling Drive Business Decisions

Data Mining and Its Applications

In simple terms, data mining is the process of understanding data through gathering information, data cleansing and classifying, identifying patterns, creating models and then testing them. It is an exploratory process to find answers to business challenges from available historical datasets. It utilizes statistical models, machine learning, and database systems.

Data mining is used to find correlations, patterns, and anomalies in large datasets that are impossible for humans to accomplish otherwise. Data mining is very helpful in unravelling the value in dark data, which is then converted to machine-readable information. Thus, data mining can be considered an important precursor to advanced technologies like AI, machine learning, and natural language processing (NLP).

The information thus processed using data mining is used to provide businesses with two main benefits: describing historical data and uncovering meaningful patterns, rules, and relationships across different datasets that can also predict future outcomes with the help of predictive analytics. In principle, data mining allows businesses to have insights from data about their audiences, past trends, and current conditions, thereby helping them to make actionable decisions.

For example, retailers can make use of data mining to prepare ‘market basket analysis’, which would help boost sales. In retail, market basket analysis is the process to analyze customer purchase history to identify products frequently bought together, like diapers and baby food; floor cleaners and cloth detergents; or even seemingly unrelated things like soap dispensers and hand sanitizers, to optimize product placement and promotions.

In the case of healthcare, data mining can be used to identify patterns and trends in patient data that can help with improved diagnosis and treatment. For example, data mining can be used to identify patterns in patient symptoms, which can help doctors diagnose diseases more accurately and quickly.

Yet another application of data mining is in logistics where large consumer organizations can apply information mining to improve their business cycle to retailers. They can use the results obtained to determine the distribution schedules among outlets, and also analyze loading patterns.

Data mining also plays an important role in big network infrastructures where it is used to detect intrusions by identifying unusual patterns, anomalies, and potential threats within large datasets. It helps classify and extract relevant data to support Intrusion Detection Systems (IDS), which monitor network traffic and raise alerts for suspicious activities.

As the Big Data applications landscape keeps expanding and becoming more complex, there is a growing demand for more data cleansing, transforming, and organizing before it is fit for analysis.

Predictive Modeling and Its Applications

As the name itself suggests, predictive modeling is a process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data, statistical models, machine learning, and AI to forecast activity, behavior and trends.Unlike in data mining, predictive modeling is a mathematical process that uses statistical algorithms, machine learning and database systems but to analyze input data to forecast or predict future or unknown events. Predictive modeling is more proactive than exploratory.

Although the primary goal of predictive modeling is to forecast the future, it can also predict current results like verifying whether a transaction that is already done is fraudulent or not. In this case, predictive modeling is used to predict whether future analysis will find the transaction is fraudulent. Further, predictive modeling can also be used to predict future requirements.

There are many types of predictive modeling which can be combined to produce the best results. Nevertheless, these models can be classified as ‘supervised’ or ‘unsupervised’. Supervised learning models use machine learning techniques such as neural networks to identify patterns buried in historical data, while unsupervised learning models use statistical models to interpret data directly using techniques like logistic regression, decision trees and time series.

The major difference between the two models is that data sets need to be carefully and properly labelled upfront before employing machine learning techniques in case of supervised learning models. The use of the different models is based on the domain where predictive modelling is employed rather than the industry.

For example, when applied to a hospital scenario, simple statistical regression (unsupervised) is good enough to identify key pain-points in scheduling, but a more sophisticated approach like using neural networks (supervised) would be needed to assign the right patients to the right doctors.

The most common application of predictive modeling is in weather forecasting, but it can also be employed for businesses across industries. In the case of online advertising and marketing, modelers use users’ historical data to determine what kinds of products they might be interested in and what they are likely to click on. Predictive modeling techniques like Bayesian spam filters can be used to detect spam mails, and in case of customer relationship management, predictive modeling can be used to target messaging to customers who are most likely to make a purchase.

Predictive modeling is also used widely for predictive maintenance like in the airline industry, where engineers employ predictive models to monitor performance of aircraft parts like landing gears, fuel pumps or jet engines. These tools enable pre-emptive deployment of maintenance resources to increase equipment utilization and limit unexpected downtime.

These are just a few examples of how businesses use predictive analytics for smarter decision-making.

Conclusion

As mentioned in the beginning of this post, data mining and predictive modelling are two separate but complementary processes that help us convert raw data into useful value. While the former helps in classifying and filtering the right data, detecting anomalies and duplicate records, predictive modeling in the mix adds value to it as these data can be then used to draw meaningful conclusions.

DeepKnit AI has industry-leading experts who understand data in much better ways than many and can help your business by implementing the right techniques required, and improving ROI with predictive modeling and data analytics.