Data Mining and Predictive Analytics: Things We should Care About

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Data and tons of data are found at the core of any organization’s success or failure. Businesses are going with all guns to create a better position in the marketplace by understanding their customer base, making constant improvements in their operations, outperforming their competitors and what not!

Now have you ever come across the term data mining? As the name implies, it is an analytic process used to explore a large amount of data in regards to consistent patterns and systematic relationships between variables. Businesses prefer data mining because it aims to predict. Predictive analyses, on the other hand, refine data resources, in particular, to extract hidden value from those newly discovered patterns.

“Data mining + Domain knowledge => predictive analytics => Business Value”

Overall, predictive analysis and data mining, both make use of algorithms to discover knowledge and find the best possible solutions around.

How Does Data Mining Work?

The entire process of data mining consists of three basic stages:

  1. Exploration– The first and foremost stage usually starts with data preparation; i.e., from cleaning data to data transformations, selecting subsets of records and so forth. The primary stage can take place anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyses using a wide variety of graphical and statistical methods. Keeping the nature of the analytic problem in mind, businesses can quickly identify the most relevant variables and at the same time, determine the complexity and/or the general nature of models.
  2. Model building or pattern identification– The second stage is all about learning about several models and choosing the right one for your need. Depending on the predictive performance, you need to conduct such simple yet elaborative process. Although, several techniques can be taken into account such as Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and Meta-Learning. It is interesting to know that many of these are based on so-called “competitive evaluation of models.” This means applying different models to the same data set and then comparing their performance to choose the best.
  3. Deployment- The last and final stage involves the use of the selected model and applying the same to generate predictions or estimates of the expected outcome. Data mining as a business information management tool seems to becoming popular day in day out. However, the only difference between Data Mining and the traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the fundamental nature of the underlying phenomena. Which means it is less concerned with identifying the specific relations between the involved variables.

If you ask me to give a one-liner answer to what data mining is – It is a blend of statistics, AI (artificial intelligence), and database research.

How Predictive Analytics Works

Predictive analytics aims to identify the likelihood of future events based on historical data. By using data, mathematical algorithms and machine learning technology, predictive analytics has the potential to provide the best evaluation of what will happen. Moreover, it offers a perfect view of what’s going on and what needs to be done to succeed.

Let’s understand how it works! With the help of a variety of models, predictive analytics can be performed effectively. One of the most common predictive models is to focus on the behavior of an individual customer. All you need to do is make use of the sample data with known attributes, and you will find the model trained and capable enough to analyze the new data and determine its behavior. Further, the information can also be used to predict how the customer might behave next.

As a result, predictive analytics can offer:

  1. Valuable insight
  2. Increase competitive edge
  3. Predict trends
  4. Identify new business opportunities in time

Make the Most of Data Mining and Predictive Analytics

Knowing what your customers are most likely to do or what they want or how much they are likely to spend to get it, are one of the best possible ways to hit your target audience. For example – think of Netflix binge recommending sci-fi shows, this is a pure example of predictive analytics results.

Furthermore, both the procedures data mining as well as predictive analytics deal with discovering secrets within big data but people often get confused with these methodologies. Data mining uses software to search for patterns, while predictive analytics uses those patterns to make predictions and direct decisions. So it is safe to say that data mining turns out to be a stepping stone for predictive analysis. Apart from this, data mining is passive while predictive analytics is active and has the potential to offer a clear picture.

Being a marketer or business owner, it is imperative for you to navigate the whole world of big data. No matter how intimidating the world of information seems, you need to keep embracing it at regular intervals.

In a Nutshell,

These techniques can surely help in saving money, increasing ROI, potentially convince your customers you’re unique— just like the aforementioned example Netflix.

About the Author

Vikash Kumar working at custom application development company Tatvasoft. Besides his profession he loves to write about Big Data and Data Management. He’s been published on major publications like Entrepreneur, ReadWrite, SAP blog and many more.


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