What Is the Difference Between Big Data and Data Mining?

Differences between big data and data mining are fundamental. Big Data is a term that refers to the storage of big and disparate chunks of data in a way that is efficient for storage and retrieval, while data mining is the tool for extracting meaningful insights from it.

Characteristics of Big Data

Volume, Velocity and Variety are the three universally accepted defining properties of big data. In a sense, Big Data is a relative term where your organisation’s quantum of data has outgrown its IT infrastructure. It can neither handle the Volume, nor the Velocity with which data needs to flow in and out of information systems. The Big Data problem is further compounded when a wider ‘Variety’ of data needs to be accommodated for competitive analysis but your infrastructure is in no position to support it.

Other characteristics of Big Data include Veracity and Value. Can every gathered data point be trusted? With unstructured big data come ambiguity, noise and inaccuracy. The cost of this inaccuracy in Big Data is huge. Hence, it has become essential to consider assigning a data veracity score to the uncertain big data sets. Finally, the most important component is Value. Does investing in data mining tools/ techniques add value to the organization? These are the questions that every business needs to answer before diving into Big Data.

Think of big data as a massive reservoir of data that holds all the answers to your current problems and any questions that may pop up in the future. Due to the aggregating nature of big data, most data in this reservoir is unstructured and completely raw. The dots are all there, waiting to be connected as and when queries are made.

Collecting “Big Data” is only one part of the picture. The explosion in data size and their complex nature necessitates the usage of sophisticated tools and techniques to mine data. Microsoft Excel and SQL queries may have worked for smaller and medium sized data sets, but do not make the cut in the new world of data mining. Read more about the Excel limitation when it comes to big data analytics in our post 10 Excel Limitations that Make True Business Intelligence Impossible.

Data Mining and Your Organization’s Big Data

It’s important to understand the relationship between big data and data mining, because this relationship is evolving with today’s rapidly changing business and technological landscape. We are now seeing a trend where businesses are cosying up to Self-Service BI solutions like DataScout due to the shortage of well-qualified data scientists.

Data mining is the process of sifting through tons and tons of data to find nuggets of information. This process can be manual where data scientists with a background in statistics and computer science will use their exceptional skills to find those nuggets.

Alternatively and preferably, data mining can be an automatic process where intelligent algorithms do all the required scavenging, thereby ensuring accuracy and saving thousands and millions of man hours.

Data Mining Techniques

Here is a gist of few important data mining techniques and their applications. These basic methodologies can be applied to data, either independently or in tandem with each other, irrespective of the size of data sets.

Association rule is used by retailers to target customers based on their purchasing history or buying habits. This technique can be used on Big Data where the need is to bring a person closer to sales by showing personalized ads and recommendations.

Classification model uses machine learning algorithms to identify the class to which new data in the dataset belongs to. For example, classification techniques can be applied in banking to classify borrowers into low risk and high risk.

Clustering is segmenting or assigning data with similar characteristics into homogeneous groups. These groups are not predefined. For example in healthcare cluster analysis can be used to identify patients having similar symptoms of a disease are grouped together.

Prediction is used to know how a dependent variable will react to the changes in independent variable. For example, banks utilize prediction technique to identify if the first time borrower is a good or bad borrower.

The above mentioned techniques are used by Data Scientists, Data Analysts and Engineers. These data professionals build tools and interfaces too for the business side of the organization. These tools help managers in the organization answer simple to complex business questions and make decisions faster. If you’d like to learn more about data mining techniques, Kurt Thearling has published a thorough post on the topic called An Overview of Data Mining Techniques.

DataScout’s Data Mining Tool

Data mining and big data are two different things but their coexistence has given birth to many sophisticated, advanced and user-friendly tools. DataScout is one such free and easy-to-use tool that stands out when it comes to meeting your data mining needs. It has several features that can help organizations discover the insights locked inside their data. If you can use mouse or a trackpad you are good to go! That is what we say when we talk about DataScout’s user friendly features and interface.