From a business feature to a necessity – Journey of data mining applications

Author: Mike Connolly

With evolving tools and data management systems, there is an enormous amount of information that gets collated in every enterprise as a regular activity. From attendance logs to resource allocation to marketing, every aspect of business revolves around the effective utilization of these data sets. This is what drives the domain of data mining on the whole. There is a significant development in building specific data mining applications which aid in identification and optimiziation of business performance.

Customer-satisfaction is the primary focus area for any business, be it a product-related or service-based domain. The data collated helps us achieve this by providing a holistic view of buying as well as decision behavior. A traditional approach often is subject to manual errors and hence data mining applications help minimize induced errors at all levels.

Trends in Development

With applications like Hadoop and Big Data the trends are changing and we are witnessing a dynamic growth. There is a component of integration that poses the greatest challenge in the development of Data mining applications. The effective usage of the dataset to attain the desired objective in business is a daunting task. Below enumerated are some key milestones that the data mining industry has achieved:

1. Cohesive Analytics: Programming and Algorithms sorting out data to achieve a contextual purpose is the next level of data mining. The cohesion is achieved with respect to functionality of the data. For example – Attendance logs or number of hours at work is a multi-functional data-set. This could be used by the human resource team as well as the accounts and finance to assess the billing hours for creating proposals.
2. Element of Perpetuity: With the virtual medium being at access mode for most users, it is extremely simple to be collating data seamlessly. There is an exponential increase in the amount of data collected, forcing businesses to analyze and improvise. Applications like Big data and Real time scoring are thus gaining momentum in the market place.
3. Customer Control: The applications developed give the option to the customer to decide the level to which his/her personal data is accessible. This is a step towards development of behavioral analytics where attitudes and behavior can also be assessed. This enables us to make significant assumptions about the consumer and also predict the purchase pattern.
4. Social Media: With growing corporations like Facebook, the need to differentiate and innovate is definitely building a certain pressure for the business to perform. With features like Graph search, Facebook analytics for the business page etc. it becomes very simple not just to collect information but also influence the buying decision.

Way ahead…

From simple analysis to influencing buying behavior, data mining applications have definitely come a long way. With predictive modeling, regressive analysis and cross validation used as common techniques for data mining, we have progressed to methods like Artificial Neural Networks, Discretization and so on which not just collect data but give us critical information to influence the consumers.