Advances in technology and prevalent automation of hitherto manual processes, coupled with the willingness of individuals, groups and businesses to adopt information technology has triggered availability of voluminous data-sets.
Many organizations are inundated with massive data from sources as diverse as customer master data, customer transactions, research agencies and so on. However, despite having access to such data-sets, many organizations are still unable to transform their data into reliable information or knowledge for decision making.
Reminds me of the popular adage; “don’t allow soap to enter your eyes when you are at the riverside”.
Visualize the following scenario:
“The federal government has implemented the Treasury Single Account (TSA) policy. This impacts negatively on the bottom-line of most commercial banks because public sector funds currently constitute a large portion of bank deposits. This necessitates a change in approach as banks now have to re-strategize and woo the new bride – private sector deposits.”
The challenge is that most of the banks have similar strategy, processes, technology and human resources. This stimulates the need for a different approach.
Who will bell the cat? Data!!!
Organizations should aim to derive better value from the large volumes of data available to them by leveraging on data mining and knowledge discovery techniques and processes. Data will make more sense to them as these techniques enable them to identify hitherto hidden patterns or trends and make better decisions.
Let’s go back to our scenario above.
Taking advantage of knowledge discovery techniques like classification and association, the banks can cluster their private sector customers into groups based on sources of income, levels of income, deposit \ withdrawal trends and so on. Based on the identified groups or patterns, relevant products or packages can be launched and channelled to the applicable customer clusters.
Employees in localities or states where salaries are usually delayed might be interested in Salary Advance Products.
High income earners with regular income might be interested in fixed deposits or other investment vehicles.
Customers of child bearing age are likely to be more interested in products targeting their children.
The density of individuals who understand a particular language might indicate a need to add more language options to Automated Teller Machines in certain locations.
This is just a minute indication of the possibilities inherent in optimal use of data. Organizations should endeavor to enhance their business intelligence by exploiting knowledge discovery and data mining techniques as they aim to achieve their strategic objectives.
Kindly share your thoughts.