Data mining is a collection of computer technologies that an

Data mining is a collection of computer technologies that analyze information to discover previously unknown and potentially useful information, including relationship s and patterns. By applying data mining technologies you can predict future sell trends and customer behaviors. You can identify patterns of financial fraud and terrorist attacks.

• Explain why data mining has become so important in today’s business world.

• Application areas of data mining technologies in business, which include government, finance, banking, and marketing. Please identify one application area and conduct research on the Internet to find out how data mining is being studied and applied in these areas.

Solution

1.

Big data is the key to a business’s success, big data will change the world, and big data will do this and that. How many times have we heard statements like these? Well I’ve lost count!! Big data and analytics are certainly the buzzwords today and the statements around big data being the differentiator between a successful business and a not so successful one are absolutely true, no denying that.

Today the amount of data being generated around the globe every single minute is humungous. When we sit down to look at this tonnes and tonnes of data and understand the way it is being used, it’s imperative that we understand the power of data analytics. If we make the data we have available to somebody else without data mining being in the picture all that somebody knows is what we have told them. Now, if we bring data mining back in the picture then not only does the third person know what we have told him/her but can also guess a great deal more. Simply put data mining enables a business to use the information provided by a customer to reveal more than the customer could ever imagine.

To most, data mining is where tonnes of data is collected and then data scientists/wizards work a spell on it and the data starts talking and reveals things nobody ever thought of. But how? Well for the most part, data mining tells us about very large and complex data sets, kind of information that will be readily apparent about small and simple things. However, a task that seems quite simple with 5 or 6 data-points is not that simple with data-points that run into billions.

Today the amount of data that’s being generated is far more than we can handle, almost every single activity or interaction leaves a trail that somebody somewhere captures, stores and analyses. Just the size of this data has gone beyond human-sense capabilities and at this scale it’s almost impossible to detect patterns just by looking at the data. This is where data mining comes into the picture, it automates a part of this process to detect interpretable patterns.

Deriving insights from data or data analytics can be classified into three forms: Descriptive, Predictive, and Prescriptive.

If you look at data mining, it more or less fits into the first bracket i.e. descriptive. Data mining simplifies and summarizes the data making it easier for us to understand and derive our conclusions about specific cases/instances basis the patterns that data mining throws up. In other words, data mining describes the situation, explains what’s going on currently and helps you understand the situation in its entirety.

How does it help businesses? For e.g. with data mining a company can identify their most profitable customers, offer that customer base better prices, also helping the company to accelerate its product innovation cycle. Data mining can help companies understand their current supply chains better and optimise it better.

This was all about defining data mining, its use and importance. Now, coming to the data mining tools, you have a variety of techniques, including neural networks, and advanced statistics to locate patterns within the data and develop hypotheses. There are analytic tools like querying tools and today, live web-connectors and even analytics in the cloud which allows people, business and governments to be more nimble and agile, even as they conduct in-depth data mining.

All of this does make data mining appear as this one really complicated field for which you need to have some special skills and should be ready to spend big monies to make sense of your data. With the rise of self-service analytics, there are tools or software available today provide you with many data mining workarounds depending upon the level and scale of data mining you expect. These self-service analytic tools not just help you understand your data better but also do it without leaving a hole in your pocket.

2.data mining used in tax collections

Tax Collection Optimization Solution (TACOS) for NY State DTF
An example of predictive analytics embedded in a key business process
Challenge
– Optimize tax collection actions to maximize net returns, taking into account
• Complex dependencies between actions
• Resource, business, and legal constraints
• Taxpayer profile information and behavior in response to preliminary actions
– Approach also suitable for optimized management of debt collection and accounts
receivables
Solution
– Combines predictive modeling and optimization to implement the predictive-analytics
equivalent of look-ahead search in chess playing programs (e.g., Deep Blue)
– Generates the logic that determines action sequencing in the tax collections workflow
– A related approach is used in Watson to optimize game-playing strategy for Jeopardy!
Benefits
– $83 million (8%) increase in revenue 2009 to 2010, using same set of resources
– 22% increase in the dollars collected per warrant (tax lien)
– 11% increase in the dollars collected per levy (garnishment)
– 9.3% decrease in age of cases when assigned to field offices

please follow the page nos 11-16 in pdf file i have attached,ibm research on data mining:

https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=8&cad=rja&uact=8&ved=0ahUKEwiiuqjA8MzPAhVGNI8KHdJyC-sQFghNMAc&url=https%3A%2F%2Fwww.siam.org%2Fmeetings%2Fsdm11%2Fapte.pdf&usg=AFQjCNFnjbbnoF2c3g1SS9GD0sVFsnL0sg&sig2=6rgQ85YkxkS8iEdGNwgOkA