Business use of datamining
When used wisely within Customer Relationship Management applications data mining can significantly improve the bottom line. It will end the process of randomly contacting a prospective or current customer through a call centre or by mailshot. With the effective use of data mining a company can concentrate its efforts on targeting prospects that have a high likelihood of being open to an offer. This in turn gives the ability for more sophisticated methods to be used such as campaigns being optimised to individuals.
Businesses that employ data mining techniques will usually see a high return on investment, but will also find that the number of predictive models can quickly increase. Rather than just implementing one model to predict which customers will respond positively, a business could build a different models for each region and customer type. Then instead of sending an offer to all prospects it may only want to send to prospects that have a high chance of taking up the offer. It may also want to determine which customers are going to be profitable during a certain time frame and direct their efforts towards them. To be able to maintain this quantity and quality of models, these model versions have to be well managed and automated data mining implemented.
Human Resources departments can also make a valid case for using data mining. It will allow them to in identifying the characteristics of their most successful employees. Information gained from such as resource can help HR focus their recruiting efforts accordingly.
Another example of data mining, is that used in retail. Often called market basket analysis, it is, for example, when a store records the purchases of customers, it could identify those customers who favour silk shirts over cotton ones; or customers who bought certain grocery items would also also buy the same specific item as well. This is often highlighted in on-line stores when you are told that so many people who bought a certain book or CD also bought XX as well.
Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules may also be present within a database. In a manufacturing application, an inexact rule may state that 73% of products which have a specific defect or problem will develop a secondary problem within the next six months.
About the Author:
Mike has more than 15 years ox experience designing and implementing Data warehouses
based on Oracle, MS SQL Server, MySql, PostgreSQL and more
he is currently working for DB Software Laboratory
Questions and Answers
To use or not to use? That the question a lot of IT managers asks themselves.
ETL stands for extract, transform and load. These are the processes that enable companies to move required data from a multitude of sources, reformat and cleanse it, and then load it into a data warehouse for analysis, or maybe onto another operational system to support another business process.
Describes what s the dirty data and how to deal with it
Many companies viewpoint of data integration is blinkered, they see it as something that is purely IT department related and is involved with the mythical subjects of ETL and data warehousing. They are missing, and therefore not utilising the power behind these tools to harness all the information held by the company and use it productively
Data integration can be seen as the practice of collecting, collating and then managing customer information that may have come from a variety of sources available within your company. Once this data has been consolidated will includes the details of the customer, customer valuation data, as well as information that has been acquired using direct marketing or interaction, such as campaign that require a “call to action”.
Data warehouses are designed in such a way that they are optimised for speed of data retrieval. To assist in speeding data retrieval, the data stored in the data warehouse is often stored multiple times, both in their most granular and summarized (aggregated) forms.
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Many companies viewpoint of data integration is blinkered, they see it as something that is purely IT department related and is involved with the mythical subjects of ETL and data warehousing. They are missing, and therefore not utilising the power behind these tools to harness all the information held by the company and use it productively
ETL stands for extract, transform and load. These are the processes that enable companies to move required data from a multitude of sources, reformat and cleanse it, and then load it into a data warehouse for analysis, or maybe onto another operational system to support another business process.
Data warehouses are designed in such a way that they are optimised for speed of data retrieval. To assist in speeding data retrieval, the data stored in the data warehouse is often stored multiple times, both in their most granular and summarized (aggregated) forms.
Data integration can be seen as the practice of collecting, collating and then managing customer information that may have come from a variety of sources available within your company. Once this data has been consolidated will includes the details of the customer, customer valuation data, as well as information that has been acquired using direct marketing or interaction, such as campaign that require a “call to action”.
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