- DTS and the Flow of Data Through the Enterprise
- Business Drivers for Enterprise Data Transformation
- Ways to Use Data
- Sources of Data
- Meta Data
- Types of Data Storage
- Conclusion
Business Drivers for Enterprise Data Transformation
As you consider the design of your enterprise data structure, you need to continually examine the business drivers that are involved. What is the business purpose for transforming data?
Data transformation is a task for the database administrators and the database developers in the Information Systems department. But deciding on the purpose and goals for data transformation is a top-level management decision. Data transformation can be very expensive, but the availability of high-quality data can be very profitable.
It's possible for an organization to do too much data transformation. Data can be transformed into a variety of contradictory formats, so that business analysts are left wondering which version of the data to trust. Organizations sometimes move their data into data marts or a data warehouse without a clear understanding of the financial gain to be realized from that effort.
But it's also possible for an organization to do too little data transformation. Many businesses are leaving their data in incompatible systems and they can't view their corporate operations as a whole. Many businesses also are not taking advantage of the possibilities of OLAP and data mining. Significant information about customer behavior is being ignored—information that is readily available in existing transaction systems and Internet clickstream logs.
Data transformation has to be driven by business needs. What are the financial benefits that can be achieved by transforming data in a particular way? Is the financial benefit of data transformation greater than the expense?
Here are some of the factors that push organizations to invest in data transformation systems:
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Effective response to customers—Customers are expecting rapid response to their orders, requests, questions, and needs. Data about these customers, their orders, and the products they are ordering has to be available so that an immediate, accurate, intelligent, and appropriate response can be given.
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Efficient use of resources—An organization needs to know what resources it has, where those resources are stored, and when they're needed. If all this information is available for making decisions, resource availability can be improved, movement of resources can be optimized, and excess inventory can be cut.
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Efficient processes—Purchasing, manufacturing, advertising, financial management, and personnel management can all be improved by analyzing the available data. The data generated by each business process can be captured and analyzed to find opportunities for improvement.
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Dealing with complexity and change—Business realities are changing more rapidly than ever before. Whereas in the past businessmen could rely on their experience to make intelligent estimates, there is now a greater need to make decisions with actual data. Last year's commonly accepted reality might not be true this year. Data is needed to understand the rapidly shifting reality of modern business.
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A competitive advantage for you or for someone else—All businesses compete in a global environment. If you can use your data more effectively than your competitors do, you can gain an advantage over them. If other companies are using their data to improve their business processes and you are not, you will start slipping behind. Your customers will know whether or not you are providing them with the highest possible level of service.
Sometimes it may be possible to calculate a specific financial gain that results from transforming data in a particular way. I have seen this done most precisely in situations where data analysis results in inventory reduction.
If the goal of your data transformation is improved customer satisfaction, the calculation of your financial gain probably won't be so precise. However, it might be even more significant for your organization's overall success.