DTS and the Flow of Data Through the Enterprise
- 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
Enterprise Data Structure and Data Flow
Business Drivers for Enterprise Data Transformation
Ways to Use Data
Sources of Data
Types of Data Storage
Organizations have both an increasing need and an expanding capability to transform data.
Some of the increasing need for data transformation comes from a desire to improve and extend transaction processing systems:
Data is being published from the existing transactional systems to the Internet.
e-Commerce is bringing data from the Internet into the existing systems.
Clickstream logs (usage reports from web servers) are providing more information about customer behavior than has ever before been available.
As companies merge, data needs to be moved from multiple-source systems into a unified format.
Data is extracted and merged from a variety of sources to build new Customer Relationship Management (CRM) systems.
As a company uses more data, there are increased needs for data archiving.
There are also many companies that are developing Business Intelligence systems. Businesses want to view and analyze data more quickly and effectively. These systems often require a very significant amount of data transformation:
Moving data into an enterprise-wide data warehouse.
Moving data into a multidimensional format for OLAP, as discussed in Chapter 4, "Using DTS to Move Data into a Data Mart."
Homogenizing data that is being extracted from a variety of sources.
Data cleansing, because poor-quality data cannot be effectively used for Business Intelligence.
One of the most important reasons that companies are transforming more data is that the technology used to manage data works better and costs less than ever before. There have been rapid improvements in both hardware and software:
Disk drives for storage of large amounts of data.
Multiprocessor servers with larger amounts of memory, which can transform more data more quickly.
OLAP and data mining software, such as Microsoft Analysis Services, that make it possible to gain new insights from good-quality data.
Data transformation software, such as Data Transformation Services in Microsoft SQL Server 2000.
The purpose of this chapter is to examine the flow of data through an organization and show how DTS can be used for a significant portion of that data transformation.
Enterprise Data Structure and Data Flow
It's important to look at data from an enterprise-wide perspective.
Data transformations are often created on an ad hoc basis. A manager wants data in a particular format for a specific report. This data is currently located in several different data stores. A new set of tables and the data transformations to fill those tables have to be created to fulfill this one request.
It's more efficient when an organization can take a comprehensive view. If enterprise data is in different source systems, consider bringing it together into a consolidated database, as is done in data warehousing.
Data is a critical resource for the modern enterprise. The flow of data through an enterprise should be examined so that the use of this resource can be maximized in every possible way.
Here are some things to consider when you're looking at enterprise data flow:
What data is needed by each of the departments in the enterprise?
What data is needed for ongoing operations?
What data is needed for Business Intelligence?
What data is available in each department?
How does the data need to be transformed?
How is data stored?
How is data archived?
Who makes the decisions about data transformation, data storage, and enterprise data flow?
No matter how effectively you manage your corporate data assets, you will still receive ad hoc requests for data in a particular format. But if you analyze the needs of your organization, you can design a data structure that will more effectively serve the whole enterprise.