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October 1, 2010

Data Integration: 3 Most Common Mistakes

Filed under: Data Integration, Data Quality, ETL — Tags: , , — Katherine Vasilega @ 4:51 am

Implementing a data integration solution is not an easy task. There are some common mistakes that companies tend to make in data integration. These mistakes result in delayed data integration projects, increased costs, and reduced data quality. Today, I’d like to focus on three most common data integration mistakes that businesses tend to make.

1. Lack of a comprehensive approach

Data integration is not only about gathering requirements, determining what data is needed, creating the target databases, and then moving data. You have to develop a comprehensive data integration approach that will provide for:

• Processing complex data, such as products and customers, in relation to facts, such as business transactions
• Filtering and aggregating data
• Handling data quality
• Capturing changes and avoiding gaps in historical data.

2. Missing data quality requirements

You may think that data quality problems are simply data errors or inconsistencies in the transactional systems that can be easily fixed. The truth is that you have to prevent quality problems at the initial stage of a data integration process. You have to plan how to set data quality requirements, incorporate data quality metrics into your system architecture, monitor those metrics in all your data integration processes, and report on data quality.

3. Using custom coding instead of ETL

While most businesses consider ETL the best practice, there are still a lot of companies that use custom coding to create countless data shadow systems. Keep in mind that custom code makes it difficult to manage and maintain programs, does not offer the centralized storage of programs, limits metadata capabilities, and also has a longer development cycle. Besides, debugging is more difficult with a custom code than with an ETL tool. To add more, an ETL tool usually has a user-friendly interface, provides for centralized storage of programs, and is relatively easy to customize.

Thinking ahead about all these issues before developing and implementing a data integration solution, you are sure to save time, money, and valuable data.

September 23, 2010

Data Integration to Achieve Data Quality

March 12, 2010

What Should Data Migration Plan Comprise?

Filed under: Data Cleansing, Data Migration, Data Quality — Tags: , — Olga Belokurskaya @ 2:22 am

March 9, 2010

The Role of Planning in Data Migration

Filed under: Data Migration, Data Quality, Uncategorized — Tags: , — Olga Belokurskaya @ 3:00 am

March 5, 2010

Database Integration: On the Importance of Data Quality Standards

February 18, 2010

Data Migration: Challenges of Moving Data to a CRM

Filed under: Data Integration, Data Migration, Data Quality — Tags: , — Olga Belokurskaya @ 4:36 am

February 16, 2010

When Developing Systems Architecture Think About Data Integration

Filed under: Data Integration, Data Quality — Tags: , — Olga Belokurskaya @ 6:34 am

February 12, 2010

Data Integration and Data Quality Require Consolidation

Filed under: Data Integration, Data Quality — Tags: , — Olga Belokurskaya @ 10:08 pm

January 15, 2010

Customer Data Integration Among the Top Priorities for Companies

Filed under: Data Integration, Data Quality — Tags: , — Olga Belokurskaya @ 8:35 am

January 12, 2010

Data Migration: Ensure Quality When Moving Data to the Cloud

Filed under: Data Migration, Data Quality — Tags: , — Olga Belokurskaya @ 5:57 am
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