Understanding data management best practices
There are certain
data management best practices to follow, but again they are not necessary
to be implemented for all the industries. Each and every industry and
even the organization can follow their own set of best practices that
depend on the process they follow.
Different companies that have different processes and business would need to follow different best practices that increase the return on investment. Ultimately they must ensure that data is accessible easily at all times and provides value to their business.
Organizations that have standard applications and packaged solutions for data management would have different practices when compared to those that have many custom developed applications that suit their business environment. The objective of data management is the use and reuse of accurate and reliable data by the ultimate users of the data management systems. If the usage is easy for the ultimate users and the data is reliable and accurate then the objective would have been met.
Maintenance of data for such interactions would count to be a best practice in data management. Typically it is found that organizations do not focus on data management best practices from day one. They ignore it until the data accumulates and it gets out of hand.
Organizations should focus on the practice that gives them value to the investment made. However you cant expect the returns immediately. So give a gestation period of at least 6 months to see the positive impact of the practice on your organization.
Upgrading and fine tuning the existing data management applications, using a good data model to store data, following standards in naming and data integration across the enterprise along with quality enforcement and data ownership are some of the best practices that might apply to all the industries.
Always make sure that the methodology used in data management is documented and is easy to use by the staff and can be modified when necessary.
| Choosing an effective data management solution | Different components of a data management process | Effective data management strategies for your organization | Features to look for in product data management software | Groundwork for effective project data management | How to implement a product data management system effectively | Outsourcing your work to professional data management services | Preferred storage for life cycle data management | Pros and Cons of distributed data base management system | Tiered storage and data lifecycle management | Understanding data management concepts | Understanding data management definition | Using the right data management techniques to increase efficiency | What are the data management best practices to follow | What do you understand from the definition of data management | What is data base management | What is product data management |
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