The Challenges of Data Management

While the hardware required to store massive amounts of data becomes cheaper with each passing year, the resulting explosion of stored data content means that companies are forced to devise innovative new ways to meet the challenges of processing this ever-growing wealth of information. Simply storing everything forever because of the low cost of storage media sounds like a good idea to the uninformed, but massive amounts of information stored in databases and flat files can make retrieval, purging, and archiving a difficult process. Recent electronic data laws that require specific periods of retention to allow for auditing in the event of fraud or other wrongdoing only serve to complicate matters even further.

Best Practices for Data Management

Like any other data processing area, the experts in Anthony Ricigliano Data Management have compiled a list of best practices. While each item will not apply to every organization, the individual IT shop should choose the practices that work well for their particular data storage model. With the growth in data warehouses, a data management strategy is critical to the overall success of virtually every business area. Rules and code should be created to make sure that each piece of data is always accurate, that it means the same thing to everyone and every system, and that everyone has access to the most current information.

Data Stewardship

A data steward maintains the metadata registry and ensures each data element's integrity. This would include making sure that each data element has a clear and precise definition, that the data element is not duplicated unnecessarily, and that each data element has clear and up-to-date documentation that specifies valid values, data sources, and data destinations. When the data element is no longer required, it should be immediately removed from the file structure. Data stewardship ensures consistent use of a defined field between multiple computer systems, allows for easier mapping of data, and reduces migration costs.

Model Driven Integration

By using Unified Modeling Language (UML), some IT shops are using the model-driven strategy to provide application integration solutions. This is an attempt to reduce the costs of meeting the ever-changing demands of the current business world by quickly adapting the existing software infrastructure. It attempts to separate business logic from the underlying system so that individual components can be reused without the need to change them. With this theory, data storage should be kept independent from application design and organized according to the business needs.

Active Data Model

Relational databases are the storage method of choice for most organizations that require retention of massive amounts of data with fast retrieval times. The Active Data Model "actively" refreshes the data that is seen at the client level. The client retrieves data in its current state. Next, it tracks the data created, deleted, or modified by the user, and then passes the information back to additional services for validation prior to permanent storage. Because data at the client level is always up-to-date, code designed to set up or manipulate the data can often be reduced or eliminated.

Organizational Challenges

As the amount of stored data grows, so do the organizational challenges. While no one wants to keep out-dated information, it has become increasingly necessary to do so in case of audits or legal challenges. Old data must be archived once it is no longer needed for instant retrieval, but it must still be kept somewhere that it can be accessed fairly easy when necessary. Due to inconsistent classification of data between systems or between organizations, substantial effort and cost is wasted in trying to reconcile data. In many cases, both systems will be correct, but they may be following different data management rules. When data elements are stored in multiple file systems, data errors can become a major problem. One system may be updated before another one, or certain systems may not be updated at all. When two or more computer systems are merged together due to the growth in mergers and acquisitions, it only compounds the problems if an aggressive data management strategy is not used.