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Overview Edit

In the early days of computing, data management and storage was a very new concept for organizations. The traditional approach to data handling offered a lot of the convenience of the manual approach to business processes (e.g. hand written invoices & account statements, etc.) as well as the benefits of storing data electronically.

The traditional approach usually consisted of custom built data processes and computer information systems tailored for a specific business function. An accounting department would have their own information system tailored to their needs, where the sales department would have an entirely seperate system for their needs.

Initially, these seperate systems were very simple to set up as they mostly mirrored the business process that departments had been doing for years but allowed them to do things faster with less work. However, once the systems were in use for so long, they became very difficult for individual departments to manage and rely on their data because there was no reliable system in place to enfore data standards or management.

Seperate information systems for each business function also led to conflicts of interest within the company. Departments felt a great deal of ownership for the data that they collected, processed, and managed which caused many issues among company-wide collaboration and data sharing. This seperation of data also led to unncessary redundacy and a high rate of unrelibable and inconsistent data.

Pros and Cons of the Traditional ApproachEdit

Pros

  • Simple
    • Matched existing business processes and functions
    • Company's were not as interested in funding complicated information sytems
  • Initially low-cost
    • Early computing was not viewed as beneficial for large funding
    • Systems were designed to be cheap in order to save on cost


Cons

  • Seperated ownership
    • Business functions had a high sense of data ownership
    • Departments unwilling to share data for fear of minimizing their superiority
  • Unmanaged redundancy
    • Multiple instances of the same data appeared throughout various files, systems, and databases
    • Information updated in one place was not replicated to the other locations
    • Disk space was very expensive, and redundancy had a big impact on storage
  • Data inconsistency
    • Redundant data stored in various locations was usually never stored the same way
    • Formatting was not centrally managed
  • Lack of data sharing
    • Same data stored in multiple locations
    • Caused unneccessary doubling of efforts for processing and managing data
  • High costs in the long run
    • Hiring data processors for each department was very expensive, and each position was typically working on the same thing just for a different area
    • Doubling of work as well as excessive maintenance costs

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