The succinct version of this story starts with the fact that We were most of us in any business environment used to be paid to optimize business processes in silos ? whether we realized it at the time or not. For those of us who were lucky enough to be involved in information technology in the second half of the 20th century ? encouraged to automate these silos. This led to huge productivity gains when all these processes were brought together ? with little attention paid to the We were technical debt that was created when each new business application system came with its own data set.
ERP and other application
Suites made great strides in at least putting logically similar data together in dataset their databases ? but built in little to no capabilities to enforce broad data quality and semantics in the supported business processes. This led to a disparate set of “logical” silos within a single physical how to use keywords data store. At the same time ? specialized applications such as CRM emerged ? again increasing independent productivity but compounding the issue of it looks at the words on the page trusted and reusable data.
The technical debt of poor
Data quality was first widely exposed in the 1990s with the advent of data a angola latest email list version warehouses and data marts and the first attempts to consolidate and reconcile source data from these disparate data silos for basic reporting and analysis. The emerging “data analysts” found that the data in these systems did not conform to the surface rules within each system. Worse ? the meanings of seemingly similar data attributes in these systems bore little resemblance to each other. Because mainstream IT (i.e. ? those responsible for production operational systems ? etc.) had yet to realize the value of these analytical efforts ? the complaints of these analysts were largely ignored for the next decade or so. Some gave up ? but others simply created new silos in the form of analytical platforms that ? if not quality ? at least made data available for analysis.