Proactive data quality system 'could save time'
Implementing a more proactive data quality management system could save businesses time.
Suvendu Dataa, a data warehouse team leader with a large insurance company, told Search Data Management that organizations "have to be proactive".
The expert added: "I think you should take care of data quality [in] the design phase."
Doing so can not only save time, but should also ensure more reliable information and ultimately help business workers to make better decisions.
However, the problem with implementing a designed data quality management strategy is that it can be difficult to justify the investment in improving the data capture and validation processes that occur 'upstream' from the data warehouse.
Such a strategy can take years to perfect, Rob Karel of Forrester Research Inc remarked.
On the flip side, 'downstream' tools are easier to justify because they produce more tangible results faster.
"The data in the data warehousing environment [isn't] created there," said Mr Karel.
"So any business intelligence or data warehousing professional trying to build out a solution is at the mercy of the data coming in. They're really just chasing their tail when implementing batch data quality within the data warehouse."
Suvendu Dataa, a data warehouse team leader with a large insurance company, told Search Data Management that organizations "have to be proactive".
The expert added: "I think you should take care of data quality [in] the design phase."
Doing so can not only save time, but should also ensure more reliable information and ultimately help business workers to make better decisions.
However, the problem with implementing a designed data quality management strategy is that it can be difficult to justify the investment in improving the data capture and validation processes that occur 'upstream' from the data warehouse.
Such a strategy can take years to perfect, Rob Karel of Forrester Research Inc remarked.
On the flip side, 'downstream' tools are easier to justify because they produce more tangible results faster.
"The data in the data warehousing environment [isn't] created there," said Mr Karel.
"So any business intelligence or data warehousing professional trying to build out a solution is at the mercy of the data coming in. They're really just chasing their tail when implementing batch data quality within the data warehouse."
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