When it comes to IT budgets, data quality management is not factored in, which ultimately costs organizations millions of dollars in lost revenue.
Data quality management continues to be misunderstood by IT departments at enormous cost, according to Janet Brimson, head of the information management practice at independent IT services consultancy iFocus.
The conference, which opens in Sydney today, will address governance, policies and procedures for maintaining data quality.
"IT budgets are overwhelmingly oriented towards new technology purchases and implementations. The business cases rarely take into account the cost of information creation and management that will feed the new applications," Brimson said.
"Information is a corporate business asset. Maintaining the information that an organization already has is very rarely allocated sufficient or any funding, but when information integrity is eroded it can be difficult and expensive to fix."
Brimson said organizations today run so many systems and have so much information that data housekeeping can no longer be ignored.
"A 'housekeeping' budget should be included as part of any IT budget if companies and organizations are to avoid being swamped with data problems in the future," she said.
"Data quality and the management of information throughout its lifecycle is fundamental best practice."
Organizations that do ignore data quality management will eventually feel the impact of a data 'domino effect', according to Brimson.
"Data that is incorrect at the source travels across all systems and applications and wreaks havoc," she said.
But before undertaking a major data cleansing project, organizations need to undergo a risk and cost assessment.
Brimson said risk assessment is particularly vital to parts of an organization that are the most public, that have the most to lose in terms of reputation and financial impact if something goes wrong.
"This process allows organizations to get to the bottom of hidden costs, isolate the source of the domino effect and implement targeted data cleansing," she said.
Brimson recommended organizations include data quality managment as part of their business quality process.
"Data value is measurable, so it is relatively easy to put forward a business case for data quality maintenance," she added.
However, few organizations have any framework or procedure for managing data quality.
One problem with customer data is, garbage in, garbage out. Therefore, data cleansing is critical before undertaking any large projects, particularly when implementing business intelligence.
Jim Goodnight, the CEO of BI vendor SAS, said data quality is a crucial factor in generating meaningful data.
"Products are only as good as the data you have," he said.