Corporations routinely make decisions based on remarkably inaccurate or incomplete data, a bad habit that's a leading cause of the failure of high-profile and high-cost IT projects such as business intelligence and customer relationship management deployments, a research firm says.
"Most enterprises don't fathom the magnitude of the impact that data quality problems can have," said Ted Friedman, principal analyst with Gartner. According to his research, a quarter of the Fortune 1000 companies are working with poor-quality data.
Friedman isn't talking only about corrupted data -- although that can be a part of the problem -- when he points to the pitiful state of data. Instead, data quality is defined by a number of components, ranging from consistency --whether the data is identical when stored in multiple locations -- to accuracy and relevance. If there's data, but it's not relevant to the process or project at hand, it's worthless, said Friedman.
Other data problems stem from the fact that the data which was collected is often incomplete.
"More important is the fact that enterprises haven't done a good job of collecting all the data that they should have, or have, but it's not in sync across the enterprise," he said. Synchronization will become an even bigger issue for businesses in the near future as they struggle to integrate the enormous amounts of data gathered from RFID projects.
To realize the benefits of cloud-native software, an application must actually be cloud-native—designed to run in the cloud, interacting with disaggregated cloud services, fully manageable as code—to deliver the expected benefits.
By combining resources and expertise, the AI-Enabled ICT Workforce Consortium will offer a blueprint for how industries can adapt to an AI-dominated future.
IT leaders should heed the guidance of cybersecurity insurance providers who think businesses should prioritize security education, incident preparedness, regular internal audits, and ongoing vulnerability scanning and patching.
