“Each line of business is driving digital transformation in its own way,” said Naveen Kamat, executive director and chief technology officer. Data and AI Services at IT infrastructure service provider Kyndryl. “They’re building their applications in the cloud, generating data on a daily basis. Then there’s web and social media data. Enterprise data assets are getting bigger; management is becoming more complex.”
The insurance industry is an example of the complexity of today’s data landscape. Ali Shahkarami, chief data officer at Allianz Global Corporate & Specialty (AGCS), says a significant challenge to good data management in the insurance industry is the multitude of legacy systems that have been built over the years. “This is especially true for international companies operating across borders with different products, regulatory requirements and reporting requirements,” he noted. “The ability to do this centrally and in a consistent way is a huge challenge. It affects everything you build with data and analytics.”
Unfortunately, while data management has become more challenging, mastering data management skills has also become more difficult.Number of skilled data personnel remain the same or even decline According to Gartner, over the past decade, despite the increase in the number of data and application silos. That means more time than ever is needed to meet integrated data analytics needs.
The consequences for organizations that fail to manage their data effectively and efficiently are becoming increasingly dire. On the one hand, the costs of data mismanagement are increasing. Thomas C. Redman, president of consulting firm Data Quality Solutions, estimates in a report co-authored that the cost of bad data can be about 20% of revenue MIT Sloan Management Review article.
“Nearly all jobs are plagued by bad data,” write Redman and Thomas H. Davenport. “The salesperson who corrects errors in marketing data, the data scientist who spends 80% of his time crunching data, the finance team who spends three-quarters of his time collating reports, doesn’t trust numbers and instructs his or her employees to verify them.”
Redman and Davenport estimate that less than 5% of companies use their data and data science to gain a competitive advantage. “Companies are not capturing the strategic potential in their data,” they conclude.
Improper data management is a significant hurdle when implementing advanced technologies such as machine learning and artificial intelligence. Not only can AI programs be ineffective if data bias, diversity, and systematic labeling are not part of a data management strategy, but “without the right data, building AI is risky and possibly dangerous,” Rita Salam saysGartner Distinguished Vice President and Analyst.
This content was produced by Insights, the custom content division of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
