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Modern data architectures fuel innovation

Modern data architectures fuel innovation

Modern data architectures fuel innovation

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“Each industry is driving digital transformation in its own way,” says Naveen Kamat, executive director and CTO of data and AI services at Kyndryl, an IT infrastructure services provider. “They are setting up their own applications in the cloud, which generate data daily. Then there’s the web and social media data coming in. The enterprise data estate is getting much, much bigger; it becomes much more complex to manage.

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The insurance sector provides an example of the current complexity of the data landscape. According to Ali Shahkarami, Chief Data Officer at Allianz Global Corporate & Specialty (AGCS), one of the major challenges to good data management in the insurance industry is the plethora of legacy systems built over the years. “This is especially true for international companies operating across borders with different products, regulatory requirements and reporting requirements,” he notes. “The ability to do this centrally and cohesively is a big challenge. This impacts everything you build with data and analytics. »

Unfortunately, as data management has become more difficult, data management skills have become harder to find. The number of data-skilled personnel has remained the same or even decreased over the past decade, even as the number of data silos and applications have grown, says Gartner. This means that it takes longer than ever to meet the needs of integrated data analysis.

The consequences for organizations that fail to manage their data effectively and efficiently become dire. On the one hand, the cost of inadequate data management is rising. The cost of poor-quality data can represent about 20% of revenue, estimates Thomas C. Redman, president of consulting firm Data Quality Solutions, in an article co-authored by MIT Sloan Management Review.

“Almost all work is plagued by bad data,” write Redman and Thomas H. Davenport. “The salesperson who corrects errors in data received from marketing, the data scientist who spends 80% of his time manipulating data, the finance team who spends three-quarters of his time reconciling reports, the decision maker who does not does not believe numbers and asks his staff to validate them.

Redman and Davenport estimate that less than 5% of companies use their data and data science to gain competitive advantage. “Companies are failing to grasp the strategic potential of their data,” they conclude.

When it comes to implementing advanced technologies, such as machine learning and artificial intelligence, inadequate data management is a significant barrier. Not only could AI programs be ineffective, but “without the right data, building AI is risky and possibly dangerous” if data bias, diversity, and systematic labeling aren’t part of it. a data management strategy, says Rita Sallam, Distinguished Vice President and Analyst at Gartner.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not authored by the editorial staff of MIT Technology Review.

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