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Artificial Intelligence and DaaS

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More than 3 years ago, I wrote a IDC Community post, “Using Robots to Curb Labor Shortage in Chinese Manufacturing” highlighting a factory in China that replaced 90% of the people in the factory with automation and robots. In that case the workforce was reduced from 650 employees down to only 60 people, those remaining were doing drastically different work than those jobs that were replaced. The jobs shifted from manual labor to oversight, maintenance, and support of the automation and robotics systems.

The proliferation of data types and quantities should be a major advantage for enterprise organizations. More data and more types of data should offer complex insights into challenges and opportunities in how the business runs and should lead to better decisions and business outcomes. However, ask any data analyst, and they’ll share this reality: data analysts spend a bulk of their time on search, data preparation, management, and governance activities, and not on data analytics where the true value lies.

There are two primary ways to buy or trade data in the Data as a Service (DaaS) market – direct sales from a data provider to end users, or via a data marketplace. While large, established information services businesses continue to make direct sales to their customers, many are also participating in data marketplaces. For smaller and emerging providers of DaaS, the rise in data marketplaces has made it simpler for them to package and sell their offerings, and for potential customers to find them. Marketplaces simplify the searching process, providing a variety of sources and types of data, along with a ready group of potential buyers.

It’s been said that all businesses are technology companies in the age of digital transformation. It’s also true that many are becoming information businesses as the amount and value of data they produce and consume continues to increase. In fact, business leaders and CIOs will find themselves not only missing opportunities but also at a competitive disadvantage if they don’t leverage data assets before markets are crowded with competitors.

Data governance is no longer optional for enterprise organizations. Aside from complying with new regulations, such as the General Data Protection Regulation (GDPR), organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. But just because businesses understand the value of data governance, doesn’t mean that enterprises are confident in their abilities to execute on it.

Data governance is no longer optional: regulations such as GDPR will start to be enforced; and organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. Because data is a digital asset, and has mostly been managed within the realm of IT, organizations are quick to look at technology, expecting to find data governance software and solutions; but technology is only part of the solution.

What if you discovered oil in your backyard? How would you get it extracted from your property and deliver it to customers? Where would you sell it and what would you charge? If the world’s most valuable resource is no longer oil, but data, how does your enterprise leverage the data it already creates and manages to turn what is ‘in your backyard’ into a sustainable revenue stream?