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Artificial Intelligence and DaaS

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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?

While it is a newer technology, blockchain is already disrupting financial services, insurance, healthcare and supply chain solutions. Two main causes of this disruption are blockchain’s ability to enable disintermediation, and how it provides an immutable historical record of data. Blockchain technology itself solves many of the problems that data governance professionals, and data management technology vendors have been trying to solve for years: group consensus on the most recent version of the truth for a given entity, and full instance lineage (provenance) of the data.

While blockchain has been able to transform these solutions, forward-thinkers are asking: will blockchain have the same disruptive effect on data management? There are two main factors to consider when assessing blockchain’s potential to disrupt data integration, storage, and data integrity:

As the market for intelligent applications and the software platforms used to build them has emerged, nomenclature confusion has grown. What should we call these applications, and what should we call the platforms, libraries and software tools used to build them?

The terminology matters. Vendors need to differentiate their products from the business intelligence and predictive analytics software that has existed for decades. ‘Intelligent applications’ and ‘business intelligence’ software provide two very different sets of functionality. For technology buyers who need to justify new solutions to budget holders, the terminology matters too.