In 2017, the big data market came of age. The market grew at an accelerated pace thanks to an increasing number of organizations that understand data is their most important business asset. These savvy companies established teams and employed tools to optimize data’s operational and competitive value. And there’s no end in sight to the focus on data: IDC forecast the U.S. market for big data and business analytics solutions will reach more than $95 billion by 2020.

As data takes center stage, one of the key takeaways is that it’s no longer enough to stockpile massive amounts of data and cross your fingers that users will find what they need to do their job.

This year, organizations will need to address the usability of data to ensure that users can find, understand and trust the data. This will involve protecting the rights of “data citizens” (all users in an organization who use data), increasing regulations to manage risk, and taking advantage of more self-service business intelligence (BI) tools.

Here are four predictions for data in 2018:

#1 - Data Protection Will Get Personal

Consumers are becoming increasingly aware of the amount of data businesses collect and store about them, especially in light of the proliferation of high-profile data breaches in 2017. They’re rightly asking questions about how organizations use, protect, and secure their personal information. Data protection will undoubtedly be on everyone’s radar during the coming year, particularly as the General Data Protection Regulation (GDPR) goes into effect in May. Organizations will need to demonstrate not only that they are protecting the data, but also that they are using the data for good.

#2 - AI Will Expose Organizational Deficiencies

During the past year, organizations of all sizes began investing in AI. But despite the many benefits AI promises to deliver, many businesses have discovered that AI doesn’t come without challenges. In 2018, AI will expose data deficiencies and reveal where data processes fall apart as organizations struggle to answer simple question, such as:

  • Where is the data I need for my AI project?
  • What is the data’s quality - and is it reliable?
  • Who owns the data - and who can fix it?
  • What can the data be used for – taking into account commercial, legal and even ethical goals?
  • How do I embed the outputs from AI into day-to-day business operations?

As AI begins to take hold, it will be important for organizations to build governance into their projects from the get-go. A solid governance foundation means AI analysts can find the data they need, know what it means, how it can be used, and gain transparency into its quality and ownership.

Data Catalogs Will Become the Biggest Data Craze

Data catalogs will be the talk of the town in 2018 as more organizations finally take note of their business users’ desire for a more “consumerized” approach to access the data. Be aware, though, that not all data catalogs are created equal. Some catalogs are nothing more than a big list of data elements. With some research, it’s possible to find a data catalog that connects the list of data elements to meaningful information in an established business glossary. This will enable business users to find data and trust that data to drive business decisions.

Data Management and BI Tools Will Become Commodities

Data management and BI tools have been around for years. But in 2018, data management and BI tools will become commodities and more vendors will build these tools and offer them in the cloud or via open source. This shift will show that self-service tools and the reports they generate are here to stay, but having proper data governance integrated with these tools is a necessity to ensure that the analysis is accurate.

The data world is changing, and organizations would be wise to keep pace with the latest developments and trends. Data can lead to insight. It can help organziations answer their most critical business questions. Organziations that wish to unlock the hidden value of their data can begin by instituting a data governance framework that will make data easy to find, understand and easy to trust – supporting these and other emerging data trends.

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