4 Data Management Trends to Watch for in 2018

by   |   December 28, 2017 12:23 pm   |   0 Comments

Ron Agresta

Ron Agresta, Director of Product Management – Data Management Product Line, SAS

A number of trends have influenced enterprises over the past few years, including the migration of data and processing to the cloud and the adoption of machine learning and artificial intelligence (AI) solutions. However, the one common thread that weaves through everything enterprises do today (and will continue to do in the future) is data management. Organizations are creating and consuming more data than ever before, and as that data flows between applications and locations, I believe that we’ll start to see an entirely new mind-set when it comes to data management – one that focuses on and prioritizes governance, value and security. Mature organizations have already begun this journey.

Here are four predictions (and challenges) I believe organizations will face over the next year and my thoughts on how to navigate the future.

  1. The Importance of Governance Will Reach Its Tipping Point in 2018

Governance is a growing challenge as more data moves from on-premise environments to cloud locations and as governmental and industry regulations, particularly regarding use of personal data, become more pervasive.

For example, consider the General Data Protection Regulation (GDPR). Starting in May 2018, organizations will be accountable for personal data protection, including how and where data is stored and how it is processed. This regulation does not affect just businesses based in the European Union (EU); any organization that handles or manages EU customer data must adhere to GDPR. This means adopting a sound data governance strategy will be critical.

  1. Organizations Will Need to Extract Real Value from Machine Learning and AI Projects

Machine learning and AI have continued to make waves across a wide range of organizations and industries. But misconceptions still dominate headlines. Instead, we should be having conversations focused on the real business benefits that can be gained from these technologies.

By moving beyond the hype of machine learning and AI, organizations will be able to extract real value from projects in these areas, including deciding if and where technologies such as natural language processing fit into broader enterprise data management and analytics projects. To do this, it’s important for organizations to get the correct balance of “offensive” (being agile and exploratory with data) and “defensive” (governance and control of data) approaches to solving data-centric problems.

Even more interesting for data management activities is the use of machine learning techniques to improve and automate routine data ingestion, correction and provisioning activities. With the ever-growing pace of data creation and consumption, advanced analytics infused into data management processes is the only way to cope – simply adding more people to manage data will not work.

  1. Big Data and Data Management Will Continue to Affect Organizations in 2018 and Beyond

Companies are looking at variations of data lake concepts that combine Hadoop Distributed File System (HDFS) infrastructure, event stream processing, relational and nonrelational data stores and other technologies. Making all these work today in a performant and auditable fashion can be challenging.

A unified data management approach can intelligently combine data in different forms and from different velocities into a managed data platform, making it easy for data consumers to find, correct and combine data for downstream reporting and advanced analytics activities.

  1. Organizations Will Prioritize Data Protection

We continue to hear about the latest security breach or critical vulnerability that results in the loss of important corporate and individual data. But organizations face data loss every day, thanks to more variables than just malicious attackers. As organizations create and store data across enterprise applications, social media platforms, cloud applications and elsewhere, comprehensive data protection strategies will need to be prioritized. To do this task, organizations will need to take a holistic approach to data protection that encompasses every aspect of safeguarding data, including corruption, disaster recovery, theft and more.

As 2017 proved, there is no telling exactly how enterprises and technology will evolve in 2018, but if there is one prediction that is sure to be validated, it’s that organizations will continue to create data at an astronomical rate. To capitalize on this prolific data creation, data management – including governance, machine learning and AI and data protection – will be the difference in turning big data headaches into big data opportunities.

Ron Agresta, Director of Product Management – Data Management Product Line, SAS
As the Director of Product Management for all data management offerings at SAS, Ron Agresta works closely with customers, partners and industry analysts to help research and development teams at SAS develop data quality, data governance, data integration, data virtualization and big data software and solutions. Ron holds a master’s degree from North Carolina State University and a bachelor’s degree from The Ohio State University. 

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