5 Data and AI Trends for 2019

What are some of the big data-oriented trends for enterprise programs and culture in 2019? Here's a look.

New year, fresh calendar page. If 2019 looks anything like 2018, you can bet that data, analytics, machine learning, and other forms of artificial intelligence will be part of a developing plan in enterprise IT organizations. Plans will turn into pilots. Pilots will turn into production.

But the year ahead will not be without challenges.

For instance, 2018 may very well be remembered as the year where the data breach or data leak went a little too far for many consumers. Facebook revealed several of these events, and its CEO appeared before government committees, answering questions about privacy and security. That wasn't last year's only challenge.

So what's ahead in 2019? InformationWeek has interviewed and corresponded with a number of industry thought leaders and asked them what to expect. Here are some of the trends they identified for the year ahead.


Data and AIOps

It's been a decade since the industry started using the term and concept of DevOps, which is defined in many different ways by many different people, but comes down to a philosophy of cooperation between software development and operations to create value for the company. The terminology also encompasses a faster and more scalable development process and the concept of infrastructure as code.

Today we are seeing other functional technology areas being joined together with "Ops" to create a faster, more scalable development iterations designed for business value. In 2019, look for DataOps and AIOps to be part of the conversation. DataOps appeared on the Gartner Hype Cycle for data management for the first time in 2018 as an "innovation trigger."

"DataOps will become much more important over time," said Dan Potter, VP of product management at Attunity, an Israel-based data integration and big data management solutions provider.  "How do I take those same DevOps principles and apply them to data to deliver on business objectives."



Maybe your organization is already openly multi-cloud, but some organizations, such as Capital One do choose a preferred provider for cloud services.

There are reasons for standardizing on a single provider. It's just easier and simpler to work with a single company, both in terms of internal operations and in terms of standardizing your technologies. But there are arguments for using multiple providers as well. While it's simpler to use a single provider, everyone wants to avoid the dreaded vendor lock-in -- when it gets too difficult to migrate your software infrastructure to an alternative provider. Maybe your preferred vendor has changed its terms of service or pricing or quality-of-service guarantees. It's good to keep your options open.

You may already have a multi-cloud strategy. Even if you claim that you are all-in with one provider, you may not be, according to Dave Russell, VP of enterprise strategy at Veeam, and who previously worked as a distinguished analyst at Gartner.  

"Some organizations say they are not multi-cloud today, but they probably are," Russell said. "They may not be aware of it, but somebody in their organization has probably deployed a different SaaS application." Those rogue implementations are more likely to be drawn under the purview of IT in 2019. Russell believes that this year we will see data center people embrace multi-cloud as a reality.


Workforce, skills, and hiring

The job market for those with specialized skills -- for instance, data science, machine learning, or Python -- has been very tight and will continue to be tight. Organizations are poaching talent from competitors and from universities. There's more demand than supply of these skills, and organizations have been evolving to adapt with more effort to create self-service tools that a business generalist is able to use.

"It's very difficult to hire very specialized people," Russell said. In some cases, organizations may be coming up with new workforce strategies to meet this challenge. For instance, some may be reframing how they think about it. Instead of hiring specialists, Russell said organizations could think horizontally and hire generalists with a range of skills and the ability to learn. These workers should understand the strategy and be in it to deliver a business outcome.

Another approach some companies have employed is to create small teams, with each member bringing a different skill. Such a team could include a statistician, a software developer,  and a business expert, for example.


Read the full article here:

About the author:
Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, predictive analytics, and big data for smarter business and a better world. In her spare time she enjoys playing Minecraft and other video games with her sons. She's also a student and performer of improvisational comedy. Follow her on Twitter: @jessicadavis.