We know that in 2019, demand for data management work will outstrip supply. We’ve seen those numbers. Dataversity’s analysis of this 2019 trend attributes this to the exponential growth in the need for business data, pushing firms to constantly look for “advanced data collection and storage.” Central to businesses’ needs are data engineers – “architect[s] of organizational data planning” whose job role “will take center stage in 2019. In fact, data teams will not be able to function without these super-techies as they are assumed to have multiple programming skills and advanced technical knowledge to prepare the groundwork for Enterprise Data Management, which can be used by other staff like the Data Analyst or the Data Scientist for specific data-oriented functions.”

Here are some other thoughts behind or beyond the numbers:

First, shifting to an Internet of Things means there will be a need for humans to evaluate the needs of other humans. We’re drawing a lot of the information and data for this post from Cynthia Harvey’s recent set of tech predictions for 2019. On IoT, Cynthia predicts a massive increase in firms’ deployment of and planning around “live production networks” in 2019. This will require the refinement of smart devices, and the “smart” will require an infusion of data analysis and management skills, which circles back to the need for those skills.

Second, Artificial Intelligence isn’t a panacea and doesn’t come close to solving the shortage of human expertise in the short or medium term. Growth in AI will be nowhere near growth in IoT technology. In fact, Cynthia cites a Deloitte survey revealing that while over half of enterprises were currently using integrated AI, and “37% planned to do so within two years,” around twenty percent had stopped current AI projects and around another twenty percent “had decided not to start one because of cybersecurity concerns.”

Those numbers indicate a couple of bumps in the road for AI, which will also spur investment in tools enabling “ordinary people” to use AI to become virtual data scientists, throwing the ball back to businesses doing their own data science.

Third, as the data management field grows, those data users in the field of political technology also want “ethical infrastructure” in place to ensure equity and efficiency in the unique ways we use new technology. One important example of this is the Open Supporter Data Interface (OSDI) coalition, providing like-minded developers with an open standard for interoperability between software applications, reducing integration time and headaches. It’s a great example of cooperation within a relatively competitive field, a sign that this kind of nuanced tech can be socially shared, to the benefit of stakeholders.