Data management and analytics have been essential to business strategy and growth over the last ten years. But arguably, they have never been more critical than they are in our current climate, as businesses navigate the pandemic and struggle to plan for the future.
As this continues to play out, expect a convergence of data, analytics, integration, and DevOps to emerge, creating a new environment for companies to leverage AI to address business challenges and opportunities. With this in mind, Michael O’Connell, TIBCO’s Chief Analytics Officer, predicts four major data analytics convergence trends to watch out for in the coming months:
- The Convergence of Data Management and Analytics: As we’ve written about before here at the APEX of Innovation, the pandemic forced many companies to accelerate their digital transformation efforts. From a data convergence perspective, increased AI adoption means that automated and low code data prep and machine learning can be incorporated into Business Intelligence (BI), analytics, data science, business process, and app-dev working environments. We call our approach to this “Hyperconverged Analytics.” Automating routine tasks in these areas frees up time for innovation and business optimization. As this trend increases, expect to see a greater need for data literacy across all functional areas and organizational levels.
- Creative Combinations of In-Motion and At-Rest Data: All data begins as business events that arrive at many velocities, and users consume analyses based on these events at many different frequencies..In the years ahead, in-motion and at-rest data will combine and give rise to closed-loop, self-learning systems in operations. As Michael puts it, “Specifically, I see a downtick in solutions that involve moving data and an uptick in solutions where data are analyzed directly on the event stream, within delegated cheap storage systems, and in fluid combinations of the two.”
- The Emergence of Data Hubs as a Convergence Bridge Between Hybrid Data Assets: Rolling updates from multiple disparate data sources are essential for fueling enterprises’ multi-cloud, AI-centric environment. Michael believes that we will see new ad-hoc data combinations emerge as the economy reopens and companies respond to changes in social behavior stemming from the pandemic. Convergence analytics on health status, building access and occupancy, and crowd density are just a few examples that we can expect to see in the post-COVID era.
- The Convergence of Data Science with DevOps, ModelOps: Greater collaborations between teams of data scientists, DevOps, and ModelOps developers to manage data science apps in production is not only a natural byproduct of remote working, but a byproduct of business seeking talent wherever talent lives and works. As ModelOps continues to grow as a discipline, this convergence will accelerate, giving rise to a new machine learning (ML) engineering persona. According to Michael, “In their [the engineer’s] role, they configure deployment scenarios in hybrid cloud environments, working with data scientists, data engineers, business users, DevOps, ModelOps, application development, and design teams to manage deployments, monitor and update data science workflows in production environments.”
It’s impossible to predict every one of the myriad business challenges that will arise throughout 2021, but it’s safe to say that convergence analytics will be vital to addressing them. Read more of Michael’s thoughts on the above predictions and other trends we can expect to see throughout the year.