The world of business intelligence and data analytics has matured significantly over the last decade. In tandem with this maturation is the explosion of data; organizations now have an ever-growing volume of information to mine in order to make more informed, data-driven decisions.

While the constantly increasing volumes of data mean that the analytics ecosystem is a continuously evolving one, a recent CIO piece outlines a few big data trends to be mindful of as companies look to evolve their analytics programs in 2021 and beyond.

Stateful Application Modernization

Enterprises are increasingly modernizing their applications to be deployed via container-native infrastructure. Under the legacy approach, these workloads were stateful and data-centric, but embracing container technology allows them to become more cloud-like. As a result, companies can gain the elasticity and agility needed to deploy anywhere and improve the efficiency of at-scale deployments.

From a data science and data analytics perspective, it’s important that organizations ensure the container platform can both support all their applications and deliver data at a petabyte scale.

Solving for App Dev and Data-Intensive Workloads

While it’s natural to want to minimize the number of solution providers, the CIO piece cautions against companies inadvertently damaging their analytics programs by stretching container technology to new spaces.

A particular tool may be extremely effective from an app developer perspective, but that’s no guarantee that it can rise to the challenge of running petabyte-scale analytics. To stay competitive in 2021, businesses need to keep in mind the importance of using the right technology for the right job—even if this means multiple co-existing platforms to complement existing solutions and address emerging use cases.

Edge Analytics Challenges

As we’ve written about previously at the APEX of Innovation, the edge is here, and with this trend comes a handful of challenges. To start with, companies must seamlessly combine data from numerous edges, multiple clouds, and on-premise systems and devices, all while providing a unified view of this information. In addition, it’s critical to be cognizant of security concerns and ensure that your edge strategy doesn’t leave the organization vulnerable to attack. As such, companies must look for a solution that can deliver intelligence at the edge while also employing the next-generation security strategies necessary to keep their data secure.

For more on these and other important data analytics considerations, check out the CIO piece in its entirety here.