Data warehouses have been around for decades. As one of the longest-lasting methods for managing data, many warehouses — still in use today — are plain antiquated. Modernizing these resources has been a priority for many enterprise systems, but upgrading just for the sake of it is never a good business model. Instead, modernization needs to be focused on generating returns and improving operational efficiency. These three growing trends can help steer any modernization strategy in the right direction.
Automation is the key to efficiency in every other industry. Why would data warehouses be any different? Advances in data warehouse automation can be applied at every step in the life cycle. Automated scripting tools help with development. Testing automation reduces problems and improves deployment. Even maintenance can benefit from new tools that are increasingly available and affordable.
Machine learning is an exciting phrase to throw into any technology discussion. While it could be classified as a branch of automation, it’s important to understand how this specific application of AI can revolutionize data management.
The long story made short is that data troves are growing at a geometric rate. Every day, more systems implement Internet of Things and comparable data collection. Human effort simply can’t keep up. Machine learning is perhaps the most affordable way to make a scalable system that grows in efficiency even as it has to tackle expanding data tables. The potential autonomy of machine learning gives it potential that dwarfs heuristics and other, less self-contained approaches to AI.
Eventually, data warehouse modernization is going to include (or perhaps be replaced by) new methodologies. The two big names in the game are virtualization and blockchain. At the moment, virtualization is seen as an insoluble competitor to data warehouses. Emerging automation and AI tools might be able to bridge that gap soon, and a hybridized system could provide the ease of access of virtualization with the robust reliability of historic warehouses.
Similarly, the decentralized nature of blockchain feels antithetical to the general design of a data warehouse. That is quickly changing. Blockchain networks offer a native way for diverse data nodes to unify tables. Once that transaction is complete, there is no reason the data warehouse can’t perform its traditional role. The potential of hybridized systems can provide real-time analysis, superior data integrity and more useable access.
There are countless options available to modernize a data warehouse. Anything that reduces the workload for human components is sure to improve operational efficiency. Whether you utilize new automation tools, machine learning and AI, or move away from some warehouse traditions, careful execution should bring good returns.