Modern Data Management — The Need for Speed and Scalability

modern data management platform

There’s a good chance that your data management capabilities are outdated. Chances are also good that this isn’t totally your fault—the rise of big data caught a significant number of organizations flat-footed—so you shouldn’t blame yourself too much for having data management processes and systems in place that aren’t the newest and the best.

Organizations of all sizes struggle mightily with the modernization that’s needed to update their data architecture to handle the kinds of data analysis and real-time decisioning required to engage the always-on consumer. And, as the pace of business change accelerates, organizations that were already behind fall further and further back because their IT teams focus on day-to-day operations instead of innovating for the long term.

This is an issue for your organization’s future, and this very modernization was the topic of a recent Database Trends and Applications-hosted panel discussion that I participated on with Chuck Wenner, Sr. Director Global Technology — Unified Compute Platform for Hitachi Data Systems, and Aerospike Co-Founder and CTO Brian Bulkowski.

Wenner pointed to several trends he’d seen in the marketplace, including that organizations haven’t spent nearly enough time updating their Tier 1 data stores over the years. This has resulted in problems with modernization and left legacy companies behind when digital disrupters born in the cloud arose and started taking business away.

To adapt, Wenner suggests that organizations first optimize their current technology portfolio through extending the value of their current software licenses and also updating platforms to add more capability. He also recommended switching to cloud solutions and using virtualization to add mobility into your data management workloads.

Bulkowski meanwhile made the case for two simultaneous speeds of IT, which he likened to splitting the system of record from the system of engagement. From a functional perspective, this means deciding which systems need to be real-time and which ones don’t. Transactional systems of record, for example, could leverage traditional databases and move at a slower pace … while systems of engagement would move in real-time to account for the constantly changing interactions organizations have with customers.

This all comes down to crafting a modern data management architecture that allows for speed, scalability, and flexibility in the face of changing business requirements and market realities. This is something we’re familiar with at RedPoint, and which I discussed at length on the webcast.

At RedPoint we accomplish this through our no-code approach that allows organizations to process complex data sets 500% faster than Apache Spark and 1,900% faster than MapReduce. We devised this no-code approach to remove friction from the marketer’s day-to-day life so they could remove friction from their customer’s lives.

That’s really what the IoT revolution comes down to, after all. If you can remove friction from internal processes and, by extension, from your customers’ lives, then you will have managed to adapt effectively to the new world of big data and the empowered customer.

big data benchmark

George Corugedo

George Corugedo

A former math professor and seasoned technology executive, RedPoint Chief Technology Officer and Co-Founder George Corugedo has more than two decades of business and technical experience. George is responsible for directing the development of the RedPoint Customer Engagement Hub, RedPoint’s leading enterprise customer engagement solution.

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