Looking back, the world has experienced a series of revolutionary events. The first revolution began in the mid-1700s and introduced the economics of manufacturing and transportation. The second revolution brought the economics of mass production, including electrical power and the advent of the assembly line, in the late 1800s. A third revolution occurred in the mid- to late-1900s and introduced the economics of communication, leading to automated production, electronics, computers, and the World Wide Web. In the 2000s, it’s the data revolution and the economics of storing and processing massive amounts of data for advanced analytics, robotics, and AI.
Current research supports this data revolution. According to 451 Research’s Voice of the Enterprise: Data & Analytics, 2H 2019 study, the median data volumes – including both structured and unstructured data – that enterprises currently have under management is now greater than 630TB, and this figure is expected to exceed 820TB within two years.
But what’s driving this data revolution? Research suggests that a variety of industries will not only be large data producers but also significant data consumers. In telecommunications, 5G is expected to drive new networks and equipment infrastructure that will generate massive amounts of data that firms must manage and monitor. In finance, the explosion of online banking and virtual wealth management is driving financial institutions to implement large digital transformation initiatives, all built around the management of data that it serves to consumers. And in retail, online shopping, especially during the Covid-19 pandemic, is only expected to grow further as consumers shop from a myriad of devices, generating data that vendors must track and process.
Collecting enormous amounts of data has its benefits and there are good reasons why enterprises want to be more data-driven. For instance, accelerating the development of new products and services, lowering overall costs, enabling efficient data access, and responding to competitive market challenges, are just a few of the benefits enterprises anticipate.
Collecting enormous amounts of data also has its challenges. The fact that an enterprise stores all its available data does not necessarily mean that the data is easy to find and analyze. In many cases, overzealous data collection leads to a lot of data silos, which then affects data access and analytical pipelines. Further, these data silos may consist of legacy systems. Most legacy systems were never designed to manage terabytes or petabytes of data and are simply unable to support high-performance querying or broad data access across an organization.
Enterprises need to view data growth, not as a threat but an opportunity. As enterprises look to be more data-driven—that is, leveraging data to make operational and strategic business decisions—it’s worth considering a few key principles. One is to make data access a priority. Many legacy systems may struggle because they often have fixed architectures that limit scaling. Modern analytics systems not only store more data with compression and other storage optimization methods, but also are better suited to handle concurrency. Another principle is to prioritize analytical workloads, matching the most demanding workloads to the data platform system that are designed to handle these workloads. Modern analytics systems may leverage specifically designed hardware, such as GPUs, which can process data in parallel, thus processing more data at greater performance. For enterprises looking to streamline their analytics processes, it could also mean fewer data silos. Moreover, as enterprises consider moving to the cloud, these modern analytics systems are often designed to leverage cloud infrastructure using cloud-native technologies, which provide not only optimized scaling, but also reduce administrative burdens.
When modern analytics systems are implemented correctly, enterprises are well on their way to not only surviving, but also thriving in the data revolution.