As data takes a more and more central role in enterprise operations, technology leaders are confronting a whole new series of challenges. And they’re doing so to keep abreast of a truly revolutionary pace of change in enterprise data management.
Until recent years, the central challenges surrounding enterprise data usage surrounded the functional logistics of data collection, storage, and analysis. The proliferation of cost-effective collection mechanisms across media types (from video surveillance to app-based customer service), coupled with the advent of enterprise-ready machine learning solutions and highly scalable storage-compute solutions, has finally resulted in an environment where enterprises can transcend a logistical perspective on data.
With sound, cost-effective data storage, collection, and analysis mechanisms in place, businesses confront the broader challenge of integrating these data capabilities with broader operations to drive tangible value.
This strategic shift in turn changes the focus from data architecture that collects and stores to data architecture that connects to facilitate real-time interaction with data-driven services for both employees and customers.
Toward an Ops-Centered Model for Data Management
To move toward a data management model centered on connection, technology leaders need to conceive of data management as more than infrastructure, as a genuine operational unit than links vital components of the enterprise.
Enterprises in many industries have tended to view data as an “accessory” that pipes in additional insight to other business units. But data-savvy enterprises need to evolve toward weaving the opportunities created by data into the actual operational fabric of the company. That means extending DevOps principles to support data-centric applications, ensuring data-rich features that can adapt at the speed of business. Concurrently, automation of routine data cleaning and processing tasks is essential to facilitate real-time data pipelines and the transformative services they can support.
An ops-centered model for data management transcends current concepts of “database administration” to arrive at a broader organizational role linking quality, security, development, and ops teams.
Real-Time is More Than a Customer Convenience
The ability to create data services that can support customers and internal users in real time is more than a simple convenience. Operations occur, unsurprisingly, in real time, and building a data operation capable of keeping pace crosses a key bridge toward an organization that truly leverages technology to change the way it does business.
While data storage and offline analytics remain a core component of any data strategy, real-time data infrastructure needs to encompass far more. Only by integrating diverse data sources like mobile-app, IoT, and live customer interactions in real-time can the full benefits if a data-powered organization be achieved.
Building Blocks of a Future-Facing Data Operation
Whatever the benefits of integrating data with every aspect of operations, that holy grail is still some distance away for most organizations. The foundations for these efforts, however, are already exceedingly practical.
While a true Data-centered ops model remains a focus of innovation at even the most ambitious tech companies, the provision of database platforms as a service has become commonplace. Data infrastructure delivered as a cloud or hybrid-cloud service enables unprecedented scalability and performance, allowing enterprises to begin fleshing out ambitious data-based tools without prohibitive upfront costs in terms of physical infrastructure.
The virtualization of data infrastructure is integral to the broader project of data integration with overall ops. This approach to data offers not only agility looking forward, but the power to leverage AI-based approaches that make data processing at a much larger scale practicable. Thus, the pursuit of the right data virtualization solution can save money and preserve strategic flexibility while paving the way for more ambitious data-driven innovations.