Data-driven transformation is a mandate for today’s CIOs and CDOs, and it’s likely to remain so for a long time. Modern data architectures, together with new services powered by AI and advanced data science techniques, are making it possible to fully use analytics throughout the organization and drive data literacy.
Although new infrastructure – such as cloud data warehouses and lakes – and new technologies such as data streaming enable businesses to significantly increase data availability, that’s not the only piece of the puzzle. You still have to address the people component - and the processes to make data always available and analytics-ready.
Your processes have to change – to deliver greater agility, faster time to value, and real-time data for users in every area of your organization. Fortunately, DataOps is here to help.
Gartner defines DataOps as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data management and consumers across an organization.” 1
It’s a methodology that encompasses the adoption of modern integration technologies, the processes that transform data from a raw to ready state, and the teams that work with data. The goal is to bring both sides of the data-delivery equation into alignment. The data manager’s requirements for control, transparency, and auditability. And the business user’s need for real-time, analytics-ready data – to ultimately extract the most value for the enterprise.
Data-driven transformation requires an agile approach across the entire data supply chain, from your infrastructure to your processes and people. DataOps helps you bring all those elements together, accelerating cycle times and improving performance with the potential to transform your organization in a number of ways:
Data literacy is quickly becoming a strategic initiative for CIOs, CDOs, and other C-level executives. Half the battle is closing the skills gap. The other half is making trusted data as easy as possible to access, use, and mine for insights – for the entire spectrum of users. Modern data integration and management can rapidly accelerate this process, centralizing control while democratizing access.
To become truly data-driven, agility and real-time insights are key. DataOps allows you to move data as it’s changed, in real time. Automating manual tasks reduces analytics cycle time, freeing resources for higher-level focus. And flexible integration solutions let IT change a source or target without disrupting the infrastructure – so you can stay agile as technology evolves.
DataOps lets you make vetted, governed data universally accessible. Instead of limiting analytical insights to data scientists, you can extend them to a broad set of line-of-business users with focused expertise. And this includes users at the front lines and edges of the business – via mobile devices, IoT, and at any point of customer interaction – helping optimize operations and customer experiences.
Smart data catalogs, data indexes, and other tools enable IT to design a modern governance process with the access controls needed to avoid data-decision variability and chaos. And IT can achieve scale and agility by leaving data in lakes, warehouses, and other repositories on-premise and in the cloud. This gives users timely access to enterprise-ready data while layering in quality assurance with role and responsibility designations that bring more of the right data to the right people at the right time.
DataOps makes it faster and easier for data scientists and business analysts to join forces – and for discrete business units to collaborate around the analysis of data and sharing of results. In fact, DataOps is a great vehicle for creating the long-sought-after business/IT alignment that tends to be elusive as companies grow. And unlike traditional task forces that tackle niche issues, DataOps affects the entire organization by delivering valuable data to every business user when they need it, in a consumable and governed way.
1. Gartner, Innovation Insight for DataOps, Nick Heudecker, Ted Friedman, Alan Dayley, 27 December 2018
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