Increasingly, data is the ammunition needed for success on the battlefield. Although information and intelligence assets have provided an advantage over adversaries since wars began, the velocity, variety and volume of data has increased so dramatically that the U.S. Army is looking for new and better ways to harness data and use it as an asset to accomplish key strategic missions.
Last week, I participated in a virtual panel session: “Data Innovation—Leveraging Venture Capital-Backed Companies,” at the AFCEA Virtual Signal Conference hosted by my friend Nick Sinai, current Senior Advisor at Insight Partners and former U.S. Deputy Chief Technology Officer for the Obama administration (I highly recommend listening to his interview on the DataMasters podcast). The topic at the heart of the panel discussion: new technologies make it easier and cheaper to collect, store, analyze, use and disseminate data—generally referred to now as DataOps. Getting DataOps right is vital to the U.S. Army and its future success. The same holds true for other government and commercial organizations.
But what exactly is DataOps and how is it successfully used? DataOps is modeled after DevOps, a set of practices that combines the Agile Development methodology and IT operations to shorten software development life cycles, increase feedback, and ultimately deliver better products through this iteration. DataOps breaks the daunting data endeavor down into straightforward, manageable steps that produce real results, quickly.
The purpose of DevOps was to enable the large internet companies to continuously build, test and release software so that they could introduce new features quickly and compete effectively in the intensely competitive internet industry. The purpose of DataOps is to enable large organizations – both private and public – to continuously build, test and release data to increase analytic and decision-making velocity for people in these large organizations.
The reference to DevOps is intentional because traditional data management was characterized by waterfall methods: long projects and expensive, single-vendor infrastructures. Conversely, DataOps—like DevOps—requires an agile approach and a next-generation infrastructure from what are today relatively new companies that provide solutions for everything from data cataloging, movement and automation to data mastering and quality. At Tamr we work with the largest organizations in the world to construct modern data ecosystems using world class best-of-breed components to continuously build, test and release high quality, curated data to a broad number of consumption endpoints (both people and machines) based on well-defined information access policies. It boils down to delivering mission value from the data. This is achievable through DataOps.
The first leg of the DataOps stool is people.
