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.
DataOps in the federal government
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 three essential components of DataOps
The first leg of the DataOps stool is people. The behaviors of people in large organizations with respect to data is the most important component of great DataOps – it’s essential for people in an organization to view data as an asset of the organization and work hard to avoid data behavior pitfalls – some of the most dysfunctional of which include:
- “Data quality denial”
- “Data entropy”
The second leg of the DataOps stool is process. Like DevOps, DataOps uses the Agile methodology to create valuable new analytics for the business or organization. Agile accepts that people don’t necessarily know what they want until they see it. The data-analytics team delivers new analytics in a short time frame and receives immediate feedback from users. This tight feedback loop steers the development of new analytics to the features that are most valuable to the agency.
The third consideration for DataOps is technology. Agencies need to turn to both open source and commercial components that can address the complexity of the modern data supply chain. These components will need to be integrated into end-to-end solutions, but fortunately many of the newest technical components have been built to support the interoperability necessary to make the tech from multiple vendors work together. Again, similar to DevOps.
In terms of the infrastructure platform to support an agile data supply chain, there are great compute, storage options. For flexibility, scalability and speed of implementation, cloud-based options should be the first choice. Cloud infrastructure providers have evolved to provide adequate security and core infrastructure.
The power of democratizing analytics
Establishing DataOps methodologies is critical for large public and private organizations to move forward in leveraging the analytic infrastructure they have built out over the past decade-plus. Great analytics always starts with great data – the classic “Garbage In, Garbage Out” mantra applies. Success in providing high quality, well curated and up-to-date data starts by embracing DataOps and thoughtfully combining the processes, people and technology that fits your organization’s needs. A holistic approach to DataOps, holds tremendous promise for large organizations such as the U.S. Army looking to manage their data as an asset and stay ahead of our adversaries through the application of data to their missions.
What large organizations are doing today in managing data as a strategic asset is something that we’ve been working toward for decades. It’s vitally important for us to enable the U.S. Army and other federal agencies to do the same. Our goal at Tamr is to equip soldiers and Army civilian workforces with modern DataOps tools, processes and people that they can use to analyze data to make sound decisions and further their missions. Our goal is to put the mission work on the forefront and the procurement work in the back.
Ultimately, we want to democratize analytics and the high quality data required to fuel those analytics and let people on the ground demonstrate the art of the possible. When it comes to data and analytics, quick wins and even quicker iterations with data analytics on and off the battlefield are the way forward.