High-Performance DataOps for Government

AI / ML • Data Optimization  |  July 10, 2023

What is High-Performance DataOps? Why is it important for government agencies to consider this as part of their entire data management strategy? 

High-Performance DataOps (Data Operations) refers to the practices and principles used to manage and optimize data operations for high-performance computing and data-intensive applications. DataOps focuses on improving the speed, quality, and efficiency of data-related processes within an organization.

In high-performance computing, DataOps involves implementing strategies to handle and process large volumes of data at high speeds. This includes designing and implementing efficient data pipelines, optimizing data storage and retrieval, and leveraging parallel processing and distributed computing techniques to maximize performance.

Some critical components of High-Performance DataOps include:

  • Data Integration: Efficiently integrating data from various sources, such as databases, data lakes, streaming platforms, or external APIs, into a unified and consistent format.
  • Data Quality: Ensuring the accuracy, consistency, and reliability of data through data cleansing, validation, and error-handling mechanisms.
  • Data Processing: Utilizing parallel processing, distributed computing frameworks, and optimized algorithms to process large volumes of data quickly and efficiently.
  • Data Governance: Implementing policies and procedures to manage data assets, including security, privacy, compliance, and regulatory requirements.
  • Monitoring and Performance Optimization: Continuously monitoring data operations, identifying bottlenecks, and optimizing the infrastructure, hardware, and software stack to achieve maximum performance.
  • Automation and DevOps: Automating data-related processes, using version control, and applying DevOps practices to ensure faster deployment, scalability, and reproducibility of data operations.

High-Performance DataOps is particularly relevant in industries dealing with massive datasets, such as government, finance, healthcare, e-commerce, telecommunications, and scientific research. By implementing DataOps principles and leveraging advanced technologies, organizations can accelerate data-driven insights, improve decision-making processes, and gain a competitive edge in their respective domains.

The DataOps Opportunity for Federal Government 

The U.S. Federal Government is increasingly adopting High-Performance DataOps practices to improve its data management, analytics, and decision-making capabilities. Some examples of how the government is utilizing DataOps principles include:

  • Data Integration and Consolidation: The government is consolidating and integrating vast amounts of data from various agencies and departments to gain a holistic view and enable more effective decision-making. DataOps practices streamline the data integration, ensuring data quality and consistency across different sources.
  • Real-time Data Processing: With the growing importance of real-time data analysis, the government is leveraging DataOps methodologies to process and analyze large volumes of data in real time. This is particularly relevant for national security, emergency response, and law enforcement agencies, where timely data insights are crucial.
  • Data Governance and Security: DataOps is critical in ensuring data governance, security, and privacy within the federal government. Agencies are implementing DataOps practices to establish robust data governance frameworks, enforce data security protocols, and comply with regulations such as the Federal Information Security Management Act (FISMA) and the Privacy Act.
  • Cloud Adoption and Scalability: The federal government is increasingly adopting cloud computing to store, process, and analyze data. DataOps principles are applied to optimize data pipelines, automate workflows, and leverage scalable cloud infrastructures, enabling efficient data processing and analysis at scale.
  • Analytics and Decision Support: DataOps methodologies streamline data analytics workflows within the federal government. By automating data pipelines, implementing efficient data processing techniques, and utilizing advanced analytics tools, agencies can generate actionable insights faster, supporting evidence-based decision-making.
  • Open Data Initiatives: The federal government has various open data initiatives to make government data accessible to the public. DataOps practices ensure available data quality, consistency, and availability, enabling citizens, researchers, and businesses to leverage government data for innovation and transparency.

In speaking with agencies across DoD and the federal government, several agencies have started on their DataOps journey. The specific implementation and adoption of DataOps practices can vary across agencies and departments within the U.S. Federal Government. However, the overall goal is to improve data management, analytics, and operational efficiency while ensuring data security, privacy, and compliance.

Although agencies embark on their own DataOps journey to accelerate data-driven insights and improve decision-making processes, many programs consider their data sources as individual assets. Instead, DataOps is so much bigger than individual data sources; it is about having a strategy to realize strategic outcomes only when you can get a holistic view of all data from any source. It is a commitment for a program or agency to be indeed “data-driven” and exemplify a data culture.

In order for organizations to be truly data-driven, DataOps must be ingrained into everyday practice and execution. It can’t be an afterthought or a bolt-on but a constant process. The push towards a data-driven culture is analogous to the early days of software development, where people, processes, and technology converged to accelerate software and application development that we take for granted today since it is a part of our everyday execution. Government agencies are in the early stages of committing to that process to be data-driven truly, and a DataOps strategy and process will be a crucial component of driving success.

Fortunately, factors are driving increased adoption or commitment to a DataOps strategy. Specific legislative acts, such as the Digital Accountability and Transparency Act (DATA Act), have pushed efforts in the direction of DataOps to improve overall data quality and encourage agencies to be more data-aware and incorporate as part of their citizen services. These Acts have led to data portals such as the USASpending.gov where we can see what each agency (e.g. DoD, HHS, SSA, Treasury, etc.) spends on or via different breakdowns with respect to budget function (e.g. National Defense, Healthcare, Social Security, etc.) or Object Class (e.g. Grants, Contracts, Personnel, etc.). Other related websites include data.gov, code.gov, search.gov and more.

A second act, the Foundation for Evidence-based Policy making of 2018, is forcing policymakers to think and act more data-driven to formulate policies rather than going on a gut feeling. These examples of legislation have modified how agencies and policymakers use data to make critical decisions. As a result, agencies desire to put systematic processes in place to ensure access to more real-time data; instead of relying on annual reports, agencies need access to data on a monthly or even daily basis to drive effective decision-making.

 Riding the AI / ML Trend

The hype around AI and ML has piqued much interest across government agencies to grasp how to leverage this data type. Those agencies and programs that got a jumpstart on AI and ML initiatives are taking a step back. There has been a realization that these initiatives are only as good as the data used to train these models. Having a thoughtful DataOps strategy that encourages agencies to really understand their data and how they collect it at the right time has become a priority. Once agencies have the right quality data, they’ll have increased success with their AI and ML models to realize maximum value out of practices relying on this data type.

Don’t Settle for a Tool; Go with a Framework

It is important to note that DataOps is not a single technology. We are noticing a lot of individual tools being used to tackle the DataOps challenge. Still, these tools are designed to fix specific things, only offering partial insights and limiting overall outcomes. If you can’t extract or get the complete picture from any data source at any time and access is limited to a small group of people, you aren’t solving the problem. However, technological architecture and tooling play an essential role in achieving more agile and automated approaches to data management. In the DataOps journey, there is no absolute finish line, but there is a maturity curve. As agencies look to refine and improve their DataOps approaches, they must consider how well ongoing data management practices and technology support their downstream data-driven outcomes. If data management is not actively accelerating outcomes, then practices must be adjusted. An organization’s ability to recognize its maturity in DataOps is essential for continuous improvement.

Committing to a DataOps Strategy Drives Significant Outcomes

How do you ingest and extract data at scale so you can be reassured you are genuinely getting at all possible data to help tell that complete story so that you can analyze and make big, impactful decisions?

It starts with committing to a DataOps strategy. There is an overall federal data strategy that agencies can leverage to get started. Enterprise AI and ML practices are also maturing. These efforts seek to deploy models directly into production where they can deliver value for government organizations. But with the operationalization of AI and ML, underpinning data management practices must also be operationalized to provide trusted and relevant data for models. With agility as an organizational focus, these data management efforts can no longer be ad hoc. Steps need to be systematic, ongoing, and iterative. A well-designed DataOps program, with appropriate supporting technology, can do this.

And those agencies that commit to a DataOps strategy and approach can reap some significant benefits:

  • Analytic Innovation: Increased ability to derive value from data.
  • Security: All data is protected and safe so risks are mitigated.
  • Resources and expertise: Time to value is accelerated by minimizing the need for specialist resources and expertise.
  • Governance and compliance: requirements are more easily achieved, reducing risks and making data inherently more usable.
  • Achieve greater heights: in analytic maturity and supporting more advance analytic capabilities
  • Data democratization across the organization:  Provide self-service for business users and citizen data scientists with appropriate governance.
  • Accurate, easily deployable AI applications:  AI/ML more accessible to end users and easily deployable from core to edge

Hitachi Vantara Federal helps government agencies accelerate their High Performance DataOps journey. We deliver intelligent data management across your hybrid, multi-cloud, core, and edge for professionals to experience data and build innovative data products managing all data from capture to publish. Learn more about our DataOps solution portfolio here.