Helping our clients capture, process, store and exploit the value of data in support of strategic and tactical decision making and stronger mission achievement.


In today’s rapidly evolving technology landscape, an organization’s data has never been a more important aspect in achieving mission and business goals. Our data exploitation experts work with our clients to support their mission and business goals by creating and executing a comprehensive data strategy using the best technology and techniques given the challenge.

At Steampunk, our goal is to build and execute a data strategy for your organization to coordinate your data collection and generation, to align the organization and its data assets in support of the mission, and ultimately to realize mission goals with the strongest effectiveness possible.

Data Strategy

Most organizations have created and have well-defined strategies for areas such as software engineering, IT operations, business process management and mission-focused processes. All-too-often, however, the mass of data at organizations is an afterthought. Program teams define the types of data they need to collect for their particular program need. The varying shapes and sizes of data being collected by an organization are typically collected in some store with a promise of clean-up at some later date that never arrives. Data is copied and propagated across the organization constantly, and very little thought is given to interoperability or reuse. Commonly, an organization’s data strategy would be self-described as chaotic.

At steampunk, we believe in developing a data strategy that ensures all data resources are managed in such a way that they can easily and efficiently be used as an asset by the organization. Data is a critical asset of the organization and is core to the decision making and execution of the organization’s mission. Not only does the strategy include the technology blueprint for storage, processing, security, utilization and sharing, but it also includes the methods, practices and policies that govern the lifecycle of data at the organization.

Data Architecture

Data architecture is the overarching strategy for the collection, storage, and use of data for the organization. The tools, systems, and the manner in which data will be managed are all critical to the data architecture.

With all the options around data storage – relational databases, NoSQL databases, graphs, key-value stores, etc.; and all the options for data processing – traditional ETL, Spark, Hadoop; the means of data movement and sharing – message bus, Kafka, microservices, APIs. There are many options to consider and the decisions need to be based on your goals as an organization, the types of data being managed, the way that data will be used and the people at are using it.

Steampunk has the depth of skills, experience, and approach to help organizations build a comprehensive data architecture. Whether you’re just getting started with cleaning your data, rearchitecting storage for better utilization, or overhauling your enterprise to gain the efficiencies and strategic advantage your data will yield, we’re ready to build and deliver your architecture.

Machine Learning & Artificial Intelligence

Machine learning and artificial intelligence are the latest emerging technology to hit the world of data exploitation. For years, artificial intelligence and the value that machine learning can produce from raw data was a conversation about science fiction. Movies and novels were really the only place these technologies were being discussed, outside of labs as the most prestigious universities and think tanks. Today, however, these capabilities are becoming mainstream, and organizations that aren’t effectively using them are missing out on the tremendous amount of value they can yield.

Our data scientists use machine learning (ML) to design and apply algorithms that are able to learn how to process data based on past use cases. We also use various artificial intelligence (AI) techniques to create intelligent machines that understand and mimic intelligent behavior. Using these techniques, our teams better understand and operationalize data for analysis, prediction, problem-solving and analytical reasoning.


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