Coaching, Planning, and Engineering Model Management and Deployment Solutions.
Model Management is a new category of technologies and processes that help organizations consistently and safely develop, validate, deliver, and monitor models that create a competitive advantage. Previously, model management referred just to monitoring production models, but we believe it should encompass a much broader capability.
Models involve code and data, but they are fundamentally different from software or data. The model myth is the misconception that because models involve code and data, you can treat them the same way you treat software or data. This myth holds us back from unlocking the full potential of models.
Models use different materials.
Models are built differently.
Models behave differently.
Our extensive experience allows us to be a coach, a planner or provide engineering support to improve your model management and deployment capabilities.
See our viewpoint on Model Management.
Roadmaps and designs for new data science capabilities, analytics technologies, and platform architectures.
All investment and architecture decisions for data capabilities and technologies should directly serve business objectives. Advisory services define those relationships and the corresponding outcomes across the spectrum of business, data, and technology infrastructure domains. No matter where on that spectrum you’re starting, we clearly articulate and justify recommendations, backed by in-depth assessments, industry expertise, vendor relationships, and empirical benchmarking—culminating in clear, prioritized roadmaps that are adaptable to changing circumstances.
Transforming a business requires cultural commitment and the ability to organize your efforts around data. Successfully executing a transformation requires the right strategy, methods, and application of technology. Our approach to Data Strategy is a process that starts with your strategic business objectives and analyzes them with your workflows, data requirements, organizational maturity, and technical capabilities. The results are roadmaps for your organization, focused on achieving near-term business benefits and creating a platform for future innovation.
Data architecture is a business-critical activity. Our extensive experience allows us to assess, recommend, and validate architectures that deliver both near-term value and a platform for continuing development. We work closely with your business and technical teams to plan a platform for your data, interfaces, and applications, while expanding your capabilities to support new products, services, and key business objectives. Plus, we’re vendor-agnostic, meaning your needs are always the first consideration in any decision.
Prototyping, building, and deploying data pipelines, reliable platforms, and applications
Meeting the rapidly growing demands to process, persist, and analyze data requires scalable, extensible production data platforms and analytics technologies. Our bias is toward “building” and following key engineering principles: (i) Software and infrastructure needs to support business objectives, (ii) Architect for adaptability as tomorrow’s technology landscape and vendors will be different from today’s, (iii) Automation of integration and deployment amplifies speed to value and (iv) our agile approach to data engineering means we prioritize the most valuable activities first.
Clients that benefit from our Data Engineering services often have one or more of the following needs:
Your current data ingestion and processing pipeline is rigid, making it cost-prohibitive to bring in additional data
Your data scientists and analysts are clamoring for more and quicker access to data sources to be able to drive business outcome
Initial efforts for innovative data use have been successful but you’re running into problems migrating to production scale
Having tons of data that can inform emerging analytical techniques, such as machine learning and AI, is only the start of the answer. Deploying the right infrastructure and workloads to ensure the right data storage and processing environments can be discovered and used with assurance, confidence, and consistency will help your organization increase velocity of what business outcomes it can achieve.
Extracting actionable insights from your data through exploration, modelLing, and artificial intelligence
Extracting actionable insights from your data through exploration, modeling, and artificial intelligence. Data science activities are organized around investigative themes, each of which aims to test a set of hypotheses or identify empirical patterns within a particular subject area. Our process starts with narrowing down a set of investigations from the desired objectives into a set of prioritized, workable, and testable hypotheses and goals we can iteratively prove or disprove using our agile methodology. Ensuring a business understanding is necessary to clearly establish the problem definition and problem scope which will drive the initial set of activities.
Companies that benefit most from our Data Science expertise:
Are searching for actionable insight in complicated datasets across domains
Want to build valuable data products based on advanced analytical models
Believe that business outcomes from data science model development (e.g., machine learning, natural language processing, advanced statistics) will move more quickly with a seasoned team
Embracing an agile approach, our teams are able to adapt quickly and stay focused on producing real-world application and value for our clients. Our agile data science projects delivers tangible results while providing our clients transparency of progress and directional control over the analyses.