Deliver an enterprise vision for data through data architecture, data management and analytics solutions. Our data strategy is supported by practical roadmaps with realistic objectives.
Clear-sighted understanding of developing trends, methods and solution capabilities.
Establish vision, determine constraints, and enablers: Where are we right now? Where do we want to be? How do we get there?
Modern data solutions for real-time speed and scale.
Ensure enterprise data is appropriately managed and controlled, establishing basis for reliable and good quality insights.
Optimizing information flow through agile data pipelines.
Create and maintain the analytics infrastructure enabling pipelines to transform raw or unstructured data use for analysis in large-scale processing systems.
Disciplined approach in data engineering management ensures data is legal, well-organized, safe, accessible, and trustworthy.
We help clients extract reliable insights, and streamline & automate decision-making by ensuring data is appropriately identified, managed and controlled.
The key to having data that can be trusted lies in empowering its owners and users with clear responsibilities such as training, standards & practices, and tooling.
Define data quality requirements for critical data elements and implement effective processes to measure, monitor, and report issues that impact accuracy, completeness, and integrity of data.
Create complete glossaries of data, including lineage from record to report, provide users means to easily access and understand business data.
We extract business insights from complex data, and by representing information visually (e.g. dashboards) we make it easier to understand trends, patterns, and identify anomalies.
Through accurate data visualization, the reporting processes can be elevated to new levels.
Effective and fast insights from data using enhanced visualizations.
Expertise with best-in-class industry tools.
Analyze and document descriptive, predictive, and prescriptive models that can generalize beyond already observed examples.
Initial investigation into available data to understand what analytics are possible.
Combine domain knowledge, mathematical skills, and expert architectural & programming skills to extract robust, repeatable, and meaningful insights from data.
Applying different modeling approaches to determine best fit model(s).
Becoming a data-driven organization means embracing automated decision-making as an integral part of everyday operations.
Bridging gaps between solution architecture and technical implementation in code.
Maintaining AI models is a challenge, requiring constant monitoring, understanding of limitations and changes to the world. We take a holistic, iterative approach to model maintenance.
The most common types of decision-making systems per application are:
Assess whether the AI solution delivers against expected business goals, and its behavior while being attacked.
Improve business efficiency by deploying Artificial Intelligence (AI), Business Process Management (BPM), and Robotic Process Automation (RPA) in the right proportions, and in the right places.
Solutions/Bots specialized in interacting with humans and computers alike with deep ties to specialized systems downstream and can help reduce friction between someone looking for a piece of information and them finding it. Discover patterns and recommend improvements to processes across large bodies of humans, and machine communications.
Discover patterns and recommend improvements to processes across large bodies of humans, and machine communications.
Reduce time to market, turn-around time and business inefficiencies by automating decision making.