Data

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Overview

Enable organizations to establish and operate a highly functional data engineering, analysis, and science program by equipping them with tools and processes.

The data practice is centered on 4 pillars:

Program foundations:

Deliver a data program strategy and roadmap to support organizations as they start their data journey

Platform adoption:

Help clients operationalize leading practices in their data platforms and tools; while enabling them to uncover the data they have and automate the process of its consolidation

Analytics activation:

Help customers discover and interpret trends in their data and report those insights in a scalable and repeatable manner

AI enablement:

Help clients extract value from data via the scoping, proof of concept & testing, and operationalization of use-cases, using industry leading Artificial Intelligence techniques

PLATFORM ADOPTION
help clients operationalize leading practices in their data platforms and tools; while enabling them to uncover the data they have and automate the process of its consolidation
AI ENABLEMENT
help clients extract value from data via the scoping, proof of concept & testing, and operationalization of use-cases, using industry leading Artificial Intelligence techniques
PROGRAM FOUNDATIONS
deliver a data program strategy and roadmap to support organizations as they start their data journey
ANALYTICS ACTIVATION
help customers discover and interpret trends in their data and report those insights in a scalable and repeatable manner

Key Offerings

Description

  • Conduct information gathering session to understand the current-state data program and platforms
  • Collaborate with key stakeholders (i.e., Data Engineering, Data Science, select B.U.s) on target state design

Outcomes

  • Current state view
  • Target state design
  • Vision and strategy
  • Lean and agile operating model
  • Technology, process, and people architecture
  • Governance model

Description

  • Gather platform requirements (i.e., functional, security, and governance) and develop platform design & architecture
  • Implement platform and operationalize key supporting processes

Outcomes

  • Federated platform governance enablement
  • Operational data platform landing zone, including dev and prod environments for data scientist to design and operationalize use-cases
  • Data-as-a-product offering including self-service

Description

  • Identify and plan migration waves for existing data pipelines
  • Re-architect or refactor as needed
  • Perform migration waves
  • Leverage automation after initial waves

Outcomes

  • Accelerated workload migration to the data platform
  • Further adoption of the data platform

Description

  • Work with you to identify value-added use-cases to reduce costs and generate new revenue streams
  • Define and scope Proof of Concept on the data platform

Outcomes

  • Functional PoC of value adding use-case, powered by your data and relevant AI & ML tools

Description

  • Scale use-case PoC to production
  • Configure DevOps / MLOps, monitoring, and fault tolerance of use-case

Outcomes

  • Production-ready workloads hosted in the data platform
  • Value generation realized
  • Identification of additional use-cases

Key Data Partners

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Our Approach.

Our framework combines best-in-class data engineers, data scientists, and DataOps practitioners to deliver sustainable and practical change. We collaborate to introduce innovation into your organization using our proven methodology, ensuring that your business can realize its full potential.