Digital Initiatives
A digital strategy begins with a set of digital initiatives. We compile a list of digital initiatives. Then, we help you prioritize the most impactful ones.
Digital Strategy Offerings
Cloud-Native Data & AI
Cloud-native data & AI leverages automated, compliant, and secure frameworks to support the deployment of robust AI architectures, accelerate digital product launches, and manage cloud migrations—ensuring enterprise data integrity, scalability, and innovation.
Our Digital Strategy Offerings
Cloud-Native Data & AI at a Glance
Three approaches
Enterprise AI Architecture
Establish an enterprise-wide AI framework to enable compliant, secure, and scalable deployment of artificial intelligence solutions.
Digital & Data Product Launch
Rapidly deliver a new digital product and data service using agile methods and cloud-native platforms to drive innovation.
Cloud Migration & Repatriation
Transition workloads to or from cloud environments to optimize cost, performance, and regulatory compliance.
How Do You Manage Data?
The data lifecycle consists of five stages.
Data Collection
How do we collect data from various sources? Does it need to be in real-time or can there be a delay?
Data Storage
Where do we store our data? How do we store it? How long do we keep it?
Data Processing
How do we prepare and manipulate our data to produce meaningful information?
Data Analysis
How do we explore and interpret our data to gather meaningful insights?
Data Visualization
How do we communicate our data-driven insights?
Future
The Future Of Digital Strategy
We work with your desired end state in mind. By applying proven frameworks to understand your pain points, we're able to build a digital strategy tailored for you. We then help you align the people and processes within your organization to operationalize your digital strategy.
Digital Approach
The correct approach to executing digital initiatives involves a combination of people, process, and technology. We help tailor your approach for each initiative.
Digital Outcomes
Every digital initiative needs a measurable outcome. Defining KPI's and measuring outcomes helps to ensure that digital investments will pay off.
What to Expect
Common Deliverables For Enterprise Architecture
Every strategy project is different. However, we've found that cloud-native data & AI usually requires the following deliverables.
Reference architecture
Clear target design showing how systems, data, and AI fit together so teams move faster with fewer mistakes.
Landing zone deployment blueprint
Ready-to-use cloud setup with accounts, access, networks, logging, and shared tools, created in a repeatable, automated way
Enterprise governance guardrails
Simple rules and checks that enforce security, privacy, cost limits, and AI safety, with alerts and a way to handle exceptions
Golden path project template
Prebuilt project templates that show the easiest, proven way to migrate apps, launch models, or ship an MVP, so teams don’t start from scratch
Migration execution plan
Step-by-step schedule that groups work into waves, shows dependencies, and defines cutover and rollback steps to reduce risk.
In Detail
Ten Steps to Tech Stack Optimization
This 10-step playbook gives a clear path to optimize your tech stack: align goals, set guardrails, prepare the platform, automate delivery, validate security/performance/cost, and continuously improve. It provides reusable patterns to reduce risk and speed value. Because cloud-native data and AI covers many scenarios, use this as a starting point and tailor each step to your goals, regulations, and current state.
- 1 Define outcomes, risk, and compliance upfront
- Clarify business goals, KPIs/OKRs, time-to-value, and total cost targets; map regulatory, privacy, data residency, and AI risk requirements to ensure the solution is compliant by design.
- 2 Discover and baseline the current state
- Inventory apps, data, models, pipelines, infra, licenses, and costs; document dependencies, SLAs, security posture, and tech debt to create an objective baseline for decisions.
- 3 Segment and prioritize the scope
- Classify workloads/products by value, complexity, risk, and dependency; build a heatmap and wave plan that sequences quick wins and de-risks critical paths.
- 4 Design target reference architecture and guardrails
- Define cloud-native reference patterns (networking, IAM, encryption, secrets, SDLC/MLOps, data mesh/lakehouse) and policy guardrails to standardize and accelerate delivery.
- 5 Prepare the platform and landing zone
- Stand up or validate landing zones (multi-account/project structure, identity federation, network segmentation, logging, keys), and enable shared services: IaC, CI/CD, registries, observability, FinOps, MLOps.
- 6 Establish data and model governance foundations
- Implement cataloging, lineage, quality rules, data contracts, tiering, access controls, PII handling, feature stores, and model registries to ensure trusted, reusable data/AI assets.
- 7 Automate delivery with golden paths
- Create reusable templates and modules (IaC, pipelines, policies-as-code) plus opinionated “golden paths” for rehost/replatform/refactor, model training/serving, and product MVPs to reduce cycle time and variance.
- 8 Execute in waves using standardized runbooks
- Run migrations, launches, or AI deployments in iterative waves with pattern-specific runbooks, cutover plans, rollbacks, and change management to minimize risk and downtime.
- 9 Validate non-functionals and compliance gates
- Prove security, privacy, performance, resiliency (RTO/RPO), cost targets (FinOps), software supply chain integrity, and for AI: fairness, robustness, and monitoring; capture evidence for audits.
- 10 Operate and continuously optimize
- Transition to SRE/AIOps, define SLOs/SLAs and error budgets, manage drift and patching, tune cost/performance, iterate model/data quality, and feed learnings into a backlog for ongoing improvements.
