Full path: Home, Digital, AI, Insights, and Solutions, Data and Analytics Transformation
How we can help
As data explodes in quantity and complexity companies find it increasingly difficult to unlock its full potential. We take a comprehensive approach to your data challenges, helping you conquer obstacles and become an analytics leader.
Prioritize analytics projects based on value and feasibility, and execute through an iterative process built for speed and flexibility
Align your people and systems around clear roles, accountabilities and smart processes for product development, data stewardship and compliance
Standardize your data architecture, cloud computing and engineering processes, and deploy user-friendly tools to your team members
More likely to be among the top-quartile financial performers
As likely to execute decisions effectively
More likely to make decisions much faster
An AI initiative stalls without a solid data strategy foundation because AI can only be as good as the data feeding it, and many organizations sit on data that is fragmented, inconsistent, and ungoverned. No usable data means no usable AI.
The pattern is consistent: Pilots succeed because they’re built on offline, manually cleaned nonproduction data sets. But when companies try to scale them across the enterprise, underlying data issues resurface and stall progress.
This is a data foundation problem, not a modeling one. The fix is to treat data as a strategic asset rather than an IT afterthought, establish governance and shared definitions, and modernize the data architecture so high-quality data moves securely across the enterprise. Companies that build a strong data foundation turn data from a bottleneck into a durable engine of innovation and growth.
You know your data foundation is ready for AI when data works as a strategic asset—governed, clearly owned, and accessible across the enterprise—instead of sitting fragmented in siloed systems that no one quite owns. In our experience, strengthening data foundations measurably improves the ability to extract value from analytics. One North American utility, for example, mapped its data maturity across 12 dimensions, built a unified taxonomy, and ran pilots to document its key data assets and lineage.
Many organizations aren’t there yet. Fragmented data, unclear ownership, and inconsistent quality are legacy barriers that resurface when a pilot tries to scale across the enterprise. But data and technology leaders can use the shared principles of successful data transformations as a checklist: prioritization tied to value, data product model, ownership and accountability, enterprise alignment, investment that evolves with needs, governance that improves data quality, and data architecture. A robust data strategy isn’t a nice-to-have; it’s a core enabler of AI value realization.
Unstructured data is becoming central to data strategy because generative AI draws real value from documents, audio, images, and video. It works across both structured and unstructured data. That’s made data more valuable but also more complex to manage. The challenge is structural: Most organizations haven’t historically governed the vast store of unstructured content that AI now needs.
Turning that liability into an asset takes three moves:
By treating unstructured data as the strategic resource it is, companies can reuse one governed foundation across many AI use cases—improving compliance and unlocking the next wave of AI performance.
Data strategy connects to business value when it’s tied to enterprise priorities and grounded in clear, business-aligned ownership, not run solely as an IT project. In our experience, leading organizations treat data as a strategic asset, not an IT afterthought. They build a data strategy that charts a pragmatic, actionable roadmap to clearly defined business goals.
Standalone or bottom-up data efforts often stall because they were never coordinated under an enterprise strategy. What works instead is:
Then, as teams capture, curate, and conform data, they steer investment to where it matters most as needs evolve. Run this way, data strategy delivers measurable results and opens new sources of value rather than stalling standalone initiatives that never scale.
The right sequence for building data and AI capabilities is to establish the data foundation before scaling AI. AI inherits whatever is weak in the data beneath it. The old rule of “garbage in, garbage out” is still a feature of AI as much as any other digital solution. Pairing technology programs with process redesign—modernizing the data estate while improving how work is done—is where the value gets captured.
The common mistake is sequencing it backward: launching AI use cases first, then finding the data can’t support them. The data foundation—unified, governed data and a forward-looking architecture—has to be in place before AI can scale on top of it. As companies build toward cohesive agentic platforms, they should start with the infrastructure everything else depends on, including data governance and quality frameworks. Companies that get the data foundation right will move pilots past the pilot stage, turning high-quality data into a lasting source of competitive advantage.
