Future-proof Your Company’s Ai Strategy: How A Strong Data Foundation Can Set You Up For Sustainable Innovation

The accelerated pace of innovation has given business leaders whiplash the past few years, and it’s been challenging to keep up with the flurry of new capabilities coming into the market. Just when companies think they’re ahead of the game, a new announcement threatens to splinter attention and derail progress. That has caused the C-Suite to think more long term with their digital strategies, and bolster their capacity for sustainable innovation.

The concept of sustainable innovation is different from sustainability itself (which often deals with climate impact), and is instead a recognition that emerging technology requires the right ecosystem to thrive. In other words, digital transformation isn’t just about acquiring technology available now, it's also about establishing a strong data foundation to be in position to acquire whatever technology comes next. That foundation is the root of innovation itself, and it allows companies to build an analytics model on top (with AI baked-in) to give insights that drive change. This sort of environment is often the genesis for the well-worn principle of “Fail Fast. Learn Fast.” because it gives space for teams to experiment and test new ideas.

As the hype around AI and GenAI turns from experimentation to execution, companies are future-proofing their investments by creating a robust, well-architected data layer that is accessible, organized, and structured to withstand the test of time.

Addressing the Data Gap

While the sexier customer-facing tech tends to grab all the headlines, it’s the data analytics behind the scenes that is the real workhorse of AI/GenAI. Most leaders understand this by now, but AI programs and data gathering efforts can still run parallel to each other, wherein data is massed in one location before it’s fed into AI programs. Instead of looking at your data program and AI/GenAI processes as two separate initiatives, the two efforts must be linked to ensure data is arranged properly and ready to be consumed. Meaning, while there may be vast amounts of data available, leaders need to consider how much of it is readily usable for driving their AI projects. The reality is, not much. In a way, organizations are duplicating efforts by keeping data and AI apart, and aligning them closer together can be a key differentiator in terms of improving efficiency, reducing costs, and streamlining operations.

According to BCG, companies that have invested the time in merging their data and AI programs from the beginning have experienced outsized growth compared to their peers. After all, companies can’t have AI development without fixing data first, and leaders are pulling away from the pack by using their more matured capabilities to better ideate, prioritize, and ensure adoption of more differentiating and transformational uses of data and AI. As a result, companies that have linked data to AI development have four times more use cases scaled and adopted across their business than laggards in data and AI, and for each use case they implement, the average financial impact is five times greater.

To Strenghten Your Data Foundation, Start By Asking a Few Key Questions

Remember, the ability to lift and shift data (whether on-site or via cloud migration) is not the same as making it AI-ready. To ensure that data is prepared to be consumed (i.e. able to be analyzed for AI-insights), companies need to first consider a few important questions:

• How does our data align to specific business outcomes? AI models need curated, relevant, and contextualized data to be effective. In the early stages, companies should switch their mindset from how data is acquired/stored, to how it will be used for AI-driven decision-making within specific functions. When companies architect specific use cases while storing and organizing their data, it can be more easily accessible when it comes time to develop new processes like AI, GenAI, or agentic AI.
• What roadblocks are in our way? When McKinsey surveyed 100 C-Suite leaders in industries across the world, almost 50% had difficulty understanding the risks generated by digital and analytics transformations – by far the top risk-management pain point. In a rush to start producing results, companies can often sacrifice strategy for speed. Instead, leaders need to carefully study all angles, think into the future, and try to mitigate any potential for risk.
• How can we optimize our data for increased efficiency? As the need for data intensifies, it’s common for managers to put on blinders and only focus on their own department. This type of siloed thinking leads to data redundancy and slower data-retrieval speeds, so companies need to prioritize cross-functional communications and collaboration from the beginning.

 4 Best Practices for Developing a Strong Data Foundation

Companies that invest in their data layer today are setting themselves up for long-term AI success in the future. Here are four best practices to help future-proof your data strategy:

1. Ensure Data Quality and Governance

• Establish data lineage, metadata management, and automated quality checks
• Leverage AI-powered data catalogs for better discoverability and classification
• Simplify data management to ensure seamless governance of structured and unstructured data, machine learning (ML) models, notebooks, dashboards, and files

A good example of a company that actively utilizes AI to ensure data quality and governance is SAP, which integrates ML capabilities within its data management suite to identify and rectify data inconsistencies, thereby improving overall data quality and upholding robust data governance practices across its platforms.

2. Strengthen Data Security, Privacy, and Compliance

• Implement Zero-Trust Security by encrypting data at rest and in transit
• Use AI-powered threat detection to identify anomalies and prevent breaches
• Ensure compliance with global regulations like GDPR and CCPA, and automate reporting/audits using AI

One company that is doing innovative things in the digital supply chain and third-party risk management is Black Kite. Black Kite’s intelligence platform quickly and cost-effectively provides intelligence into third parties and supply chains, prioritizing findings into a simplified dashboard that risk management teams can easily consume and close critical security gaps.

3. Explore Strategic Partnerships

• Evaluate your own advanced analytics capabilities and study how existing data performs
• Seek out partners that can integrate AI, data engineering, and analytics into one easily-managed platform

Some cloud-based partner solutions that can help structure data for AI success are: (a) Databricks, which integrates with existing tools and helps businesses build, scale, and govern data/AI (including GenAI and other ML models); and (b) Snowflake, which operates a platform that allows for data analysis and simultaneous access of data sets with minimal latency.

4. Foster a Data-Driven Culture

• Democratize data access by implementing self-service AI tools that use natural language querying (NLQ) to make data insights accessible
• Upskill employees in AI & data literacy, and train teams in AI, GenAI, and other data governance processes
• Encourage collaboration between data scientists, engineers, and business teams to facilitate data sharing and generate more holistic insights

A prime example of a company that actively fosters a data-driven culture heavily reliant on AI is Amazon, which uses customer data extensively to personalize product recommendations, optimize logistics, and make informed business decisions across their operations, making data a central pillar of their strategy.

Building a Data Foundation for the Future

According to a recent KPMG survey, 67% of business leaders expect AI to fundamentally transform their businesses within the next two years, and 85% feel like data quality will be the biggest bottleneck to progress. That means it’s time for a big re-think about data itself, focusing not just on storage, but on usability and efficiency. By getting their data foundations in order now, companies can future-proof their AI investments and position themselves for ongoing, sustainable innovation.

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