Over the past few years, accelerated pace of innovation has whipped business leaders and keeping up with a range of new capabilities to enter the market has been a challenge. Just as the company believes it is in the lead, the new announcement threatens distracting attention and derailment. This leads C-Suite to think longer-term through its digital strategies and enhance its sustainable innovation capabilities.
The concept of sustainable innovation is different from sustainability itself (often involving climate impacts), but rather recognizes that emerging technologies require the right ecosystem to thrive. In other words, digital transformation is not just about getting the technology available now, it also involves building a strong data base to get whatever technology is next. This foundation is the root of innovation itself, which allows companies to build analytical models at the top (with AI baking) to provide insights that drive change. This environment is often the origin of the principle of “failed learning quickly”. Because it provides the team with room to experiment and test new ideas.
As the hype around AI and Genai moves from experimentation to execution, companies realize future investments by creating a robust, good data layer that is accessible, organized and structured, accessible, organized and structured, to withstand the test of time.
Solve data gaps
While customer-facing technology with more sexy technologies tend to capture all headlines, behind-the-scenes data analytics is the real force of AI/Genai. Most leaders understand this now, but AI planning and data collection efforts can still be parallel to each other, where data is batched in an AI plan in one location. Both work must be linked to ensure that the data is arranged correctly and ready to be consumed, rather than treating your data program and AI/Genai process as two separate plans. Meaning, while there may be a lot of data available, leaders need to consider how much of it can be used to drive their AI projects. Reality is nothing. In a sense, organizations are replicating their work by keeping data and AI apart, and aligning them close to each other can be the key difference between improving efficiency, reducing costs and simplifying operations.
According to BCG, companies that have invested their time from the outset to consolidate data and AI programs have experienced huge growth compared to their peers. After all, companies cannot develop AI without fixing data first, and leaders can get leaders out of the way by using more mature features to better conceive, prioritize, and ensure more differentiated and transformative uses of data and AI. As a result, the use cases of companies that link data to AI development are scaled and adopted four times more than the lagging cases on data and AI, and for each use case they implement, the average financial impact is five times higher than the average use case.
To make your data basics, first ask some key questions
Remember that the ability to improve and shift data (whether on-site or through the cloud) is different from the ability to make it ready. To ensure that the data is prepared (i.e., AI-Insights can be analyzed), companies first need to consider some important issues:
- How do our data align with specific business outcomes? AI models require planning, relevant and contextual data to be effective. In the early stages, companies should convert their mindset from how to acquire/storage data to how to use AI-driven decisions in specific features. When companies build specific use cases when storing and organizing data, it can be easier to access when new processes such as AI, Genai, or Agentic AI are needed.
- What are the obstacles to our road? When McKinsey surveyed 100 C-suite leaders in industries around the world, it was hard for almost 50% to understand the risks arising from digital and analytics conversions—by far the highest risk management pain points. In a hurry to start producing results, companies can often sacrifice strategies for speed. Instead, leaders need to carefully study all angles, think about the future, and try to mitigate any risks.
- How do we optimize data for efficiency? As the demand for data increases, managers are often blinded and focus only on their own departments. This isolated thinking leads to data redundancy and slow data return speeds, so companies need to prioritize cross-functional communication and collaboration from the outset.
4 Best Practices for Developing a Strong Data Foundation
Companies investing in the data layer today are preparing for future long-term AI success. Here are four best practices that can help with your data strategy:
1. Ensure data quality and governance
- Establish data lineage, metadata management and automated quality inspection
- Leverage AI-driven data catalogs for better discovery and classification
- Simplify data management to ensure seamless governance of structured and unstructured data, machine learning (ML) models, notebooks, dashboards, and files
A great example of a company actively leveraging AI to ensure data quality and governance is SAP, which integrates ML capabilities into its data management suite to identify and correct data inconsistencies, improving overall data quality and maintaining strong data governance practices for its platform.
2. Strengthen data security, privacy and compliance
- Zero-value security by encrypting data in REST and TRANSIT
- Use AI-driven threat detection to identify exceptions and prevent corruption
- Ensure compliance with global regulations such as GDPR and CCPA and use AI to automatically report/audit
A company that does innovative events in digital supply chains and third-party risk management is Black Kite. Black Kite’s intelligence platform quickly and cost-effectively delivers intelligence to third parties and supply chains, prioritizing discovery in a simplified dashboard where risk management teams can easily consume and bridge critical security gaps.
3. Explore strategic partnerships
- Evaluate your own advanced analytics capabilities and study how existing data is performed
- Find partners who can integrate AI, data engineering and analytics into an easy-to-manage platform
Some cloud-based partner solutions that can help build AI successful data are: (a) Data Streptococcus, which integrates with existing tools and helps enterprises build, scale and control data/AI (including Genai and other ML models); (b) Snowflake, which operates a platform that allows data analysis and simultaneously access data sets with minimal latency.
4. Cultivate a data-driven culture
- Democratize data access by implementing self-service AI tools using natural language query (NLQ)
- UPSKILL employees train teams in AI and data literacy and 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
Amazon uses customer data extensively to personalize product recommendations, optimize logistics and make informed business decisions during its operations, making data the main pillar of its strategy.
Establish a data foundation for the future
According to a recent KPMG survey, 67% of business leaders expect AI to fundamentally change their business in the next two years, while 85% feel that data quality will be the biggest bottleneck for improvement. This means it is time to rethink the data itself, focusing not only on storage, but also on usability and efficiency. By being organized now, companies can prevent future AI investments and position themselves as continuous, sustainable innovation.
