In a Gartner survey of data management leaders, 63% said that they either do not have or are unsure if their organizations have the right data management practices for AI. One consequence, according to the survey, is that by next year organizations will abandon an estimated 60% of AI projects unsupported by AI-ready data.
What constitutes “AI-ready” data is open to interpretation, but it’s a safe bet that many of the projects doomed to fail will be those from organizations with more relaxed standards. The reality is that AI and customer engagement systems are only as good as the data that feeds them. And with AI rapidly becoming indispensable for broader customer experience (CX) use cases, the urgent need to support AI projects with clean, accurate, timely, trustworthy, and actionable data elevates data readiness as a top priority. Put simply, don’t plan your AI strategy before you’ve invested in your data strategy.
Address AI Challenges with Data Readiness
While some enterprises consider data management an operational cost, a strategic commitment to data readiness represents a crucial investment for maximizing the ROI of AI and CX initiatives. Ensuring robust data quality, semantic consistency, and persistent identifiers are foundational for successful AI model training and deployment, as well as streamlined operations and data-driven decision making.
Data readiness directly addresses several key challenges in AI:
- Enhanced Model Accuracy and Reliability: High-quality, clean data minimizes noise and bias, leading to more accurate and reliable AI models. Inconsistent or erroneous data can result in suboptimal model performance and flawed predictions.
- Improved Feature Engineering: Well-organized and semantically consistent data facilitates the extraction of meaningful features, which are critical for training effective AI algorithms.
- Reduced Training Time and Compute Costs: Automated data prep streamlines the preprocessing stage of the AI lifecycle, significantly reducing model training times and the associated computational resources.
- Explainable AI (XAI): Consistent and well-documented data, coupled with persistent identifiers for traceability, enhances the interpretability and explainability of AI model outputs, fostering trust and facilitating debugging.
- Scalability and Interoperability: Data readiness ensures data assets can be efficiently accessed, integrated, and used across multiple AI applications and platforms, promoting consistency and reuse within the data ecosystem.
Investing in data readiness is thus not merely a cost mitigation strategy, but a fundamental enabler for realizing the transformative potential of AI to achieve a significant competitive advantage.
What it Means to Have AI-Ready Data
One reason why companies make the mistake of thinking that their data is AI-ready when it is anything but is because of the investments they’ve made in customer data platforms (CDPs), master data management (MDM) systems, data clouds, and other technologies that overpromise and underdeliver as far as getting data ready for business use. In unifying data that has not been made fit-for-purpose they create poor outcomes along with high data consumption. To remedy the problem, companies resort to data engineering resources, enterprise consulting and/or technology initiatives – all costly, and all time-consuming.
The Redpoint Data Readiness Hub is purpose-built to solve poor data quality by making customer data ready for use across the enterprise. It does this for systems and teams that power AI, analytics, CX and operations. The Data Readiness Hub generates data that is right – complete, accurate and timely – and fit for purpose – actionable, trusted and compliant. (See Figure 1)
Figure 1: Data readiness makes enterprise customer data right – and fit for purpose
The Data Readiness Hub solves for data quality upstream – at the point of data ingestion. It creates and continuously updates unified profiles for any enterprise use. By resolving accuracy, identity, and compliance gaps before data enters critical systems, The Data Readiness Hub creates a solid, trustworthy data foundation that yields smarter decisions, relevant engagement and faster time to value.
Whereas traditional tools merely aggregate data, Redpoint combines automated data quality, precise identity resolution, and real-time processing – as well as a composable architecture built for speed and flexibility.
A key result is smarter AI and decisioning, with high-integrity data that boosts model accuracy, improves targeting precision and powers next-best-actions, leading to proven superior outcomes and increased revenue.
The Redpoint approach works with all modern technology that AI data engineers and analysts work with – allowing the right data that is fit-for-purpose to be available when it needs to be available – and in the format that is most effective.
