In a survey of hundreds of chief data officers (CDO) sponsored by Amazon Web Services and the MIT CDO Symposium, data quality and finding the right use cases were ranked as the two biggest challenges (46 and 45 percent, respectively) for realizing the potential of generative AI (genAI).
And while 93 percent of survey respondents agreed that data strategy is critical for getting value from genAI, only 37 percent agreed that their organization has the right data foundation in place.
A Gartner survey on AI in general produced similar findings, with 40 percent of organizations claiming that a lack of AI-ready data is the top barrier to implementing AI – with the real-world consequence of more than half of AI projects expected to be abandoned by next year.
Solving AI Challenges with Data Readiness
The urgent need to support AI use cases elevates data readiness as a top priority for organizations looking to optimize use of their customer data. With AI rapidly becoming indispensable for broader customer experience (CX) use cases, ensuring that data is accurate, timely, complete and actionable is essential to guarantee that results meet business expectations.
While some enterprises consider data management simply a necessary operational cost, those that think of it more strategically will best maximize the ROI of AI and CX initiatives. Robust data quality, semantic consistency, persistent identifiers and accurate matching set the foundation 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:
- Relevance: A deeper understanding of customers is only possible through data. By having the most complete, accurate and timely customer profiles, AI applications are best able to make predictions and decisions that in turn create relevant offers, messages and content in the context of each customer’s journey.
- 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.
Forrester says that “companies that prioritize data readiness for AI see a significant improvement in operational efficiency and customer satisfaction.” Investing in data readiness is therefore not merely a cost mitigation strategy, but a fundamental enabler for realizing the transformative potential of AI and achieving a significant competitive advantage.
Data Readiness for AI in Action
One organization that realized the potential of AI through investment in data readiness includes a CPG company that used Redpoint technology to achieve a 79 percent increase in conversion rates and a 144 percent increase in sales.
One challenge that prevented the company from achieving its goal of increasing direct-to-consumer sales was having 20 different data sources – including batch and streaming – comprising more than 2,400 data elements. This made it all but impossible for the company to make relevant, real-time product recommendations for customers engaging online. In addition, the company was unable to provide personalized offers based on product registrations, which eliminated a powerful avenue for driving long-term customer loyalty.
With AI rapidly becoming indispensable for broader customer experience (CX) use cases, ensuring that data is accurate, timely, complete and actionable is essential to guarantee that results meet business expectations.
Hosted on Microsoft Azure for virtually unlimited scalability, Redpoint delivered a production-level recommendation engine using out-of-the-box predictive models and built-in machine learning to deliver real-time product suggestions across all enterprise touchpoints. Previously, decisions had been siloed without taking into account an individual customer’s preferences, behaviors or previous transactions. This all changed with having a solid data foundation from reliably integrating all the different data elements – each with unique characteristics, latencies, and levels of quality into an accurate, real-time unified profile for each customer.
Real-time, highly relevant product recommendations were also made possible because Redpoint technology enabled marketing teams to select their own campaigns, identify and segment audiences, and pull content for use across channels without the need for IT assistance – or the need for deep technical expertise. In addition to higher sales and an increase in conversions, the company used Redpoint to increase e-mail sign-ups, reduce cart abandonment, provide triggered coupons for product registrations, market gift subscriptions more effectively, and execute more effective win-back campaigns by targeting lapsed customers with relevant, personalized offers.
The Data Readiness Difference
A Redpoint customer in the travel and hospitality industry achieved an 80 percent reduction in model prep time by using better data.
“By leveraging Redpoint’s data management strengths, we quickly got a 360-degree view of our customers. This saved us an enormous amount of time and capital expense,” said the company’s Director of CRM and Marketing.
By using Redpoint, the company was able to keep its entire IT infrastructure intact without having to change a single reservation platform. Redpoint integrated more than 100 data sources from an extraordinarily diverse set of properties, transactional, and management systems into a common database, handling all cleaning, standardization, and enrichment with hundreds of third-party appended attributes.
With a unified customer profile for each individual guest in place, the company used Redpoint’s machine learning capabilities to create dynamic segments using eight distinct personas.
“Using Redpoint, we focused our campaign messaging on guests’ personal interests, experiences, and past interactions with our offers. This resulted in a 91 percent year-over-year revenue improvement and 103 percent year-over-year increase in transactions,” said the marketing director.
Explore the Data Readiness Difference
Because more than half of AI projects will be abandoned by next year due to a lack of AI-ready data, the costs of inaction are mounting. In contrast, Redpoint customers are seeing measurable returns by investing in data readiness, including steep revenue gains, improved conversion rates and faster time-to-value for AI models. By addressing data quality upfront, Redpoint eliminates inefficiencies, boosts model accuracy, and enables enterprise-scale AI success. The results speak for themselves: Organizations that prioritize data readiness are building a competitive advantage in today’s AI-driven economy.
For more on Redpoint’s approach to data readiness and what it means to have your data ready for business use across the enterprise, click here.
