Nov 4, 2024

Another important lesson is the importance of investing in people as much as we invest in technology. Many of the technology challenges that organizations grapple with can be traced back to organizational challenges and skill gaps. A highly skilled and engaged team is as important for the success of technology and digital initiatives as the technology investments . As organizations are accelerating their investments in AI, both of these are important aspects to consider. Instead of investing in AI for the sake of the technology, or being driven into it due to peer pressure or the fear of being left behind, organizations have to start by identifying how and where AI can be applied to deliver tangible benefits. And, organizations need to invest in upskilling their workforce to be proficient in AI and related data management capabilities which are essential for the success of the AI initiatives . How does Reltio leverage AI in its data unification and management offerings, and what advantages does this provide to organizations? Reltio leverages a “built-in, built with and built for” AI strategy to enhance our data unification and management platform, empowering customers with clean, trusted data essential for fueling their own AI ambitions. Our technology enables faster, smarter data consolidation, helping organizations unlock AI’s potential across their enterprise. One of our key innovations is the Flexible Entity Resolution Networks (FERN), a secure, LLM-powered solution that automates matching records across multiple data sources , dramatically improving accuracy and efficiency. FERN offers out-of-the-box match suggestions without requiring manual rule setting, enabling data stewards to process more records with enhanced precision. With semantic understanding of individual and organization data out of the box, it matches “edge cases” such as nicknames, hyphenated names, abbreviated addresses, and more that rule-based matching cannot detect. Additionally, the Reltio Intelligent Assistant (RIA) uses generative AI and natural language technology to simplify data exploration through a conversational interface. RIA allows users to ask questions, generate visualizations, and create dynamic segments with ease, boosting productivity and expanding user adoption. These AI-driven components of our platform empower organizations to rapidly deliver trusted, real-time data for their AI initiatives across different business programs. In other words, our AI-powered products unlock the full potential of their data and enable trusted AI that businesses can act on. Our customers use this enterprise-wide data foundation to improve decision-making, provide a better customer experience , increase operational efficiency, and simplify compliance and risk management . Discuss the solutions to the top challenges organizations face with siloed data. As you’d expect, data fragmentation and quality concerns are some of the biggest problems plaguing enterprises and work teams today. Cloud-based data unification and management solutions have emerged as a ray of hope to fixing data silos and fragmentation, offering a comprehensive strategy to navigate the enterprise data conundrum by providing high-quality, rich data where it is needed when it is needed in a flexible, responsive way. Modern data unification offerings include the latest innovations to remove data silos and curate comprehensive 360 profiles in real-time for all departments and teams across an organization. Here are some innovations that forward-looking companies are using today: Real-time data unification and access for enterprise-wide consumers. Real-time is no longer a luxury when AI is operationalized. Unified, up-to-date data must be available in milliseconds in operational use cases such as personalized offers or next-best action during customer conversations. Flexibility and scalability are essential to be responsive to business needs, while taking advantage of constantly evolving variety and volume of data, and evolving technology landscape. Cloud native technologies for data unification and management have become the norm now. Data management teams need to invest in modern solutions built on technology foundations . Forward-looking companies are harnessing the power of cloud-native MDM SaaS with industry-specific prebuilt components and advanced AI capabilities . They’re using generative AI (Gen AI) and large language models (LLMs) to drive 10x productivity gains, employing chat-based digital asset search tools and pretrained machine learning models to match and merge individual data across systems efficiently. Innovative businesses are also accelerating their access to high-quality data and achieving faster time-to-value by using prebuilt, industry-specific SaaS MDM on day one of implementation. These companies leverage prescriptive delivery methodologies that enable them to implement solutions quickly, often going live within 90 days and demonstrating clear business value before scaling to additional use cases. What role do strategic technology partnerships play in the product strategy, and how do these collaborations contribute to delivering joint solutions? Born in the cloud as a SaaS offering, we run natively on Microsoft Azure, AWS, and Google Cloud, which makes the integration with their cloud services easier for our customers. Today, businesses want to build a comprehensive data management solution with best-of-breed components in a fast and cost-effective way. That’s why we focus our product strategy on creating interoperability with market-leading technologies through prebuilt integrations. Examples include  Microsoft Purview,  Collibra, and Alation for simplifying data governance . AI and analytics are also key areas of focus for our partnerships. We have worked with Databricks to offer real-time data pipelines to Databricks Delta Lake which allows our joint customers to tap into the power of their AI engine using trusted real-time data. Data enrichment partnerships, such as ZoomInfo, Moody’s, and Medpro, allow our customers to easily validate and augment their data in Reltio. How are AI-driven insights helping organizations derive more value from their data? We see AI-driven insights powering multiple business functions to drive better business outcomes from improving customer experience and loyalty, increasing conversion to revenue in different touchpoints, creating productivity for employees by increasing automation and accuracy of basic processes. AI-driven insights also help with improving risk assessments and preventing losses. In financial services, AI is enhancing fraud detection, optimizing lending decisions, and improving marketing campaigns with propensity scores. Similarly, insurance companies are deploying AI for risk-based pricing, detecting fraudulent claims, and reducing customer churn through predictive models. In life sciences and healthcare , AI helps accelerate drug discovery and improve patient outcomes. In other markets such as retail, travel/hospitality, or technology, AI is helping businesses create targeted marketing strategies with hyper-personalized recommendations. For example, B2B companies use AI to prioritize sales prospecting and reduce customer churn , while B2C companies benefit from AI-driven demand forecasting and inventory management. AI’s outputs are only as good as the data that fuels it. Each of these applications depends on comprehensive, trusted, and up-to-date datasets. Without trusted data, AI models can suffer from inaccuracies, AI’s predictions or classifications can be wrong if data is inaccurate, biased, or incomplete– leading to poor business decisions. This creates an underlying need for clean, connected, and accurate real-time data, especially when AI is factored in. Having data transparency and audibility is also fundamental to building ethical AI practices, which is what we all want in our personal and professional lives. The data leaders we work with understand that you need to streamline your data governance foundation to have the assurances around your AI investments and do this without slowing down your innovation. Luckily,  with the advancement in data management technologies this is now possible and easier than many think. I mentioned how AI transforms data unification, exploration, and segmentation in the earlier parts of this interview. Beyond that, the ability to do continuous real-time ingestion, processing, data quality monitoring, and mobilization at large scale is now possible with cloud-native technologies. Before we close, please share some of the future trends in data management and master data management (MDM) that you believe will redefine the IT industry. Data management solutions and MDMs will become more verticalized – knowledge of business semantics for data domains by verticals, Faster time to value – solutions will have to become flexible, and faster. Automation and intelligence will shorten data unification and integrations. Interoperability among best-of-breed solutions – even though we will see some consolidation, best of breed solutions for different aspects of data management are here to stay. Standards will emerge and integration between different data management solutions for interoperability is critical. AI tech including digital assistants , agents, autonomous bots and co-pilots will enhance productivity and revolutionize how decisions are made and how tasks are completed. However, for this vision to become a reality, enterprises must move beyond their current systems of record (SORs) and embrace a unified, enterprise-level SOR that delivers a comprehensive, accurate, real-time view of their data. I don’t see a future for the incumbent applications as SORs in this new reality. To power AI co-pilots and agents, enterprises need multimodal data that is both available and unified, accurate, and current. Incumbent systems like CRMs and ERPs provide only a partial or fragmented view of enterprise data. A CRM may have some customer details, while an ERP handles product or supplier data, but none provide the full 360-degree view that intelligent agents and co-pilots require. The incumbent SORs are inherently limiting because they create more data silos. Far from helping, the incumbents are standing in the way of the future. The key to unlocking the future lies in unifying enterprise data, creating a real-time, accurate multimodal SOR that enables AI to perform at its best. Enterprise data and IT leaders who t********** today will set their organizations up for success in the AI-powered world of tomorrow. [To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]