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Data integrity key to business success
As more organizations and individuals become increasingly familiar with sophisticated technologies such as generative AI (genAI), every industry will need to develop a robust data integrity strategy. We talk to experts at Precisely (www.precisely.com) for their views on how businesses can achieve and maintain trusted data capable of delivering more dependable results for the confident data-driven decisions needed by agile businesses if they are to meet their risk management and compliance needs.
“While artificial intelligence (AI) is everywhere these days, the truth is, very few organizations have AI-ready data in place to power successful business outcomes. In 2024, we expect organizations to continue implementing traditional AI to increase efficiency in both the back and front office, while exploring how genAI can create new capabilities, products, and services. In the process, businesses will increasingly realize that data is central to their AI success and will seek to integrate all their relevant and critical on-prem and cloud data into the datasets used to train their AI models – including complex data residing on mainframes – to minimize bias and improve the accuracy of their AI outcomes,” explains Tendü Yoğurtçu, PhD, Chief Technology Officer.
“Ensuring that data meets rigorous quality metrics related to validity, accuracy, completeness, standardization, and more is just the first step for businesses looking to increase trust in the quality of the data used by their AI systems. Organizations will need AI governance to reduce risks, make sure AI is used in an ethical and responsible way and build trust among stakeholders. AI and data governance frameworks will be critical for organizations to ensure AI initiatives have the best possible outcomes while minimizing risk.”
Adding to the mix is Environmental, Social and Governance (ESG). In 2024, ESG data management and reporting strategy takes on a whole new meaning with the application of the European Union’s Corporate Sustainability Reporting Directive (CSRD), which started on 1st January. “To meet mandatory formats of ESG disclosure and reporting standards, businesses will need to implement robust data integrity strategies to ensure the accuracy, consistency, and context of their data. Without this in place, many organizations face challenges with data that lives in silos, is incomplete, unstandardised, or lacks the detail required to make it fit for purpose. This is not sufficient for the thorough level of insights required to make strategic decisions,” says Pat McCarthy, Chief Revenue Officer.
“More than ever before, companies require trustworthy data to make confident decisions, set goals, and track the progress of sustainability initiatives. If companies aren’t already investing in the integrity of their data, they are already behind the curve and any new ESG regulations introduced will only continue to widen that gap.”
“In 2024, we’ll see companies under even more pressure to deliver advanced analytics, explore new uses for AI, and keep up with customer demands for up-to-the-minute insights,” says Eric Yau, Chief Operating Officer. “In response, organizations will need to move further into their hybrid and multi-cloud journeys – breaking down data silos and ensuring access to trusted data in as close to real-time as possible. As companies continue to see the combined power of mainframe and cloud environments, they will focus on which workloads and applications are best suited for the cloud and which should remain on-premises. Ultimately, hybrid cloud success will rely on a robust data integrity strategy, allowing organizations to access valuable data from modern and legacy systems, proactively detect issues, deliver the highest-quality data, uncover previously hidden relationships, and enrich data with added context.”
“Although companies have been creating more valuable data with data enrichment and geo addressing for some time, we can expect this to accelerate in 2024 as companies prioritize solutions that help them to build the consistency and accuracy of their data as well as enriching it for additional context,” adds Dan Adams, Senior Vice President – Data Strategy & Operations. “AI will be a key driver for this, as enhancing data with additional trusted geolocation information increases the certainty of an AI model’s outputs to automate decisions and increase the speed of gaining insights. Companies looking to unlock the full potential of AI models in 2024 will increasingly invest in understanding data lineage as an essential element of their data integrity strategy.”
As enterprises investigate how AI can benefit their organizations, using governed and verified data sets will bring confidence to the decisions and outcomes that are generated by AI. “Internal data marketplaces are based on data sets managed by a product owner that is governed and verified,” says Emily Washington, Senior Vice President – Product Management. “This ownership of data provides a data product that can be used with confidence because it has been organised as a singular asset or product with a purpose. It considers any local influences on the data and can be easily tailored to fit any business need by bringing together concepts of data governance and the distributed nature of data mesh into one data product.”
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