Artificial intelligence is set to transform the way companies operate over the next decade, making workers more productive, improving customer service and offering firms invaluable insights on their operations. Yet despite the huge competitive advantages AI offers, many early adopters have not achieved the results they hoped for, while others have found it hard to adopt these systems at scale.
Typically, data management issues are to blame, as organisations struggle to access the high-quality data needed to power the AI algorithms supporting their operations. Poor data input leads to bad outcomes at scale, as using poor-quality, incomplete or untrusted data as a foundation for AI assistants results in inaccurate or biased decisions that are of no help to firms, and may even hinder them.
Poor data management could also create compliance problems, as organisations lose track of the data driving their AI platforms, putting themselves at risk of breaching incoming AI regulations. So how can organisations get a grip on their data today and fully reap the benefits of the AI revolution?
No silver bullet
Greg Hanson is GVP and head of EMEA North for Informatica, a leading cloud data management provider that helps businesses handle the complex challenges of dispersed and fragmented data to innovate with their data and AI.
“Technology forms a major part of the solution,” says Hanson. “But organisations also need a data management strategy and cultural change which involves sponsorship at board level, engagement of people and the establishment of governance of polices.”
This is why Informatica works closely with leading advisory organisation Cognizant, a global strategic alliance partner of Informatica, that helps firms embed the tech, teams and processes for successful AI adoption. Making the most of data is a theme that both Informatica and Cognizant are witnessing among customers, says Sean Heshmat, GGM data and AI head at Cognizant.
He adds: “without the right input you will simply make incorrect decisions at an accelerated pace. Firms need to build the right foundations to ensure AI works for them, not against them.”
Organisations need a data management strategy and cultural change which involves sponsorship at board level, engagement of people and the establishment of governance of polices
It’s a sentiment that Hanson agrees with: He adds: “Many organisations believe AI will be an overnight silver bullet but there is a significant amount of foundational work required to benefit from this technology. That’s because when it comes to data, the old adage applies – if you put garbage in, you will get garbage out.”
For any business adopting AI at scale, the first task is to corral all the data it has in one place so it can be processed and accessed with ease. But this can be challenging as large firms typically have multiple divisions, servers and systems in place around the world and data is often siloed.
To counter this they must simplify their data landscape, standardise the tech they use and deploy an effective data catalogue to organise and manage data assets properly.
“A company can’t get a proper picture of their customers or operations if their datasets are incomplete or disorganised,” says Heshmat. “Similarly, AI can’t make quality decisions in realtime without real-time data, and that is hard to achieve with myriad different systems and integration points.”
Quality control
According to The State of AI in 2023 McKinsey survey , inaccuracy is the biggest risk companies face when it comes to AI. Yet just 32% said they were mitigating that threat of inaccurate data and inaccurate outcomes, even lower than the 38% who said they mitigated cybersecurity risks.
As such, it is vital that organisations have high-quality data to power their AI platforms, although identifying, verifying and extracting this information can be challenging. Staff also need to be able to access data with ease, while establishing robust data principles to ensure regulatory compliance.
Firms have already had to adapt to the EU’s GDPR rules, and over the next few years the EU’s AI Act will come into force, requiring companies to demonstrate they have full oversight of the data going into their AI platforms, with breaches leading to significant fines. Informatica’s solutions offer organisations vital support as they prepare for AI adoption. The firm’s AI cloud platform enables them to manage and organise all their data with ease via one unified platform that breaks down silos.
It also lets users locate, extract and cleanse data to develop first-class algorithms, while supporting good data governance by recording the data trails and providing data lineage visualisation sitting behind automated decisions, simplifying compliance.
‘Democratising data’
Cognizant deploys Informatica’s solutions as part of its wider work supporting organisations’ digital transformations. It acts as a trusted partner to companies, helping them to change their data culture and processes and get the most of data management and AI systems.
“Together we help organisations democratise data and bring it to life,” says Hanson. “This helps to make it more easily accessible to those in the company that need it – subject to data access controls. With Informatica, teams no longer need to ask IT for the information they require to make more informed business decisions, it is self-serve and ready to use.”
Gilead Sciences knows first-hand why good data governance is essential to business success. The global biopharmaceutical company worked jointly with Informatica and Cognizant to bring more value to customers by getting more out of the data the firm had amassed through the manufacture and development of advanced treatments.
Gilead wanted to improve its master data management processes and compliance controls, while bringing data into the hands of employees who needed it. It deployed a data mesh framework on Amazon Web Services, supported by Informatica’s AI-powered cloud platform which provides useful, holistic data to decision makers. As a result, Gilead was able to speed up its drug development, discovery and commercialisation processes and bring down costs.
“To us, a cloud-based enterprise data platform is not just about cost or operational efficiencies. For us, it’s a competitive differentiation in the industry, we can make better, faster decisions about our business,” says Murali Vridhachalam, head of cloud, data & analytics at Gilead. Cognizant and Informatica have partnered to help many global brands deliver greater value to their customers. One such brand is BMW, which worked with both Informatica and Cognizant to implement a unified platform for global product data that provides a trusted, omnichannel view of critical information.
The Informatica system enables the German manufacturer to deliver consistent comms globally and one that is helping to power a next-generation customer experience capable of leveraging new technologies like AI. BMW finds itself in the midst of an AI journey as it seeks – like many other businesses – to unlock the huge potential benefits of AI, but to do so requires organisations to resolve their long-standing data management issues.
“Adding that good data governance is an end-to-end process, not a one-shot deal. says Heshmat. “It’s about having a data-driven culture, the right tech, and proper communication between your board, the business and IT to make sure data is treasured and protected across your organisation.”
Hanson agrees, “AI requires holistic, trusted and governed data for companies to succeed with correct, unbiased insights. Our goal is to help firms unlock the power of AI and bring their data to life.”
To find out more please visit: informatica.com/gb
Artificial intelligence is set to transform the way companies operate over the next decade, making workers more productive, improving customer service and offering firms invaluable insights on their operations. Yet despite the huge competitive advantages AI offers, many early adopters have not achieved the results they hoped for, while others have found it hard to adopt these systems at scale.
Typically, data management issues are to blame, as organisations struggle to access the high-quality data needed to power the AI algorithms supporting their operations. Poor data input leads to bad outcomes at scale, as using poor-quality, incomplete or untrusted data as a foundation for AI assistants results in inaccurate or biased decisions that are of no help to firms, and may even hinder them.
Poor data management could also create compliance problems, as organisations lose track of the data driving their AI platforms, putting themselves at risk of breaching incoming AI regulations. So how can organisations get a grip on their data today and fully reap the benefits of the AI revolution?
