In 2006, British mathematician Clive Humby said, “data is the brand new oil.” While the phrase is sort of a cliché, the appearance of generative AI is respiratory latest life into this concept. A world study on the Way forward for Enterprise Data & AI by WNS Triange and Corinium Intelligence shows 76% of C-suite leaders and decision-makers are planning or implementing generative AI projects. 

Harnessing the potential of knowledge through AI is important in today’s business environment. A McKinsey report says data-driven organizations exhibit EBITDA increases of as much as 25%. AI-driven data strategy can boost growth and realize untapped potential by increasing alignment with business objectives, breaking down silos, prioritizing data governance, democratizing data, and incorporating domain expertise.

“Corporations must have the obligatory data foundations, data ecosystems, and data culture to embrace an AI-driven operating model,” says Akhilesh Ayer, executive vp and global head of AI, analytics, data, and research practice at WNS Triange, a unit of business process management company WNS Global Services.

A unified data ecosystem

Embracing an AI-driven operating model requires corporations to make data the muse of their business. Business leaders need to make sure “every decision-making process is data-driven, in order that individual judgment-based decisions are minimized,” says Ayer. This makes real-time data collection essential. “For instance, if I’m doing fraud analytics for a bank, I would like real-time data of a transaction,” explains Ayer. “Subsequently, the technology team may have to enable real-time data collection for that to occur.” 

Real-time data is only one element of a unified data ecosystem. Ayer says an all-round approach is obligatory. Corporations need clear direction from senior management; well-defined control of knowledge assets; cultural and behavioral changes; and the flexibility to discover the correct business use cases and assess the impact they’ll create. 

Aligning business goals with data initiatives  

An AI-driven data strategy will only boost competitiveness if it underpins primary business goals. Ayer says corporations must determine their business goals before deciding what to do with data. 

One approach to start, Ayer explains, is a data-and-AI maturity audit or a planning exercise to find out whether an enterprise needs an information product roadmap.This will determine if a business must “re-architect the best way data is organized or implement an information modernization initiative,” hesays. 

The demand for personalization, convenience, and ease in the shopper experience is a central and differentiating factor. How businesses use customer data is especially necessary for maintaining a competitive advantage, and might fundamentally transform business operations. 

Ayer cites WNS Triange’s work with a retail client for instance of how evolving customer expectations drive businesses to make higher use of knowledge. The retailer wanted greater value from multiple data assets to enhance customer experience. In an information triangulation exercise while modernizing the corporate’s data with cloud and AI, WNS Triange created a unified data store with personalization models to extend return on investment and reduce marketing spend. “Greater internal alignment of knowledge is just a method corporations can directly profit and offer an improved customer experience,” says Ayer. 

Removing silos 

No matter a company’s data ambitions, few manage to thrive without clear and effective communication. Modern data practices have process flows or application programming interfaces that enable reliable, consistent communication between departments to make sure secure and seamless data-sharing, says Ayer. 

This is important to breaking down silos and maintaining buy-in. “When corporations encourage business units to adopt higher data practices through greater collaboration with other departments and data ecosystems, every decision-making process becomes routinely data-driven,” explainsAyer.  

WNS Triange helped a well-established insurer remove departmental silos and establish higher communication channels. Silos were entrenched. The corporate had multiple business lines in numerous locations and legacy data ecosystems. WNS Triange brought them together and secured buy-in for a standard data ecosystem. “The silos are gone and there’s the flexibility to cross leverage,” says Ayer. “As a bunch, they resolve what prioritization they need to take; which data program they need to select first; and which businesses must be automated and modernized.”

Data ownership beyond IT

Removing silos will not be at all times straightforward. In lots of organizations, data sits in numerous departments. To enhance decision-making, Ayer says, businesses can unite underlying data from various departments and broaden data ownership. One approach to do that is to integrate the underlying data and treat this data as a product. 

While IT can lay out the system architecture and design, primary data ownership shifts to business users. They understand what data is required and learn how to use it, says Ayer. “This implies you give the ownership and power of insight-generation to the users,” he says. 

This data democratization enables employees to adopt data processes and workflows that cultivate a healthy data culture. Ayer says corporations are investing in trainings on this area. “We have even helped a number of corporations design the obligatory training programs that they need to take a position in,” he says. 

Tools for data decentralization

Data mesh and data fabric, powered by AI, empower businesses to decentralize data ownership, nurture the data-as-a-product concept, and create a more agile business. 

For organizations adopting an information fabric model, it’s crucial to incorporate an information ingestion framework to administer latest data sources. “Dynamic data integration should be enabled since it’s latest data with a brand new set of variables,” says Ayer. “The way it integrates with an existing data lake or warehouse is something that corporations should consider.” 

Ayer cites WNS Triange’s collaboration with a travel client for instance of improving data control. The client had various business lines in numerous countries, meaning controlling data centrally was difficult and ineffective. WNS Triange deployed an information mesh and data fabric ecosystem that allowed for federated governance controls. This boosted data integration and automation, enabling the organization to grow to be more data-centric and efficient. 

A governance structure for all

“Governance controls might be federated, which suggests that while central IT designs the general governance protocols, you hand over a few of the governance controls to different business units, corresponding to data-sharing, security, and privacy, making data deployment more seamless and effective,” says Ayer. 

AI-powered data workflow automation can add precision and improve downstream analytics. For instance, Ayer says, in screening insurance claims for fraud, when an insurer’s data ecosystem and workflows are fully automated, instantaneous AI-driven fraud assessments are possible. 

“The flexibility to process a fresh claim, bring it right into a central data ecosystem, match the policyholder’s information with the claim’s data, and be certain that the claim-related information passes through a model to provide a advice, after which beat back that advice into the corporate’s workflow is the outstanding experience of improving downstream analytics,” Ayer says. 

Data-driven organizations of the longer term

A well-crafted data strategy aligned with clear business objectives can seamlessly integrate AI tools and technologies into organizational infrastructure. This helps ensure competitive advantage within the digital age. 

To learn from any data strategy, organizations must repeatedly overcome barriers corresponding to legacy data platforms, slow adoption, and cultural resistance. “It’s extremely critical that employees embrace it for the betterment of themselves, customers, and other stakeholders,” Ayer points out. “Organizations can stay data-driven by aligning data strategy with business goals, ensuring stakeholders’ buy-in and employees’ empowerment for smoother adoption, and using the correct technologies and frameworks.”