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Data unification can unlock AI value for the finance function

A Cloudera-commissioned study highlights the consensus among IT decision-makers on the need for modern data architectures, such as data lakehouses, to manage unstructured and semi-structured data efficiently. Despite these benefits, achieving unified data management is fraught with obstacles, including data quality, scalability, and the integration with existing systems, necessitating a balance between technological innovation and organizational adaptability.

The rise of artificial intelligence (AI) presents both opportunities and challenges for Chief Financial Officers (CFOs) and the finance function. While AI promises to drive strategic decision-making and surface invaluable insights from financial data, a recent Cloudera study reveals a critical roadblock: fragmented data management across organizations.

The survey of IT leaders found a resounding 90% believe consolidating the data lifecycle onto a unified platform is vital for effective AI and analytics initiatives. This need for data unification resonates profoundly with finance teams grappling with data silos that impair visibility, analysis, and accurate forecasting.

Overcoming Obstacles to Trusted Finance AI

The survey was conducted with IT decision makers and found the main obstacles faced in enterprise AI journeys include quality and availability of data (36%), scalability and deployment challenges (36%) and integration with existing systems (35%).

Each of these obstacles can have severe ramifications when it comes to finance’s core responsibilities of reporting, compliance, forecasting, and strategic planning.

Incomplete, inaccurate or outdated financial data, for instance, can lead to flawed AI models and dangerously misguided insights. This jeopardizes not just the AI investment itself but also the integrity of financial decisions with far-reaching consequences for the business.

Similarly, legacy finance systems may struggle to scale and integrate with modern AI capabilities seamlessly. The inability to process and analyse large, diverse data sets can handicap finance AI initiatives from the outset.

“As more enterprises look to transform their businesses to build digital and AI ready solutions for their customers, they are choosing a hybrid and multi-cloud strategy, which in turn creates ‘data sprawl and architectural overruns’ across LOBs, functional units, business applications and practitioner teams,” says Abhas Ricky, Chief Strategy Officer at Cloudera.

“In order for them to effectively leverage AI capabilities, organisations need to design and embed standardised, use case-centric data architectures and platforms that will allow disparate teams to tap into all of their data – no matter where it resides – whether on-premises or in the cloud.”

Architecting a Unified Finance Data Strategy

Within the global business landscape, the vast majority of organisations are keen to develop and deploy Gen AI to disrupt their operations. However, research suggests that many currently underestimate the requirements of building and maintaining a clear AI strategy long-term.

Notably, only a small number of businesses currently believe they have the right level of technology, funding and skill sets to support the rapid adoption of AI.

To surmount these challenges, Cloudera outlines key requirements for a unified data strategy optimized for finance AI:

A modern data architecture anchored in overarching business strategy, with a consolidated data platform operating cohesively across cloud and on-premises environments. This democratizes access to trusted financial data while simplifying analytics processes.

Centralized data management facilitated by flexible cloud technologies robust enough to govern the volume, complexity, security, and compliance demands of financial data. This lays the foundation for reliable AI model development.

Hybrid multi-cloud deployments allowing finance to maintain operational resilience and adapt nimbly to changing conditions. An overwhelming 93% of surveyed leaders underscored the importance of this versatile approach.

For CFOs, unifying data management is not just an IT undertaking but a strategic finance imperative. A unified, AI-ready data strategy enables finance to extract maximum value from data assets, mitigates risks from fragmented silos, and empowers data-driven decision-making across reporting, forecasting, compliance, and strategic planning.

As AI’s influence over finance operations grows, companies that prioritize a cohesive data unification strategy will be better positioned to outpace competitors still grappling with disconnected data challenges. By investing in modern, cloud-enabled data management, CFOs can effectively unleash the power of trusted AI to drive finance transformation.

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