Three ways data can transform AI innovation for SMEs

The promise of generative and predictive AI has organisations across all sectors making plans and investments in new technologies, but very few of them are realising meaningful value in return–leaving them stuck between their AI ambitions and fiscal constraints. 

Successful AI requires not just large volumes of data, but thoughtful and coherent integration of well-governed data from an evermore complex and varied set of data sources.

Globally C-suite executives are prioritising data readiness to achieve AI success, according to a MIT Technology Review Insights report with 82 per cent of C-suite leaders saying that scaling AI or generative AI use cases to create business value is a top priority this year. Their common focus is on robust data foundations, data integration, governance and security. 

For startups and SMEs, a data strategy is essential. It will set these businesses up for the long term, future proofing for potential AI applications.

Tips for SMEs to harness their data for AI

1. Focus on fundamentals

SMEs and startups are unconstrained by legacy technology, yet in starting, will need to establish a strong strategic approach to data built on reliable data pipelines, a modern cloud data platform and a collaborative culture in governance.

SMEs should put in place robust data governance frameworks to manage data quality, privacy, and security. This involves defining clear policies, roles, and responsibilities for data management, ensuring that data is consistently accurate and reliable.Security and governance are perennial issues that need to be addressed to protect sensitive information and ensure compliance with regulations before moving onto innovation.

Another advantage of being unconstrained by legacy technology is the opportunity to build a modular, future-proof data architecture. Governed data lakes that support open table formats and integration with data catalogs have grown in capability, enabling support for both batch and streaming use cases on a common platform.

2. Be business-led

With data fundamentals in place, the promise of AI should not just be considered for the sake of it. Make sure the tech team’s enthusiasm does not take the stage; business leaders should set the priorities first. 

SMEs can take inspiration from National Australia Bank’s infrastructure modernisation approach. It has moved from outdated legacy technology to a new data platform, giving it the power to use data to improve its relationships with customers by offering greater personalisation, scale fraud detection and introduce AI-powered workstreams. 

As a result, and despite the scale of NAB’s operations, it has become an industry leader in the adoption of modern, cloud-native data technologies and improved outcomes for customers at the same time. 

Business stakeholders should be able to provide the perspective needed to set the appropriate priorities and look to software that can grow and scale with your ambitions. 

3. Improve productivity through data integration

At the end of the day, these investments need to generate a quantifiable ROI for the business and improve productivity aligned with the business goals. Particularly as businesses grow, bringing together an ever expanding volume of data can present challenges especially in overcoming data silos.

Data integration combines data from different areas of your business—as well as 2nd and 3rd party data from your customers and partners—taking the raw data and transforming it into a standard format so you can easily analyse big data for insights to drive better business decisions. 

Gill Capital, a leader in Southeast Asian retail, manages well-known global brands including H&M and COS, experienced significant productivity gains by implementing a robust data integration solution across its brands to track end-to-end sales and supply chain operations. The result was a 25% increase in total annual sales and a 121% rise in online sales, with Black Friday sales alone surging by 32%.

The new integration capabilities allowed Gill Capital to transform their data management approach, reducing the need for manual data manipulation in Excel and providing real-time insights. 

This transition not only improved sales tracking and inventory management but also significantly boosted team productivity, saving 90 hours per week, equating to 4,680 hours annually. These efficiencies prevented the need to double the team size.

Any level of business can learn from Gill Capital’s experience by prioritising the consolidation of their data sources into a unified, real-time analytics platform.

There are no shortcuts to unlocking the power of your data. By taking a strategic approach and ensuring strong foundations in data governance and security, SMEs can harness their data to drive innovation and long-term growth. 

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