Pursuing an AI project? Get your data in order first!

Over the past year, the rapid rise of artificial intelligence (AI) has provided many Asia Pacific businesses with a strong competitive edge. But while enterprise AI can drive success, there can be significant challenges in integrating these solutions. The excitement surrounding AI adoption often overlooks a very necessary protocol – data management. Without robust data handling strategies, even the most advanced AI initiatives can falter.

One of the primary hurdles for organisations is understanding the compute and networking demands of an AI lifecycle. Misleading assumptions about the complexity of AI technology can cause misallocation and project delays, hindering a business' day-to-day operations. Such delays can lead to subsequent risks associated with data security. In fact, a recent survey revealed that only 38% of businesses had established a policy to mitigate the inaccuracy of generative AI.

Although AI has the capability to drive efficiencies within a business, a sound deployment of its capabilities must start with proper data management practices. 

Mapping out cloud and data storage policies

The rapid rate at which AI is developing has forced many organisations to rush into implementing the technology without proper due consideration of risks or workload. However, it is critical that companies know how and where their data is being stored. An organisation that lacks proper control of their data cannot realise its unlocked potential. Therefore, creating data visibility enables companies to avoid short term risks and ultimately make more informed business decisions.

First steps to take involve defining data storage locations, reviewing access protocols and implementing additional security measures. The integration of these practices will empower companies to regain control and put their data to good use. Data visibility can help negate unnecessary costs, such as redundant storage and compliance challenges, as company business grows. 

Ensuring compliance with regulations

Ensuring that data storage solutions comply with government regulations is another critical factor in the proper adoption of AI technology. In Singapore, the Personal Data Protection Act (PDPA) sets stringent guidelines for data protection and privacy. Non-compliance can lead to severe penalties and damage to a company's reputation, ultimately resulting in financial loss. 

Similarly, other countries within the APAC region have their own regulatory frameworks that businesses must adhere to. According to the International Association of Privacy Professionals (IAPP), APAC countries are consistently evolving their data privacy laws. These regular updates will require enterprises to remain vigilant in adopting mandatory practices. For example, Japan's Act on the Protection of Personal Information (APPI) and China's Personal Information Protection Law (PIPL) present unique compliance challenges that must be addressed proactively.

Combining a business's proactivity in adopting clean data practices whilst adhering to local regulation are both needed to create a robust AI implementation plan. A proper roadmap will protect both company data and fast-track AI adoption. 

Developing a robust AI implementation plan

Creating a robust plan is essential for aligning AI projects with business objectives and ensuring the necessary infrastructure is in place. Any plan should outline the specific data requirements for AI solutions, detail the steps for integrating these solutions, and provide a guide for setting up data storage systems that support AI initiatives. Long-term planning should also consider scalability and future AI needs to avoid pressure points as projects, with the corresponding data sets, continue to grow. Companies should see any plan as dynamic rather than stagnant. Projects will likely see increased demand for volume and variety of data as AI applications become more sophisticated.

Strategic recommendations for AI Success

To achieve success with AI, companies in Singapore and the APAC region should consider the following factors when developing their AI roadmap.

• Invest in data infrastructure: Building a robust data infrastructure that can handle large volumes of diverse data types is crucial. This includes scalable cloud storage solutions and high-speed networking capabilities.
• Enhance data quality: Implement processes and technologies to ensure data accuracy, consistency, and reliability. Data cleansing and validation tools can help maintain high data quality.
• Leverage advanced analytics: Utilise advanced analytics to gain insights from data, identify trends, and make informed decisions. This can help optimise AI models and improve their effectiveness.
• Focus on security: Implement robust data security measures to protect sensitive information from breaches and cyber-attacks. Encryption, access controls, and regular security audits are essential.

As enterprises in Singapore and the APAC region embark on their AI journeys, prioritising their data management practice will remain crucial. Mapping out cloud and data storage policies, ensuring compliance with regulations, and developing robust AI implementation plans will enhance a business's chances of successful AI adoption. Proper data handling and strategic planning will not only help mitigate the risks associated with AI projects but also position companies to leverage AI's full potential effectively.