Artificial Intelligence (AI) will stay now! The effect of AI is seen more in the Asia-Pacific (APAC) regions, where companies embrace it with open arms. However, the reality is that these companies are unable to harness the full potential of the technology as they lack the foundational data infrastructure and strategy needed to support AI at scale.
Undoubtedly, the gap between AI integration and reality is accentuating at an exponential speed, and organizations favoring AI should intervene to improve data quality. Otherwise, the AI projects will start sliding downwards.
As per reports, the data of many organizations isn’t ready for artificial intelligence.
This is pertinent because AI-ready data improves decision-making with real-time insights and predictive analytics. This further leads to operational efficiency and enhances competitiveness through AI innovations. Additionally, it works toward easy integration with future technologies while improving data governance and maximizing the value of data investments.
An expert analyst from Garter said that there is a possibility that at least 30% of generative artificial intelligence (GenAI) projects will be stopped after proof of concept by the end of 2025. Who are the culprits for this? All these happened due to poor data quality, inadequate risk controls, rising costs, and other factors.
All these communications lead to one fundamental truth, i.e., AI-readiness is only available when there is a high-quality data foundation.
But there is another side of the story that several companies operating in APAC regions are unable to meet levels with this standard. The research was conducted by Informatica, which stated that data fragmentation and complexity are major hurdles. The report further added that 56% of APAC data leaders are struggling to balance over 1,000 data sources within their company.
Additionally, respondents in the APAC regions reported facing problems such as AI ethics (42%) and data privacy and protection (42%).
This disconnect is hampering several industries, such as finance, retail, and healthcare, where data is a vital asset in powering organizations.
An essential issue for organizations is how they focus on AI readiness. As a matter of fact, AI models are naturally dependent on data! However, they need data from different sources that are of high quality and transparency.
Critical pitfalls can still occur with even the most advanced AI tools if the data being used is inconsistent, inaccurate, or poorly managed.
The effectiveness of AI models is directly linked to the quality of the data used for training. If the data is faulty - whether it's missing, wrong, or disorganized - it can distort AI models and worsen biases. Consequently, models can generate incorrect or deceptive results.
Having data spread across different environments can cause operational inefficiencies, resulting in more time and resources needed to upkeep AI systems. This problem is exacerbated in companies that continue to depend on outdated systems and manual data procedures.
Preparing your data to be ready for AI is vital for any company in APAC looking to use advanced analytics and machine learning successfully. Below are a few effective tactics for preparing your data for AI utilization:
1. Assess and Cleanse your Data: Begin by thoroughly examining current datasets to pinpoint and fix inconsistencies, missing values, duplicates, and inaccuracies. High-quality, clean data is crucial for AI model success.
2. Develop a Robust Data Governance Structure: Create a solid data governance framework that is in line with and regularly updated according to regulatory regulations and changes in the region, such as the Privacy Act in Australia or the Personal Data Protection Act in Singapore. A clearly outlined governance structure will guarantee data integrity, adherence to regulations, and protection. This can act as a guide for AI projects, ensuring they are in line with business goals and ethical guidelines.
3. Invest in Powerful Infrastructure: Make sure to invest in infrastructure that can easily grow as AI projects move from pilot stages to full-scale implementations. Moving workloads to the cloud allows for effective data processing and storage while also enhancing business continuity and decreasing the costs linked with current management methods.
4. Encourage the Development of Data Literacy: Cultivate a data literacy culture across all levels of the organization. Offer ongoing learning opportunities for employees to effectively grasp and use data in their positions, while also making data accessible throughout organizations to speed up decision-making and gain insights.