2023 Predictions: Data Trends That Will Dominate Business Agenda in APAC

2023 Predictions: Data Trends That Will Dominate Business Agenda in APAC

Vice President of Asia Pacific and Japan Remus Lim discusses the key business trends that leaders in APAC need to keep an eye on, and how to leverage data to stay the course in the coming year.

In the past year, businesses who doubled down on digital transformation during the pandemic saw their efforts coming to fruition in the form of cost savings and more streamlined data management. Faced with even more pressure to remain resilient and agile amid looming global economic threats, Asia-Pacific (APAC) region businesses are looking to further mobilize emerging technologies such as artificial intelligence (AI) and machine learning that will optimize operational efficiencies and cost savings. 

As more industries mature digitally and widely adopt AI and machine learning technologies, 2023 will be a pivotal year for organizations looking to deploy emerging tech solutions company-wide to fulfill business objectives. Here are three key trends that will likely dominate the priorities of APAC’s business leaders in the coming year.

1- Treating data as a strategic business asset 

Recent years have seen organizations generating unprecedented volumes of data as a by-product of their digitalization activities and increasing digital customer touch points. This is especially so in industries like telecom, retail, healthcare, manufacturing, insurance, and financial services. And with the anticipated deployment of 5G networks across the region, this volume of data will increase significantly.

In APAC, we have observed that organizations are doing (or aiming to do) more with their data, and reduce the time to value. Data contains valuable insights for critical business decision-making, and the most innovative and successful organizations recognize data as a strategic resource that demands its own strategy. How this strategy looks depends on the organization’s unique business needs as one affects the other. There is no one-size-fits-all approach; the strategy must continue evolving with the business’s priorities.

What is certain is that having an enterprise data strategy aligned to the organization’s cloud strategy and business priorities will help the organization drive greater business value by improving operational efficiencies and unlocking new revenue streams. According to findings from Cloudera’s Enterprise Data Maturity research report,  organizations across the globe with data strategies in place for more than a year see an average profit growth of 5.97%. 

With the right tools in place, distilling actionable insights from data to achieve business objectives or unlock new revenue streams is easily achievable for organizations of all sizes across industries, especially with the availability of self-serve functionalities that do not require specialized ops or cloud expertise.

2- Operationalizing adaptive AI systems for quicker business decision-making

With the increase in demand for real-time data processing, streaming, and sharing, which power transformation into data-driven organizations, we anticipate more businesses investing in building adaptive AI systems that can ingest large amounts of data at frequent intervals and adapt to changes and variances quickly.

What will determine the winners from the laggards will hinge on the speed at which predictive analytics can be executed, and the cost-benefit ratio related to these algorithmic paradigms. An organization’s ability to create trust with usable and explainable AI for faster and more flexible decisions will separate the leaders from the pack.

We foresee organizations pivoting focus beyond the algorithm to things like business-ready predictive dashboards, visualizations, and applications that simplify the use of AI systems to reach conclusions. These will help business leaders quickly understand the impact to their business and act with confidence. 

We have been working with APAC organizations to operationalize data analytics and AI solutions to unlock data-driven decision-making and operational efficiency, with them quickly seeing distinct business benefits. For example, Singapore’s United Overseas Bank (UOB) used machine learning to operationalize analytics and provide insights to users across the bank. Through the Cloudera Data Platform, UOB has launched a deposit analytics solution to ensure it can build stable deposits with optimal pricing, and provide consistent and accurate views of deposits. The results are higher revenues, lower risks, and increased productivity for the bank.

3- Continued move to the public cloud and hybrid cloud, optimizing deployments

Public cloud spend and workload volumes continue to accelerate for organizations of all sizes as cloud-first policies and cloud migration remain top of the agenda. However, a significant amount of this spend is wasted as organizations struggle to optimize costs effectively. 

According to Flexera’s 2022 State of the Cloud Report, respondents self-estimated that their organizations wasted 32% of cloud spend in 2021, up from 30% the previous year. As cost optimization remains the top cloud initiative for organizations for the sixth year running, we will likely see organizations opt for more cost-effective strategies to deliver results quickly and efficiently, including:

  • Migrating more workloads to the cloud to free up resources while driving agility 
  • Implementing data and analytics solutions that can manage the end-to-end data life cyclefrom ingesting data from multiple sources to storing, processing, serving, analyzing, and modeling it to drive actionable insights
  • Repatriating some machine learning workflows back on premise, where complex processes are more cost effective, to optimize cloud spend for compliance, governance, and security

This is where leveraging modern data architectures like data lakehouse, data fabric, and data mesh is essential to driving business efficiencies across diverse operations. In addition to managing data on premises and in public or private clouds, these modern data architectures are also intrinsically designed to handle complexities such as security and governance-related issues. They also address the concerns of IT teams in allowing access to organizational data. 

Organizations can consider moving to hybrid data platforms to better manage the entire life cycle of data analytics and machine learning. The platforms must have features of openness and interoperability that allow ease of sharing and enable self-serve functionality, such as the Cloudera Data Platform (CDP), which has a built-in shared data experience (SDX) feature. These features provide businesses with a common metadata, security, and governance model across all their data.

Overall, organizations must take the time to evaluate their overarching business objectives before embracing cloud, edge, and data capabilities. It is crucial to determine the approach and strategies that best fit the unique needs of their business, and determine where these capabilities can benefit the entire organization and not just to solve specific problems.

Find out more about CDP for modern data architectures here.

Remus Lim
Vice President of Asia Pacific and Japan , Cloudera
More by this author


by Alok on

Within Asia Pacific, companies in countries like India & China are already going data-driven. The future definitely belongs to AI, and we already see organizations using data for growth and targeting. The only thing that worries me is whether humans will be able to handle AI as we expect?

Leave a comment

Your email address will not be published. Links are not permitted in comments.