Frequently touted as the next center of global growth, the APAC region has one fuel to realize its fullest potential.
Faced with fast-changing market conditions, organizations need the ability to quickly decide and reshape the business according to insights, in order to adapt to changes promptly and retain competitiveness. But much needs to be done to achieve this capability.
According to a Harvard Business Review Analytic Services study commissioned by Cloudera, only 35% of organizations around the globe are confident that their analytics and data management capabilities are on course to achieve business objectives over the next three years. Most of them still struggle with limited data visibility and insufficient insights because their data is spread across multiple environments and their analytic workloads are running in silos.
Given these challenges, what should organizations in Asia Pacific do to accelerate their data-driven journey, especially as we head into uncertain times during and after the coronavirus pandemic?
Make AI boring
Gartner predicts that by 2022, more than half of major new business systems are expected to incorporate continuous intelligence that uses real-time context data to improve decisions. Continuous intelligence integrates real-time analytics—including streaming analytics, machine learning, and AI—within a business operation, processing current and historical data to prescribe actions in response to events.
However, getting analytics, especially machine learning and AI, into production or embedded in the entire organization can be challenging. Since businesses may be running on an inflexible, siloed IT infrastructure, it will be difficult for machine learning systems to access the necessary data, and scale as needed.
One way of overcoming this issue is by building an AI factory. Built on a platform that unifies and powers all machine learning workflows across multiple clouds and on-premise environments, an AI factory enables the process of building, scaling, and deploying enterprise machine learning applications to be automated, repeatable and predictable (that is, ‘boring’).
As this AI factory eliminates the complexity of deploying machine learning apps, organizations can easily operationalize and scale machine learning capabilities across the enterprise, which ultimately empowers the entire company to make data-driven decisions.
Tap on APIs to share data fast
As applications become heavily reliant on data, it needs to be drawn from diverse sources to ensure the effectiveness of those applications. Financial institutions in the Asia Pacific region have recognized this, with a significant number of them already sharing their data with third parties—such as fintechs or companies from other sectors—using application programming interfaces (APIs).
Since this move enables them to stimulate collaborative development of new personalized customer services through new applications, it ultimately opens doors to new opportunities and revenues.
The rise of open data collaboration will also encourage businesses across industries to move towards becoming a one-stop center to deliver more convenience to customers. This calls for them to move away from the traditional value chain and rethink the way they develop and deliver their products.
In order to run on “as a service” focused business model, organizations will need to create their own communities and ecosystems to deliver an all-encompassing service and drive growth in customer-lifetime value.
This is exemplified in the evolution of how people buy cars. Traditionally, consumers will head to a dealership or online website to select a car they desire before purchasing it. However, they will also need to pay for additional costs such as car insurance and petrol.
In the near future, consumers will only pay a monthly fee to use a car as they need. The fee will include miscellaneous costs as the car dealer will work with their partners—such as automotive insurance and repair shops—to provide comprehensive offerings to consumers.
Build a unified data platform
In most organizations, business-critical data can be found in multiple locations. For example, they may retain sensitive records that fall under the compliance scope on-premises, while running proof-of-concept applications using anonymized datasets in the public cloud. This poses two major challenges: how to bring together the best data resources with the finest analytic tools when they are spread across multiple environments; and how to keep track of who is doing what with the data.
Having an open platform that can effectively support a hybrid, multi-cloud environment can help overcome those challenges. Such a platform will help organizations maximize their cloud potential by preventing cloud lock-in effects and future cloud concentration risk exposures, while allowing workloads to run in different environments with security and data governance applied consistently across all platforms. This not only reduces operational risks but also provides a secure framework to manage their workloads across their hybrid, multi-cloud environment.
All in all, data is the new oil for businesses in Asia Pacific, but only for those that have an insights-centered strategy that embeds analytics capabilities across the organization.
This can be achieved by adopting an enterprise data cloud that provides the ability to:
- take advantage of the best infrastructure options for agility and elasticity across multiple cloud environments
- process data from multiple locations
- ensure data is always available to where it is needed
- and apply a range of analytics on the same dataset with consistent security and governance policies
By empowering every team with the data and resources to develop the best intelligence for their business, organizations will be able to improve responsiveness to changing market dynamics and minimize the impacts of disruption—to survive and thrive today and tomorrow.
