In recent years, data has been at the front and center for enterprises seeking to stay relevant, sustainable and emerge as industry leaders. The democratisation of data has further led to the availability, demand and use of data at an unprecedented pace. This phenomenon, however, has surfaced challenges and opportunities for enterprises. The rise of generative AI such as ChatGPT has accelerated this further. Forward thinking enterprises recognise the opportunities and are looking at ways to turn generative AI into productive data for their industries.
The current landscape underscores the belief that data is the lifeblood for enterprises to grow their business in a sustainable way. Becoming more sustainable is an opportunity to innovate, make a difference and scale. While top performing enterprises are data driven, research found that data veracity is still a challenge with up to 68% of data not getting analysed and data silos persists at 82% of these organisations. A joint study by IBM and Morning Consult found that businesses are drawing from more than 20 different data sources - such as databases, data warehouses and data lakes – with some up to a whopping 500 data sources.
These issues are only intensified by the complexities of the platforms that enterprises have parked their data on. Enterprises aren’t just dealing with data spread all over their company, it’s also being stored and managed across a variety of places - public clouds, private clouds, on-premises and data centers. The average enterprise has five different environments, cloud-wise, turning the data challenge into a hybrid cloud problem as cloud spend continues to rise in Thailand (95%), Indonesia (94%), Philippines (91%) and Singapore (83%) while Malaysia has committed to migrate 80% of government data to the cloud.
Why data fabric
Clearly a robust strategy is needed to manage the complexity of data to harvest useful insights within heterogeneous environments. How can enterprises simplify access and governance of data quality, regardless of where it resides? There are industry leaders who have adopted a data fabric architecture to improve “findability” of data.
ING and Citigroup are great examples enterprises that have maximised the data fabric architecture for business sustainability. ING put in place a set governing and data quality rules, defined business taxonomy, access rights, privacy, and protection across its data stores regardless of the platform where it resides help its team make informed decisions. Citigroup introduced machine learning and AI into the full audit lifecycle to find anomalies within business monitoring, then planned and scoped audits more effectively using all the fresh findings. IBM collaborated with Citi to build a new audit platform injected with advanced analytics and AI.
Their success presents opportunities for enterprises in ASEAN given that this region had added 70 million new online shoppers during the pandemic with 80% buying goods and services through mobile and web platforms. Indonesia, for example, will account for 50% of South-East Asia’s e-commerce market by 2025, followed by Malaysia, Philippines, Thailand and Vietnam.
The prolific increase in online shopping increased the variety of new information such as shopping habits and interests to the already burgeoning data velocity that is already in the quintillions, prompting the need for a data fabric architecture to support the need to obtain timely and meaningful insights to make informed decisions. A survey of 500 ASEAN leaders found that they will use data and AI for customer experience (77%), human resources (75%) and marketing (72%) in the next two years. The possibility of mining the same data for trends and other practical purposes to potentially improve societal welfare also exists.
This also makes the case for a data fabric architecture stronger especially with the demand by regulators to ensure data quality, fairness, governance and equitable access to boost secure sharing without compromising personally identifiable information.
Productive data with AI
The first step is usually the hardest as enterprises struggle to make sense of vast data volume and tapping into AI tools had helped reduce manual labour needed to sift and analyse data, remove data duplication and develop a recommendation engine while meeting regulatory compliance.
Removing bottlenecks to data with a data fabric architecture also allowed enterprises to foster more productivity, enable users to make informed decisions and free up valuable time for teams to focus on higher value work. Done right, a data fabric will connect the right people with the right data at the right time. It will eliminate the complexities involved in data movement, data transformation and data integration.
The urgency for right data at the right place and right time is vital with initiatives such as the ASEAN Connectivity Master Plan 2025 and the ASEAN Smart Logistics Network that is spurring infrastructure growth in this region. Technology is at the heart of this wave for enterprises to achieve sustainable growth that is translated into true productivity, efficiency and operational stability. And a well-designed data fabric architecture to manage the influx of data without compromising the integrity is surely the way forward to an agile, resilient and sustainable business enterprise.
(Agnes Heftberger is the General Manager & Technology Leader for IBM Australia, Southeast Asia, New Zealand & Korea (ASEANZK). A practitioner of sustainable growth at work and home, she is more likely to flip back questions to uncover insights instead of trying to explain what the acronym ASEANZK stands for).