Since August last year, the Association of Southeast Asian Nations’ (ASEAN) decision to accelerate the region’s digital transformation has boosted innovation and the adoption of new technologies. This allowed startups to form at a high rate to meet the budding demand for disruptive solutions in society and the marketplace. Investors are also able to search for opportunities to fund these new companies. Big data and analytics played a significant role thus far by enabling startup founders to make informed decisions for their businesses, create better strategies, and find solutions for customer problems.
Data is essential to Southeast Asia’s startup scene because it enables companies to measure the effectiveness of their strategies, delivering targeted solutions to improve their customers’ lives. Managers use data to monitor the quality of their products or services, taking a proactive rather than reactive approach to maintaining high standards. Moreover, data analysis helps track company benchmarks, taking note of the best performing strategies to better direct funding and resources where they are most needed.
Is it bad data or bad design causing AI projects to fall short of expectations?
Despite this, a Harvard Business Review (HBR) study showed that only 3% of company data meets basic quality standards. Many significant decisions depend on having the correct information, and the current AI and machine learning trends in Southeast Asia highlight the promise that these technologies could hold the key to improving data quality in the region.
Here are the top five data trends to expect in 2022.
Public cloud services will grow
According to research from Gartner, Inc., global end-user spending on public cloud services is likely to grow beyond $480 billion USD this year. The impact of the COVID-19 pandemic is fueling greater innovation and cloud service usage, with four new trends boosting cloud technology: global cloud adoption, the emergence of regional cloud ecosystems, focus on sustainability, and the development of cloud infrastructure and platform service (CIPS) providers’ automated programmable infrastructure.
Senior principal analyst at Gartner, Brandon Medford, pointed out that the organisational shift to the cloud is modernising business operations, improving system reliability, and supporting the novel hybrid models of working brought about by the pandemic. Moreover, many companies choose cloud service providers with sustainability as a business model.
Regional cloud ecosystems are emerging because of the varying regulatory frameworks and policies in different countries. Some leaders pursue protectionist policies, limiting startups’ ability to team up with cloud service providers outside their own nations.
There will be a rise in convergence of data warehouses
Data warehouses, which store, manage and analyse data, will converge with data lakes–large, centralised data repositories. In a report from venture capital firm a16z, some experts believe the two terms will become interchangeable as technology is simplified and the actions and results of both sides start to resemble one another.
Since data warehouses produce insights and data lakes enhance business operations, their convergence will create the blueprints for a modern data infrastructure.
Data lineage will become even more important
The term “data lineage” means tracing the flow of data from its origins to the end of the cycle, helping to understand any changes that occurred to it. This process enables companies to track errors, thereby validating the collected data’s quality.
When done correctly, tracking data lineage ensures startups have the correct information to make better decisions.
Increased democratisation of data
Sharing data at every level of an organisation or industry and removing gatekeepers allows decision-makers to access information, empowering them to take necessary business actions instantly. Democratising the collected data enables non-technical employees to visualise or understand the data, meaning they can analyse company insights. Faster decisions make teams more agile, making businesses more competitive than their industry rivals.
Nevertheless, research from the International Data Group (IDG) shows that 93% of data will be unstructured, making it essential for organisations to use analytics and AI tools to understand the irregular data and choose the right strategies for their companies’ future.
Quality and validation of data will continue to be a challenge
According to Gartner’s Data Quality Market survey 2017, poor data quality costs companies $15 million USD a year. As the research firm also said last year, data and analytics have become a core business function that can accelerate digital initiatives. Therefore, companies must set up quality data testing systems and try to streamline their data-gathering processes.
Although big data and analytics are vital for effective business operations, the quality of the data has to be at a high level to have the right impact. The data trends discussed here point toward advancement in multiple sectors and more significant startup growth and innovation opportunities. AI and machine learning trends in Southeast Asia suggest that the region can add up to $1 trillion USD to their Gross Domestic Product (GDP) if they invest in and nurture these technologies, making such investment genuinely worthwhile.