In just a few years’ time, data centricity will drive and predict the most important decisions, processes and interactions of market-leading financial institutions across Asia – Pacific and globally.
Only 16% of organizations say that they are data-centric
To become data-centric, Asia - Pacific financial institutions need to view the value of data as equal to raw materials or sales. This will require significant investment for the majority of organizations, but it will enable them to outperform competitors who have not transformed their business models.
So what is “data centricity”? Data centricity is the use of data as a shared asset to create intelligence and insights for customers and stakeholders, and to continuously improve decisions, processes, products, and services. According to the EY Tech Horizon 2022 survey, only 16% of organizations say that they are data-centric today, although that percentage appears to be increasing.
Regulators across jurisdictions now expect financial institutions to share increasingly granular data, faster and at a greater frequency. Financial institutions are also faced with data privacy regulations. For example, Vietnamese Government issued Decree 13/2023/ND-CP on Personal Data Protection (Decree 13), which took effect on 1 July 2023.
The EY’s bi-annual Tech Horizon survey provides critical answers and actions to help CIOs reframe the future of their organizations. The last study shows that successful companies are leaping upward to create a data-centric organization to improve every decision, process and interaction. It learns as it goes. It anticipates. It collaborates. It outthinks the competition.
The EY Tech Horizon analysis reveals that, data and analytics is the second-highest area of tech investment within the Asia - Pacific financial institutions (behind blockchain) – and investment has been growing since 2020.
The destination: data that learns and evolves
For many companies, data exists as isolated bits of information. Only a fraction of structured data is used, and only a sliver of unstructured data. That which is used – quarterly financials and monthly sales reports – is often out-of-date. Siloes not only block interoperability and integration – they block insight at the enterprise level and create conflicting data. Many firms ask if they can trust the insights they generate from data because all too often they see conflicting data due to poor governance and management.
With the empowerment of artificial intelligence (AI) and machine learning (ML), data will no longer remain static. AI systems, combined with ML, are transforming data so that it will learn, cleanse itself, and pull in additional data as customers and market conditions change.
Data will move from static to real-time across a vast array of devices, internal sources and external sources. This isn’t just making reports more current. The low latency of 5G systems and IoT will open the gates to a flood of innovations. As examples, autonomous cars used to be something in a science fiction movie and remote, technology assisted surgery was thought of as mind bending. Both are now in practice.
Like cloud, AI serves as a launchpad for innovative new offerings – emerging technologies such as natural language processing, image recognition, and the recommendation and prediction engines used in today’s cutting-edge analytics.
The challenge: a new generation of data management
While the benefits will be profound, there will also be important challenges. 99% of companies report a significant data and technology barrier to executing their transformation. The executives surveyed cited the high cost of technology as the number one challenge (35% of all tech challenges) to achieving transformation. Cost drivers include the greater scale of data, the need for higher computational power, and increased consumption commitments to cloud service providers.
While costs are rising, they are also being mitigated by greater efficiencies in the hyperconvergence and virtualization of existing infrastructure. By adopting modern data platforms and progressively decommissioning the old legacy systems, companies gain a significant cost reduction in their IT infrastructure.
The great accessibility of data centricity – by employees, suppliers, customers and others – also presents the challenge of building complex security and privacy requirements, cited as the second greatest challenge (27% of respondents). The data-centric organization must not only deepen its cyber-security offensive and defensive measures, but it must also spread the net to cover a diverse set of players.
A key operational challenge of data centricity is the complexity of connecting and integrating diverse data systems (the third greatest challenge, cited by 25% of respondents) – a key contributor to the cost of technology.
It goes beyond simple cost metrics. To develop true data centricity, it is necessary to aggregate and curate data from thousands of enterprise information systems, suppliers, customers, markets, and regulators, as well as internal control systems, IoT devices and sensor networks.
Apart from challenges, many organizations find opportunities in their data-centricity. An increasing number of organizations are developing data strategies that offer opportunities for new revenue-generating operating models including those where they are commercializing their data.
One of the emerging opportunities in this area are those where data ecosystems are created across organizations to sell curated data sets and models or insights that have been trained using an ecosystem of data.
In conclusion, data is only valuable when it is transformed into insights and drives informed decision-making. Given the expanded user base of data, data strategists should place a strong priority on democratizing data – making it more user-friendly and accessible through a wide range of devices or through citizen developers. Also, the data being used needs to be trusted, and to ensure trusted data, it needs to be managed and governed well. It is only trusted data that will lead to insights that can be trusted.
Note to readers:
The article was first published in Vietnam Investment Review on 22 November 2023
