Data centricity is key to future business strategies

Sayantan Choudhury

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 the world.

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 from the majority of organisations, but it will enable them to outperform competitors who have not yet transformed their business model.

So what is 'data centricity'? Data centricity is the use of data as a shared asset to create intelligence and insight for customers and stakeholders, and to continuously improve decision-making, processes, products, and services.

According to the 2022 EY Tech Horizon survey, only 16 per cent of organisations say that they are data-centric today, although that percentage appears to be increasing.

Regulators across several jurisdictions now expect financial institutions to share increasingly granular data faster than ever and at a greater frequency.

Financial institutions are also faced with data privacy regulations. For example, the Vietnamese government issued Decree 13/2023/ND-CP on personal data protection.

The data-centric organisation 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.

The EY’s bi-annual Tech Horizon survey provides critical answers and actions to help CIOs reframe the future of their organisations.

The last study indicates that successful companies are making the leap to create data-centric organisations to improve every decision, process and interaction. It learns as it goes. It anticipates and collaborates. It out thinks the competition.

The EY Tech Horizon analysis reveals that, data and analytics are the second-highest area of tech investment within the region's financial institutions, behind blockchain, and investment has been growing since 2020.

Data that learns and evolves

For many companies, data exists as isolated bits of information. Only a fraction of structured data is used, even less with regard to unstructured data.

That which is used – quarterly financials and monthly sales reports – is often out-of-date. Silos 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 generated from data because all too often they see conflicting data due to poor governance and management.

With the empowerment of 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 and using internal and external sources. This isn’t just about making reports more up-to-date. The low latency of 5G systems and the Internet of Things (IoT) will open the gates to a flood of innovation.

For example, autonomous cars used to be something in science-fiction and remote, technology-assisted surgery was regarded as pure fantasy. Both are now in regular practice.

Like the cloud, AI can serve as a launchpad for new, emerging technologies such as natural language processing, image recognition, and the recommendation and prediction engines used in today’s cutting-edge analytics.

A new generation of data management

While the benefits will be profound, there will also be important challenges, as 99 per cent 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 per cent 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 hyper-convergence and virtualisation of existing infrastructure.

By adopting modern data platforms and progressively decommissioning the old legacy systems, companies can gain a significant cost reduction in their IT infrastructure.

The great accessibility of data centricity – by employees, suppliers, customers and others – also presents a need to build complex security and privacy requirements, cited as the second-greatest challenge (27 per cent of respondents).

The data-centric organisation 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 per cent 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 these challenges, many organisations are finding opportunities in their data-centricity. An increasing number of organisations are developing data strategies that offer opportunities for new revenue-generating operating models, including those who are commercialising their 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 prioritise the democratisation of data – making it more user-friendly and accessible through a wide range of devices.

Also, the data being used needs to be trusted, and to ensure that, the data needs to be managed and governed well. Only trusted data will create insights that can be trusted.

* The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organisation or its member firms.

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