What does your organisation do with the flood of data that comes in diverse formats and sources – both structured and unstructured?
How do you integrate data, both old and new, while ensuring its quality and compliance with regulations?
If your business is constantly struggling with the overload, fragmentation, and complexity of data, should you completely restart and rethink your data management strategies?
Ultimately, wrongly managed and inaccurate data can lead to decisions that result in financial losses, customer dissatisfaction, and wasted resources.
Delving into these key challenges and questions that data and IT decision makers face, iTNews Asia sits with Dhiraj Goklani, Splunk’s Area Vice President, South Asia, to find out why so many companies in APAC are still struggling in their data management, where they are falling short, missteps they commonly make; and how they should modernise and become more data-astute by building a data-first culture.
iTNews Asia: Your recent global data management study reveal that a large number of organisations are still struggling to manage the surging volumes of data and their lack of visibility. This makes it difficult to create value from their data and make informed decisions. Why is this so?
Goklani: Many organisations struggle to manage their surging data volumes because data management is still often reactive — addressed only when something breaks.
Our global data management report found that despite 64 per cent of organisations managing over 1 Petabyte of data, many still lack a unified approach that brings structure, governance, and actionability to their data.
This is happening because much of the problem stems from fragmented infrastructure and siloed systems. Their data is scattered across teams, tools, and formats, making it hard to centralise or analyse in real time.
Many organisations also lack modern observability or governance frameworks, so even when insights exist, they’re delayed, incomplete, or out of context.
In short, the scale of data has outpaced the maturity of data strategies. Solving this isn't just about collecting more — it's about building the right architecture, accountability, and tooling to unlock its full potential.
iTNews Asia: Compared to the US and Europe, is this data challenge even more pervasive or acute amongst organisations in the Asia Pacific where the digital maturity, awareness and preparedness vary widely between countries and industries?
Goklani: The challenge is arguably more pronounced in Asia Pacific. While data volumes surge across the board, we found that only about two-thirds of APAC firms have a formal data strategy, and about 40 percent of regional data leaders cite data quality and trust issues as major barriers.
Take Singapore for example. On one hand, it leads the region in advanced practices, with more than half of SOCs in our report having adopted detection as code, significantly above the global average. These organisations have seen tangible gains, including a boost in SOC productivity. Yet, even in a mature market like Singapore, organisations still struggle with significant challenges stemming from dispersed tools. This underscores the uneven pace of digital maturity across the region.
Our study reveals that the majority of APAC organisations plan to increase public cloud storage in 2024. This move will add more complexity if it is not paired with robust governance. It also reflects a wider problem — many APAC organisations are scaling fast, but their data architectures remain fragmented.
As a result, real-time insights are delayed, context is lost, and decision-making suffers — not due to a lack of data, but lack of data readiness.
iTNews Asia: Given the challenges, how should organisations strike a balance between the abundance and utility of data? What about data that is old, legacy and unstructured? What should be the considerations when deciding whether to discard, retain, improve and reuse them?
Goklani: Abundance doesn’t equal advantage — and that’s where many organisations get stuck. Just because you can store massive volumes of data doesn’t mean it’s useful. The key is to treat data with the same discipline as any other business asset: assess its quality, relevance, and cost to maintain.
Old, unstructured, or legacy data shouldn’t be dismissed outright. Historical data often holds valuable signals but only if it's accessible and contextualised. Start by categorising data based on business value, compliance requirements, and access frequency.
If data is redundant, obsolete, or low trust, it may be time to archive or discard. But if it supports analytics, AI models, or compliance, then invest in cleaning, enriching, or reformatting it.
Ultimately, the goal is to create a lifecycle strategy — not just a storage strategy — that ensures data remains both compliant and valuable over time.
iTNews Asia: Are many businesses still relying on outdated data management practices, which impact their operations and growth from inefficiencies and lost opportunities? How urgently must they change their data strategies?
Goklani: Yes, many businesses still rely on outdated, reactive data management practices, treating data as a byproduct rather than a strategic asset. For example, we found that almost half or of organisations still move data monthly, exposing them to security risks, compliance failures, and operational disruptions.
In contrast, SOCs in Singapore that have adopted a unified platform report faster incident response and fewer tool maintenance issues. This shows that modernising data practices directly translates into improved agility and competitiveness.
The urgency to modernise is high. Organisations that don’t adopt real-time observability and integrated data platforms risk falling behind in an increasingly data-driven world. In fact, seven out of 10 organisations currently face poor decision-making due to mismanaged data, and many saw a direct impact on their competitive standing.
To stay competitive, businesses need to shift from simply collecting data to actively managing it, leveraging AI and automation to gain real-time insights, reduce costs, and improve decision-making. This shift isn’t just about technology; it’s about building a data-first culture.
iTNews Asia: Data can be complex to manage and governance hard to achieve. How can an organisation better ascertain the accuracy, integrity and accessibility of their data?
Goklani: Organisations should start with establishing a strong foundation in governance and data quality practices. They must first create clear data policies and standardisation frameworks to ensure data consistency across departments and systems. This includes setting up regular data audits and employing automated data validation tools to identify and rectify errors early on.
Additionally, real-time data observability is key. Implementing a unified platform that centralises data from different sources can ensure that the right stakeholders have the information they need in real time.
Lastly, empowering data stewards or governance teams who are responsible for maintaining data standards and overseeing data usage ensures the integrity and accessibility of data, driving more informed decisions across the organisation.
iTNews Asia: How often does compliance failure result from improper data management, or lack of? How can we avoid them?
Goklani: It’s more common than many realise. Difficulties with data management directly resulted in compliance failures. This highlights a critical gap — when organisations struggle to manage and govern their data effectively, they open themselves up to legal and regulatory risks, especially in industries with stringent data requirements.
One way to mitigate this risk is by adopting a data federation strategy. Instead of centralising all data, data federation enables organisations to create a unified view across multiple data sources. This enhances visibility, access control, and consistency without the need to physically move the data. It also supports compliance efforts by allowing governance policies to be applied in real time across systems.
With federated access, businesses can monitor, audit, and enforce compliance standards more effectively, reducing the likelihood of regulatory breaches while maintaining the agility needed to adapt to evolving requirements.
iTNews Asia: Amongst APAC businesses, we hear of many data governance initiatives failing. What would you attribute as the most major or common causes for the failures and why - is it mostly people-related, because of the lack of ownership/advocates within the businesses; or lack of understanding, often leading to misaligned initiatives; or just that many organisations not optimally structured (or lack the infrastructure) to enable proper data governance?
Goklani:
Data governance initiatives in APAC often fail not because of a single issue, but due to a combination of structural, cultural, and capability-related gaps. A common reason is the lack of clear ownership. When data governance is seen as ‘just IT’s job,’ there’s often no strong business advocate to drive alignment, accountability, or adoption across departments.
- Dhiraj Goklani, Area Vice President, South Asia, Splunk
Another frequent barrier is misalignment: many businesses launch governance programs without fully understanding the data’s lifecycle, usage, or value across the organisation. This results in frameworks that are either too rigid, too vague, or disconnected from day-to-day operations.
Infrastructure also plays a role. Especially in markets with wide digital maturity gaps, legacy systems and fragmented tools make it difficult to implement scalable governance practices. Successful governance isn’t just about policies — it’s about people, platforms, and shared purpose working in sync.
iTNews Asia: Do you think companies in this region are more hesitant to invest in governance as they find it difficult to quantify or attribute its value to the business? How can we overcome this perception and challenge? What advice can Splunk give to businesses here?
Goklani: Many APAC organisations are still hesitant to invest in data governance, often because its value isn’t always immediately visible on a balance sheet. Unlike revenue-generating tools or cost-saving technologies, governance is often seen as an operational overhead—until something goes wrong.
To change this mindset, organisations need to stop viewing governance as a checkbox for compliance and start linking it to outcomes like faster innovation and resilience. Take Singapore Airlines, for example. By working with Splunk to unify data across its operations, the airline was able to significantly reduce its mean time to detect (MTTD) and mean time to resolve (MTTR) incidents. This not only enhanced security and operational continuity but also supported faster, more informed decision-making when it mattered most.
To overcome the perception challenge, businesses should start tracking governance through tangible outcomes: fewer security incidents, faster reporting cycles, reduced downtime, and improved decision velocity.
Our advice is simple: governance should be embedded into your core data strategy, not treated as a compliance exercise. When integrated effectively, it enables consistent, trusted access to data that powers both everyday operations and long-term transformation.
iTNews Asia: Increasingly, the digital transformation narrative is rewritten by data and AI implementation. How are they intertwined today and why does AI and data management need to go together?
Goklani: Data and AI are inseparable — AI is only as powerful as the data that fuels it. As organisations race to integrate AI into their digital transformation strategies, many overlook the foundational requirement: clean, well-governed, accessible data. Without that, AI outputs are unreliable at best, and harmful at worst.
Our research shows that organisations with mature data strategies are not just better at managing data; they’re significantly more confident in their AI initiatives. In contrast, those still dealing with fragmented infrastructure or poor observability struggle to operationalise AI meaningfully.
Think of it this way: data is the raw material, AI is the machine, and governance is the operator ensuring the machine works safely and efficiently. Without trust in your data’s accuracy, timeliness, or context, AI can’t drive insights or action.
In APAC, where digital maturity varies widely, aligning AI ambitions with solid data management practices isn’t just best practice — it’s the only way to unlock real value.
iTNews Asia: Given this, what strategies should businesses look at to best manage security, regulatory and compliance collectively? How are they changing from the past?
Goklani: Organisations must move from reactive security postures to proactive and unified strategies. They must go beyond compliance checklists to continuous risk monitoring, powered by real-time data visibility, by adopting integrated platforms that provide observability across the entire digital estate — data, infrastructure, AI systems, and user behaviour.
Security and compliance teams also need to collaborate more closely with data and IT leaders. AI models themselves should be auditable and explainable, and data pipelines must be built with security controls and access policies from the start, not bolted on later.
In this new era, resilience comes from convergence: shared accountability, unified tooling, and governance frameworks that span all three pillars — data, AI, and security.
