In recent years, data has grown exponentially, giving rise to a specific operational domain known as DataOps.
Organisations are increasingly focusing on DataOps to manage and derive value from their ever-expanding data resources.
The Rise of DataOps
Today’s organisations are committed to collecting and analysing as much data as possible. However, success in extracting value from this data varies. The challenge often lies not in the data itself, but in its sources and how it is utilised. This involves the people, processes and technology it encounters along the way.
The explosive growth in data has led to the emergence of a new operational category: DataOps. The effectiveness of DataOps best practices plays a critical role in successful data management and achieving data-driven outcomes.
Defining a Successful DataOps Approach
Defining a successful DataOps approach is inherently tied to what an organisation wants to achieve with its data. This approach must be customised to the organisation’s specific circumstances and targets. Various factors contribute to data-driven business success, such as the size of the business, its geographical location, data management maturity, and the implementation of DataOps best practices.
Other crucial elements include the operating model for data management, whether centralised, federated, or hybrid, and the use of technologies like artificial intelligence (AI) and machine learning (ML) in data management.
Maturity Profiles in DataOps
Organisations can be classified into four maturity profiles based on their data management strategy, practices, and architecture:
Developing: Strategy is emerging but not yet closely aligned with critical business outcomes.
Functional: Strategy is mostly developed, with some data practices linked to critical business outcomes.
Proficient: Strategy is well-established, with nearly all data practices driving business outcomes.
Exceptional: Strategy is continuously optimised, with data practices creating novel value.
Data Governance Considerations
European companies face an increasing array of compliance requirements, particularly around customer data protection and personally identifiable information (PII). A significant number of organisations – 65 per cent of survey respondents – are managing data to support corporate governance.
The report indicates that as the regulatory landscape evolves, organisations with developing maturity will need to allocate resources towards meeting external requirements while also focusing on generating business value from their data. Mishandling customer data can lead to severe repercussions, including financial penalties and reputational harm.
DataOps and Regulations
European organisations are at the forefront of adopting active data management practices utilising emerging technologies. These organisations are also subject to stringent data-related compliance and AI requirements. The Digital Operational Resilience Act (DORA), effective 17 January 2025, aims to bolster the operational resilience of digital systems within the financial sector.
DORA mandates that shared information must adhere to relevant guidelines, and PII must comply with the EU’s General Data Protection Regulation (GDPR). Establishing robust DataOps practices allows EU organisations to stay ahead in compliance readiness.
Data Compliance Considerations
DORA outlines five critical considerations for organisations working towards compliance, all of which are pertinent to data management:
Identification: Recognising risks to systems, individuals, assets, data, and capabilities, including business context and vulnerabilities.
Protection: Implementing safeguards to limit or contain the impact of potential cybersecurity incidents. Ensuring the integrity and security of critical data and systems.
Detection: Identifying threats, cybersecurity incidents, and anomalies in real-time to mitigate potential impacts swiftly.
Response: Executing actions to limit the effects of threats and incidents through well-defined response protocols.
Recovery: Ensuring systems can efficiently return to normal conditions.
Data is a crucial business driver, and mastering its management through effective people, processes and technology is imperative.
Organisations must understand their data maturity and continuously evolve their DataOps practices to remain competitive.
