10 Reasons for Cloud-native Platform and Data Transformation RoI Failure

Organisations increasingly use cloud-native platforms for their data transformation initiatives in today's rapidly evolving digital landscape. However, despite the promise of agility, scalability, and cost-effectiveness, many of these organisations need help in realising the full potential of their data transformation efforts. Numerous hurdles can impede progress, from a lack of data understanding and buy-in from executives and appropriate funding to issues with master data management (MDM) and infrastructure timelines.

In this blog, we delve into these challenges and provide some actionable insights on how organisations can overcome them to drive successful data transformations in cloud-native environments, explicitly focusing on operations and finance functional areas.

1. Lack of Data Understanding

A comprehensive understanding of data assets is fundamental to any successful data transformation initiative. With it, organisations can make misguided decisions and investments that can derail their transformation journey. Data understanding involves knowing what data is available, its quality, relevance, how to model it for optimal value creation, and potential impact on business outcomes. To overcome this challenge, organisations must invest in robust data governance and control frameworks that define clear roles and responsibilities for data management, establish data quality standards, and ensure compliance with regulatory requirements. Additionally, continuous training and education programs can help enhance data literacy among employees, enabling them to make more informed decisions and extract actionable insights from data.

2. Master Data Management (MDM) Issues

Poor master data management can lead to data inconsistencies, duplication, and errors, undermining the integrity and reliability of analytical insights.

MDM involves the processes, governance, policies, standards, and tools that consistently define and manage an organisation's critical data to provide a single point of reference. Organisations can overcome MDM challenges by implementing robust MDM strategies and technologies , establishing clear data ownership roles, and enforcing strict data quality standards. By centralising master data repositories, implementing data validation rules, and conducting regular audits, organisations can ensure the accuracy and reliability of their data assets, thereby enhancing decision-making and driving business value.

3. Reference Data Management (RDM) Complexity

Reference data management (RDM) is essential for ensuring the accuracy and consistency of data used across different systems and applications within an organisation. In fact, without appropriate reference data, no solution can be scalable or add value to the business in envisaged ways. However, managing reference data can be complex, especially in cloud-native environments where data volumes and sources constantly increase. To overcome RDM challenges, organisations should leverage automation tools for reference data management, establish centralised repositories for reference data, and ensure continuous monitoring and validation of data sources. By implementing robust RDM processes and technologies, organisations can streamline data integration, improve data quality, and enhance the reliability of analytical insights.

4. Engineering Challenges in Cloud-Native Environments

Cloud-native environments offer numerous advantages, including scalability, flexibility, and cost-effectiveness. However, they also present unique engineering challenges that can hinder data transformation initiatives, given the number of applications that are required to orchestrate a solution that can be scalable. Inefficient engineering practices can lead to costly rework, performance bottlenecks, and deployment delays, undermining the agility and effectiveness of data initiatives. To overcome engineering challenges, organisations should embrace DevOps methodologies (DevSecOps Engineering workbench ), automate testing and deployment processes, and leverage cloud-native services for scalability and reliability. By adopting a culture of continuous integration and delivery (CI/CD) and implementing robust monitoring and logging mechanisms, organisations can accelerate the development and deployment of data applications, driving faster time-to-market and improved business outcomes.

5. Infrastructure Timelines Required to Enable Tools

The setup and configuration of infrastructure play a crucial role in enabling data transformation initiatives. However, traditional infrastructure provisioning processes can be time-consuming and resource-intensive, leading to delays in project timelines and increased costs. Organisations should embrace Infrastructure as Code (IaC) principles to overcome infrastructure challenges, which involve defining infrastructure configurations in code and automating the provisioning process. Additionally, organisations can leverage serverless architectures and managed services provided by cloud providers to reduce infrastructure management overhead and enable faster time-to-value for data initiatives. Organisations can enhance agility, scalability, and cost-effectiveness by adopting a cloud-native approach to infrastructure provisioning, accelerating their data transformation journey.

6. Siloed Data and Functional Areas

Siloed data and functional areas can hinder collaboration and integration across different parts of the organisation, leading to fragmented insights and missed opportunities. To overcome siloed data challenges, organisations should foster a culture of data sharing and collaboration, break down organisational silos through integrated data platforms, and establish cross-functional teams to drive alignment and coordination. By providing access to centralised data repositories, implementing data governance frameworks, and promoting knowledge sharing and collaboration, organisations can break down silos and enable seamless integration and analysis of data across different functional areas.

7. Legacy Systems Integration

Legacy systems pose compatibility issues and data migration challenges, hindering modernisation. Organisations should implement robust integration frameworks to overcome legacy systems integration challenges, gradually migrate data and functionality to cloud-native solutions and prioritise interoperability and API-based communication. By adopting a phased approach to legacy modernisation and leveraging technologies such as microservices and containerisation, organisations can minimise disruption to existing operations while realising the benefits of cloud-native architectures.

8. Security and Compliance Concerns

Data breaches and regulatory non-compliance can result in reputational damage, legal liabilities, and financial penalties. Organisations should implement robust security measures such as encryption and access controls, adhere to industry-specific compliance standards, and regularly audit and monitor data access and usage to overcome security and compliance challenges. By adopting a proactive approach to data security and compliance, organisations can mitigate risks and build trust with customers, partners, and regulators, enabling them to unlock the full potential of their data assets while maintaining confidentiality, integrity, and availability.

9. Talent Shortage and Skill Gaps

A lack of skilled resources can impede the implementation and maintenance of complex data transformation initiatives. Organisations should invest in employee training and development programs, leverage external expertise through partnerships and consultancy services, and cultivate a culture of continuous learning and knowledge sharing to overcome talent shortages and skill gaps. By empowering employees with the skills and knowledge needed to succeed in a cloud-native environment, organisations can build high-performing teams capable of driving innovation, delivering business value through data-driven insights, and maximising the investments made.

10. Resistance to Change

Resistance to change can stall progress and prevent organisations from realising the full benefits of their data transformation efforts. To overcome resistance to change, organisations should prioritise change management initiatives, communicate the benefits and rationale behind data transformation initiatives, involve stakeholders in the decision-making process, and celebrate successes to foster a positive transformational culture. By addressing concerns and fears and providing support and resources to employees, organisations can create a supportive environment that encourages experimentation, innovation, and continuous improvement, driving successful data transformation in cloud-native environments.

In conclusion, successful data transformation in cloud-native environments requires organisations to address various challenges across data understanding, governance, engineering, and cultural aspects with appropriate sponsorship from the executive teams. By recognising the importance of these challenges and implementing proactive strategies to overcome them, businesses can unlock the full potential of their data assets, drive innovation, and gain a competitive edge in today's digital economy.