As organisations continue to grapple with data volumes, in my previous article, I focused on the concept of data modernisation, with reference to the immense value it can bring to a business.
In the piece, I share my views on key steps to focus on when developing and implementing a data modernisation strategy.
To close the loop on the topic, in this follow-up article of my two-part series, I address the challenges of legacy system modernisation, the risk of not modernising data infrastructure and take a look at data modernisation in the cloud.
Let’s jump straight in…
Challenges of legacy system modernisation
There are, of course, always some reservations within a business when it comes to making changes or modifications to technology and systems. In my experience, when it comes to a data modernisation strategy, the following challenges may exist:
Data modernisation is an enterprise-wide initiative that requires buy-in from and involvement of all relevant stakeholders for it to be successful. However, change is hard, and people tend to resist it, or at least put if off as much as possible.
It can be even harder if the change involves replacing an entrenched system that business has been using for many years − which is often the case with data modernisation.
The high cost in the long-term can be mitigated by a stepwise approach instead of a risky big-bang approach.
There can also be internal politics at play, where the IT personnel supporting the legacy system do not co-operate, as they view the system changes as a threat to their job security.
Lack of in-house expertise
Organisations may lack internal expertise to be able to successfully implement a data modernisation programme. In this case, a credible external partner can be brought in to provide help with the implementation.
The data modernisation process often takes time to fully realise, and management needs to be aware of this fact and provide the project team with the necessary support and resources to allow for the process to be successful.
Oftentimes, organisations delay transformation projects due to the cost involved. They would rather prolong the lifecycle of legacy systems − usually at high costs – instead of biting the bullet and investing in new infrastructure.
The high cost in the long-term, however, can be mitigated by a stepwise approach instead of a risky big-bang approach.
Risks of not modernising data infrastructure
The problems organisations face today require the latest fit-for-purpose tools designed with the current data challenges in mind.
Without a modern data platform, organisations are unable to take advantage of new services like artificial intelligence and big data analytics that can give them a competitive-edge.
They may also find it difficult to meet compliance requirements, especially those relating to data privacy and data retention.
The risks include:
Data access: A modernised data estate tends to be more business focused, which means the right data access is given to the right people to do their jobs. Legacy systems, on the other hand, tend to be more IT focused, where business users are required to log calls for IT to extract data on their behalf, or give access to a dashboard and the entire process can take too long.
Storage issues: In a legacy environment, implementing data retention policy remains a challenge due to limited storage capacity. When database administrators are faced with the risk of a system crash, they often resort to deleting data (usually historical), which they deem no longer required, to avert potential disaster. However, this could render the business non-compliant, which may attract hefty penalties from the regulatory bodies.
Integration issues: In most cases, legacy systems are not compatible with modern platforms. It means data from these systems remains isolated. Integrating legacy systems with new platforms may require extensive custom coding, which could be too costly and time-consuming.
Customer experience: Legacy systems tend to be inflexible, and this may hinder an organisation’s ability to modernise and improve. Customers demand a better user experience, which is usually an outcome of a digitised organisation, and this cannot be achieved without modernising the underlying infrastructure.
Difficulty or inability to process large datasets: Traditional data platforms do not have built-in capabilities to process large datasets. This makes it difficult for the organisation to explore the full extent of its data estate to derive insight.
Data modernisation and the cloud
The discussion about data modernisation is not complete without mentioning the cloud. In fact, in recent times, data modernisation has become synonymous with cloud data migration. Data modernisation in the cloud can be achieved with a ‘re-architect’ cloud adoption strategy.
Re-architect, also referred to as ‘modernisation with migration’, allows organisations to take advantage of cloud-native capabilities by mapping legacy application to their cloud equivalent.
This also gives organisations an opportunity to re-design their architectures so that they are optimised for the cloud. During this time, applications and data that is no longer required can be decommissioned, thus ensuring only the required workloads are migrated to the cloud.
Big data analytics platforms in the cloud give organisations the ability to extract more value from data, irrespective of its size or format. Cloud architecture allows organisations to leverage important cloud features, like on-demand self-service, elasticity and scalability, better collaboration, data pipeline automation and advanced tooling.
To conclude, data modernisation affects the entire enterprise and has a potential to change the way employees and customers use and interact with data.
As data infrastructure evolves, there is a need to constantly upskill the workforce to empower them to take full advantage of the new data platform.
Efficient use of the modernised platform can help unlock business value by improving existing processes and potentially pivot new products and services.