What are some of the key challenges facing CIOs in relation to infrastructure modernisation?
- The key challenges in relation to infrastructure modernization can be categorized in 3 areas of Data, Applications and Infrastructure Operations:
- Data – As enterprises move more workloads to the cloud, they need to have a very clear data governance strategy and processes. They need to start with ensuring that their data is classified and then make sure a clear strategy exists on which data can move to the cloud and which needs to stay on-premise (if needed). The next step will be to have a comprehensive data governance strategy and processes with clear data map, ownership of data and processes to ensure data is regulated and monitored to ensure its availability, integrity, security and usability. Regardless of whether enterprises move to a hybrid cloud or complete cloud, data governance in cloud computing becomes even more crucial as data is stored in many places across the network. Data should be managed centrally through a platform with fine-grained, attribute-based and purpose-based access control. Enterprises should ensure that data usage is monitored and logged for compliance with regulatory requirements. While data governance was a challenge even with on-premise infrastructure, the hybrid models of cloud computing, data proliferation and the advancements in data analytics with AI/ML have exacerbated the challenges even further.
- Applications – The monolithic nature of legacy applications make it a challenge to move to a microservices based architecture and leverage the power of cloud computing. The data dependencies of the application stack and the need to refactor some of the applications for scalability and performance make it a challenge to aggressively move towards modernizing the infrastructure. DevSecOps practices need to be ingrained in application development processes and enterprises need to make sure the staff is adequately trained and the tools exist to make sure code is secure by design and infrastructure optimization is incorporated while designing the applications
- Infrastructure Operations – Enterprises need to move to FinOps to ensure that the financial accountability is brought to the variable spend of the Cloud. Significant work needs to be done to incorporate infrastructure as a code and develop continuous delivery frameworks for containerization and automation.
· On the flip side, how can IT keep pace with heightened business demands? (highlight key benefits of enhancing infrastructure capabilities; examples of best practice etc)
- As Lord Tennyson said “the old order changeth , yielding place to new”, the old IT models needs to dramatically evolve to meet the new world where the role of technology has changed to a business and innovation partner to leverage a tech-forward strategy for the business. Technology delivery needs to be reinvented to take advantage of the advances in data analytics, platform-as-a-service and end-to-end automation. Organizations should start by rebuilding a strong, secure, flexible, scalable and reliable foundation so that the applications that sit on top of this foundation can be agile to the ever-changing market dynamics. Advances in data analytics along with AI/ML and powerful yet affordable data analytics cloud computing can enable enterprises to drive actionable insights in a proactive manner, predict trends and course-correct quickly. In addition to adopting agile practices, modern computing and incorporating DevSecOps and FinOps in the delivery approach, enterprises need to make sure that they budget for and make it imperative for their staff to be trained constantly.
- In addition to technology, focus should also be on business process optimization and leveraging Lean Six Sigma and Continuous Improvement principles/methodology to streamline the processes so that time and money is not wasted in automating processes that have not been optimized.
- Finally, enterprises need to become more data-driven and create a separate team to create the appropriate data analytics platform and centralize the efforts to create models leveraging AI/ML, while decentralizing the effort to build the reports and generate insights through strong self-service Business Intelligence. Data governance strategy and processes should be revamped in light of the data exploding and being distributed across the network with cloud computing.