This year, enterprises are expected to embrace technology modernization for benefits beyond pandemic-related pivots. With half of enterprises forecasted to adopt cloud-native technology in 2022, there’s a growing need for expertise to plan, implement, and manage re-platforming and refactoring in this wave of digital transformation. For data professionals, there are many roles to play in helping organizations transform their technology with minimal disruption and optimal ROI.
Guiding a technology modernization program requires more than technological skills and experience to ensure a smooth transformation. It also requires data leaders to evaluate and communicate about the business impacts of the initiative, such as the cost components and other benefits of different options, like private, public, or hybrid cloud systems. Studying the costs, options, and business impacts before modernization can help data stewards manage the change effectively because modernization is a complex, multistage process that affects multiple business processes, from operations and data security to the employee- and end-user experience.
Key technology modernization roles for data professionals
Data professionals need to own the modernization strategy. This ensures alignment among solutions and use cases during modernization planning and implementation. Data stewards are also responsible for clarifying and managing expectations within the organization about what the modernization program can do.
With a digital ecosystem upgraded to include AI, machine learning, and API-driven capabilities for the consumption of enterprise data, the organization may find itself with more insights available than ever before. Data experts can guide the discussion about how to use those insights to evaluate inputs, identify potential revenue channels, and more, to ensure the best ROI on the modernization investment.
New technology also creates the need for new employee skills. Data professionals have to assess the readiness of the internal organization to work with not only new software but also with new vendors and a new technology ecosystem. Making sure those workers are prepared is a key part of modernization planning.
Last but not least, data professionals also need to think about their organization’s data archival requirements. For example, if an organization needs to access data that are several years old, will they still be able to do so after the modernization? If so, will it be as easy to access that legacy data as it is to access current data on the new platform? Thinking about how and where that historical data will be stored and accessed — and what the cost will be to maintain that older data — is a critical part of modernization planning.
Data-related technology modernization strategies
Every modernization planning phase should include assessments of the organization’s application and data portfolios. For example, consider a company that needs to update its technology. First, they’ll need to review their existing data stores, operational costs, the software products they use and the support levels they have, and the types of applications operating on their data. These assessments can also ensure that modernization plans factor into business continuity needs during and after the transition.
For example, if an organization’s C-team receives their alerts and notifications on dashboards that are configured to display data in a certain way, the data team will need to model similar or better notification dashboards for the executive team using the new technology. The reality is that some features of the legacy system may have to be modified or go away entirely if they’re no longer relevant. The responsibility of the data champion here is to set those expectations and get the new experience right with minimal disruption for the leadership team. Similar processes will play out for different employee teams and end-users that are affected by the transformation.
With leadership and across users, data stewards also need to manage expectations about the technology modernization timeline. It’s helpful to get teams thinking in terms of a three-month or six-month sequence of operations, for example, so they don’t assume that new tools and processes will be ready overnight. That’s because not all databases may be available on the new platform at the same time. In the interim, stakeholders and leaders may receive modified information, and not all employees may have access to the new platform at the same time.
Another key factor is data security during modernization. Data stewards need to plan carefully and execute meticulously to ensure data privacy, not only for the post-modernization technology stack, but also during the migration to those new tools and processes. Depending on the organization’s sector, it may need to maintain general data privacy standards throughout the transformation, like GDPR, as well as specific standards such as FedRAMP for government contractors, HIPAA for health care organizations, or FERPA for educational institutions.
After the transformation, internal and end-users may need additional support until they’re fully comfortable working with the new platform. Data professionals can work with in-house or outsourced teams to find the best ways to provide that support. Data stewards will also need to plan for the proper timing of decommissioning the older legacy systems once the migration is complete.
Ensuring a smooth transition
Modernization is critical for enterprises that want to remain competitive by leveraging new technologies to get the most value from their data. Data professionals have critical leadership, assessment, planning, and implementation roles to play throughout the process. By bringing an understanding of the appropriate technologies for the organization’s business processes, budget, and user behavior, data leaders can craft a technology modernization program that minimizes disruption, ensures business continuity, enhances ROI, and creates new opportunities.