Your Data Infrastructure in the New Normal

The rush to remote work resulted in multiple challenges for organizations around the world, including Singapore, and leading the list of challenges was technology. Business organizations are forced to scale IT infrastructure to support the sudden shift, resulting in a migration to cloud-based applications and solutions, a rush on hardware that can support a remote environment, and challenges scaling VPNs to support remote worker security.

These concerns in technology persist while the world gradually opens during the ‘new normal’ period, since the remote work setup won’t go away. In fact, in a Gartner survey of 317 CFOs and finance leaders that 74% will move at least 5% of their previously on-site workforce to permanently remote positions post-COVID-19. Companies have options to stay fully remote or to apply staggered workforce, which means part of the employees will still have to work from home.

As such, having the right technical infrastructure in place to support remote workers remains critical. Companies will need to continue to upgrade their infrastructure to operate at scale while reducing expenses, performance and security issues. Cloud seems to be the best option, and for some organizations, hybrid cloud is the answer since they can still host their data on-premises, as the hybrid cloud model supports both private and public clouds, giving them a combination of flexibility and security during IT infrastructure scaling. 

However, in terms of data management in a remote or blended work setting, the diagnostics, resolution and optimization of data infrastructure has emerged challenged considering the vast, dynamic and interconnected nature of the underlying resources. With larger and much more complex sources and datasets, how can the data infrastructure, especially for the larger enterprises, cope with the challenges resulting from employees working in either remote or hybrid working arrangements during these unprecedented times?

While every business organization wants to be more data-driven, the silos created by the current work conditions driven by the pandemic forced companies, mostly enterprises, to rethink their approach to data access. Before, only a few elite data scientists could perform the task of analyzing complex data. Now, the goal is to empower anybody to use data at any time to make faster decisions with no barriers to access or understanding, allowing no gatekeepers that create a bottleneck at the gateway to the data.

Data democratization seems to be the popular approach, since it is said to be the future of managing big data and realizing its value. It is crucial in allowing data to pass safely from the hands of a few analysts into the hands of the masses within a company. Businesses that have armed their employees with the right tools and understanding are succeeding today because they are arming all their employees with the knowledge necessary to make smart decisions and provide better customer experiences.

In essence, data democratization starts with breaking down information silos as the first step toward user empowerment. Ideally, the tools will filter the data and visualizations shared with each individual — allowing employees visualize their data and align them with the organization’s KPIs: metrics, goals, targets, and objectives that have been aligned from the top-down that enable data-driven decisions.

With the right visualization and analytics tools in place, training the team becomes the next essential step. Since data democratization depends on the concept of self-service analytics, every team member must be trained up to a minimum level of comfort with the tools, concepts, and processes involved in order to participate.

Lastly, you cannot have a democracy without checks and balances. The final step to sharing data across your teams is data governance. Mismanagement or misinterpretation of data is a genuine concern.