Digital Resilience in the time of Pandemic

If we have learned one thing it is that the nature of successful business constantly changes. Although supply chains have remained consistent, once a lot of the technicalities were worked out the focus has been on improving efficiencies and maximizing revenues rather than executing an ever-evolving business model. The focus of most IT organizations has been to support the initiatives of the business with the focal urgency on supporting applications, managing automation, and getting in front of the data requirements. Yet as we emerge on the other side of the pandemic what are the real open issues, and where should we focus? 

First, remember that we were in a time of disruption regardless. It is easy to conflate the Internet of Things with the devastation of the pandemic, but it is also essential to understand how we can cope while adapting to both factors. The disruption with IoT has really made the architectures of how we manage data evolve and adapt to a larger scale of information, whereas the pandemic has accelerated the move to an online presence, as well as catapulting businesses into a digital transformation. While these may seem similar, they involve significantly different business models that will need to be supported while addressing a common problem: companies now need to become digitally resilient because it is the root adaptation for both issues and will help to manage costs, performance and SLAs. If these can be recognized and managed as separate issues those business models are more likely to thrive, particularly under stress.

The stresses of change on an IT infrastructure will address every aspect of IT from processes and governance to architectures. Thorough, well thought out processes are often a huge roadblock in managing the agile nature of the work required from an IT perspective. A process needs to be periodically reviewed to see if it still makes sense or it needs to be properly updated/automated. This concept will ensure that the company processes are properly governed and are cohesive with the enterprise data strategy. 

Governance is usually an area that is lacking in how to manage many types of data, beyond enterprise data. The management of big data and the privacy measures involved in merging datasets of social data bring to light new issues that must be addressed. Data designs and platform architectures will need to support new initiatives, larger datasets and merge far more data than normal constructs today. This mindful data management in the face of digital transformation also pushing customers to the cloud to manage costs and reducing complexity. 

Complexity is expected but rarely appropriately planned for, which is problematic because the complexities of managing so many additional data sets brings creativity and innovation to a standstill. If delivery is falling behind with a lack of managing datasets and there is a growing backlog of requests, there needs to be an assessment of where the complexity exists, and where it should be managed (out of IT or elsewhere). Sometimes the options are to establish as much automation as possible, replace processes with RPA where possible as well as SaaS, PaaS and AaaS solutions that require less overhead and enable more time to get things done. 

The pandemic has fundamentally changed the way we do business, accelerated online business, and forced businesses to adopt digital transformation much faster than they planned. As we emerge from the pandemic we will see and have to address a new normal where teams engage less at the office, work more remotely and collaborate online. The IoT technology wave has allowed us to really move quickly to bring new sources of data in, but businesses with siloed data sets will struggle to merge them when they need to do it earlier in the process rather than later.  

Merging data later is quicker and less invasive than building it into the system as data is ingested so that is often the case. Results of late merging data are mixed; it tends to gloss over issues but they come back with a vengeance when a data analytics and data science team need access to raw data or data in process. Then the lack of a data lake becomes clear, but a data lake built for staging data is not the kind of data lake that remains viable for very long.  

The clear mandates need to be to manage data quickly at the source of the data allowing for data to become part of the fabric of the company immediately rather than later in the process. For example, searching for fraud in banking, one does not want to analyze data at the backend when it is too late to take action, but instead at the point of sale when there is a need to know and understand behavior and not just bank amounts. Edge analytics will help but data analysis needs to move out of the usual data warehouse and into agile, smaller analytics systems very close to where the transactions are created. It is not just the what, combined with the who that matters anymore; it is also the where.

Geolocation capabilities now allow us to track customers on a real time basis, which means that marketing teams have fundamentally changed how they interact with customers. There is currently a huge shift to combine financial data (payer data) and retail preferences. The banks in Europe are under pressure to release customer financial records to PSD agents that can authorize transactions. The goal of Amazon, Facebook and Google is to begin to add that information to their already strong understanding of your purchase habits. Understanding where someone is in real time has become critical to now offer the right incentives at the right times. The new business model will apply pressure to all businesses to adopt new technologies to new models quickly so they can compete. There are many more components driving business change but suffice it to say we are in for constant change and the pandemic is only part of the story. The new normal is that change is now a part of the model and business survival is based on the resilience of the processes and architectures to manage that change quickly.