Digital Resilience in the Time of a Pandemic
If we learned one thing it is that business changes. Supply chains remained consistent, once a lot of the technicalities were worked out the focus was on improving efficiencies and maximizing revenues rather than an ever-evolving business model. The focus of most IT organizations was to support the initiatives of the business, every now and again there would be some change in the business model but for the most part the urgency was on supporting applications, managing automation, and getting in front of the data requirements.
So 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 anyway. 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 with both of them. 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 a digital transformation. While these may seem similar they involve significantly different business models that will need to be supported and yet they both share a common problem. The problem is that 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 you can separate the issues yet manage them with some better models.
The stresses of change on an IT infrastructure will address every aspect of IT from processes, governance to architectures. Thorough, well thought out processes are often a huge roadblock in managing the agile nature of the work required. While I am not saying to remove process you need to periodically review a lot of the processes to see if they still make sense or when and if automation can alleviate some of the roadblocks. Governance is usually an area that is lacking in how to manage other types of data instead of just enterprise data. The management of big data, the privacy involved merging datasets of social data bring new issues that need to be addressed. As for architectures they will need to support new initiatives, larger datasets and merge much more data than normal. The digital transformation aspects are also pushing customers to the cloud to manage costs and compute requirements as well as reducing complexity.
Complexity is something that is not often thought of since it is expected but the complexities of managing so many additional data sets brings creativity and innovation to a standstill. If you are falling behind in delivering, curating and managing datasets and there is a growing backlog of requests you need to assess if you need to manage some of that complexity out of IT and 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 silo’d data sets will struggle to merge them when they need to do it earlier in the process rather than later in the process. Merging data later is quicker and less invasive than building it into the system as data is ingested so that is where it is happening. Results of late merging data are mixed; it tends to cover over issues but they come back later when the 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. If for example you are looking for fraud in banking, you do not want to analyze data at the backend when it is too late to take action but at the point of sale when you 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.
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 your survival is based on the resilience of your processes and architectures to manage that change quickly.
About the Author
Asim Razvi is the head of data management and data strategy at Onis Solutions with over 25 years experience in delivering world class solutions in data to clients. He has architected some of the largest hybrid data management solutions for the Fortune 100 and also worked closely to deliver Business Intelligence strategy assessments to them as well. He works and collaborates closely with a number of CDOs and maintains a busy schedule of events and speaking engagements. Outside of work he trains outdoors to maintain a healthy lifestyle and spends time with his family in the wilds of the California mountains.
Vice President Lead, Data Management