Rethinking Digitization and Data science in a post- COVID world

You might have come across this cliché – COVID-19 has accelerated the shift to digital. With ongoing lockdowns and a projected recession, businesses are struggling to keep up with day–to–dayoperations and making tough decisions like layoffs, salary–cuts, and Capex rollbacks. While the present seems bleak and the future looks uncertain, businesses find themselves amidst an unprecedented crisis with only one thing certain: The future is digital.

While some industries quickly adapted to remote work and digital tools, others had to deal with multiple challenges to maintain business continuity. Business leaders have been busy with the adaptation of new operating models, optimizing business processes, measuring RoI of various spends, and gauging long-term business impact through data science and data–driven scenario simulations.

Governments and healthcare providers worldwide have adopted data science in mitigating the impact of COVID -19. This has been possible through the digital tracking of patients to monitor disease spread through epidemic forecast models to allocate healthcare resources through molecular modeling in drug and vaccine discovery and more. Access to quality data and data science experts to apply enhanced techniques is proving to be critical for faster recovery.

With no travel and reduced meeting hours, it could be an apt time for CXOs to rethink their future in this changing business environment. Once this crisis is past us, questions that will rush to be addressed are: Do manufacturers need more resilient and diversified supply chains? With video calls being the new norm, should service firms rethink client engagement? With reduced or remote staff, are there any activities that can be automated through RPA or AI?

Data and data science are two key ingredients of any digital operating model. While data sciencemight seem like a luxury today amidst this struggle for survival, it could be a differentiating factor in deciding winners of tomorrow. With a few visionary companies already ahead on the curve, all organizations must plan their digital strategy to adapt to the post- COVID normal before it looks us in the eye.

In the same context, this article discusses the potential roadblocks in the adoption of data science and possible ways to sidestep them.

Sizing the Pie

It’s all ‘covfefe.’

Before trying to ‘seize’ the pie, it is important to have an adequate idea about the ‘size’ of the pie. Yes, we do come across a lot of potential use-cases and disruptions that might be unleashed by ‘Data’ in every industry (Brace for Impact!). But should we be worried? And more importantly, is now the right time to act?

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Well, research indicates so.

Data science does have a wide appeal in many functions, ranging from customer-facing roles (sales, marketing, customer support) to operations (finance, manufacturing, supply chain). Still, it is crucial to align and prioritize efforts in the direction of business goals because each business unit within an organization requires a different approach and data science maturity. What is it that the organization wants to achieve, automate back-office tasks, increase marketing RoI, or optimize inventory levels? An assessment of the organization’s vision, along with its present capabilities and constraints, is important to shorten the wishlist and identify where the initial focus should be.

Culture Conundrums

The storming of the Bastille!

Yes, this is where we get to complain about corporate culture. Gut-based decision-making, multitudes of excel reports, never-ending budget forecasts, and out-of-the-world sales targets! But things could be better if all decisions were backed by data and facts so that everyone could see the underlying rationale while supporting and contributing to the decision-making process.

Undoubtedly, commitment from the top leadership is vital. A top-down mandate alone can’t ensure the wide use of data science for decision-making throughout the business. A bottom-up adoption to embed data science into the way the organization thinks, decides, and acts is necessary for good results.

Know Thy Hooman

Terminator Genisys: Fate of Humans

Even if the data and data science roadmap is in place, a skilled workforce (yes, that too!) is needed to make sense out of data. With an ever-changing landscape and faster-than-ever evolution in the field of AI, data tools, and infrastructure, hiring skilled data science resources is already a challenge. Moreover, retention and up-skilling the resources can be very demanding for an organization, in terms of both time and finances,

It is important to have internal leaders (e.g., a Chief Analytics Officer), or external partners who can drive the data science journey for your organization. Popular approaches include centralized units (CoEs for analytics), localized data science units for business functions, and external data sciencepartners. While in-house units may provide more control over operations and data, setting them up is a very time-consuming and resource-intensive process. External partners can mitigate talent and personnel risks by delivering access to leading data science practices, along with a reduced cost of ownership.

Error 404: Data Not Found!

Have it in the bag?

Data (or its lack thereof) can be the biggest and most overlooked challenge when it comes to the adoption of data science. Many organizations don’t have the necessary data to perform data science.Legacy practices, common examples of which include – data captured through physical forms, unstructured data, no scalable IT infrastructure in place to process data, and data stored in remote silos, are the primary reason that some organizations are not even aware that the data they have is of no practical use.

Prioritizing data collection and digitization of data from existing sources is the frontline solution to this problem. However, it is also important for companies to explore new data sources whileenhancing data accessibility for all key stakeholders.

Too many irons in the fire

Life is messy, but your data doesn’t need to be.

Data might be overwhelming at first because of the associated security, compliance, and financial (IT cost-related) risks. Most of these risks can be mitigated using proper data management, data governance, and cybersecurity guidelines.

The Chicken and egg situation

Practice makes perfect!

It is no surprise that analytics solutions need analytics users with requisite understanding. While innovation and pilot testing are what make an organization’s analytics infrastructure competent, company-wide adoption is the only way to derive value out of the investment. This might require significant effort towards change management, including training on ‘Why data science?’ and ‘How to incorporate data science into regular processes, governance, and strategy.’

Lastly, it is important to stay relevant and lean (you’d have to excuse this blog). There is no one-size-fits-all strategy (not even blockchain!). One needs to figure out what works and what doesn’t, which is possible only through a sustainable and self-improving implementation process.

Like in any other crisis, this one too will see strong and resilient business models emerge stronger,while their weaker counterparts, unfortunately, pave the way for newer ones.