As the world is grappling with COVID-19 pandemic, human race is coming to terms with the new normal in the form of social distancing, shelter-in-place, and lockdown. While meeting humanitarian needs is of paramount importance to countries and businesses, the path to recovery and revival is still unclear. Economic impacts are more pronounced in some countries and businesses than in others. This crisis enabled an unprecedented surge of digital adoption across all walks of life. 

Businesses are re-drafting their strategies to survive from this economic impact and to prepare for the future. Recovery from this scourge is a carefully crafted art of balancing “Cost optimization effort to survive” (vs) “Digital transformation effort to thrive”.  Data-driven digital transformation has now gained relevancy like never before, for driving decisions timely and accurately. Many businesses had already started experimenting with data-driven digital transformation even before the pandemic hit. While a few had the appetite to translate these experiments into prioritized initiatives, others were nervous about this daunting undertaking as it is a business disruptor and it challenges status quo (i.e. decision process of the executives in-charge are upended and many times data-driven decisions contradict intuitiveness; it demands a higher level of commitment, governance, alignment along with a huge investment to fulfill these initiatives). 

In the next new normal, the  risk of not doing  the data-driven digital transformation is much greater than otherwise. Survival in this digital economy culminates in businesses building capabilities to adapt new business models that are in the form of micropayments — fragmented content, pay-per-use, streaming, subscription model, membership, freemium/premium selling and more. 

The central theme for this digital economy is the ability for companies to know their customers well and to build offers that can entice them through enriching experiences. Looking at this challenge through a data-lens, this is a function of harnessing heterogeneous datasets from multiple sources to produce insights that enhances the overall customer experience, defining new offers, evolving new pricing models, and supporting different buying patterns. Data needs to be the first-class citizen for every business strategy to drive this shift. To make this shift, a business function must trust their data and build a culture to embrace the “Data-driven decision process” by enhancing their technology capabilities to produce timely insights that are valuable for decision making. 

Key Considerations 

Making this paradigm shift to be a Data-driven Organization requires a framework that balances many key decision considerations from strategic to execution level that spans across People, Process, Policy and Technology: 

• Future backward (Transformative model for tomorrow’s need) vs Current forward (Enhancements for pain points) 

• Embrace business disruption vs Gradually introduce business changes 

• Enable modern data platform first vs Retire legacy data platform in parallel to maintain net neutral cost 

• Big bang Implementation vs Phased rollout 

• Centralized model vs Departmentalized model 

• Use Case centric vs Technology centric 

• Deliver Quick wins vs Build foundational capabilities first 

• Upskill current team to support future technology vs Inject new team to support business agility 

While there are many more decision factors involved based on an organization’s maturity, scale and its ability to embrace data culture and drive transformation , the following are the key success factors that every organization would like to measure in this journey: 

Agility: Ability of an organization to respond rapidly and easily for a new business model introduction. For instance, the ability of a data platform & its assets to quickly enable actionable insights for a business to define the right subscription pricing point for a product that is sold traditionally in a perpetual pricing model. 

Scalability: Ability for a data platform to handle the growing amount of workload & perform more work in the same elapsed time. For instance, if the platform can automatically scale up / scale down the storage, compute capacity of the environment based on the workload demand at an optimum cost. 

Security & Privacy: Ability for an organization to ensure their data protection and confidentiality with the right level of authentication and access controls; governance of personal data to be compliant with the regulatory rules like GDPR, CCPA. 

Availability: Ability to commit high availability by eliminating downtime despite failures, upgrades and outages through timely and reliable access to data. 

Performance Efficiency: Ability to use computing, database resources efficiently to meet the requirements and to maintain that efficiency with demand fluctuations. 

Cost Optimization: Ability to manage the transition to a new platform at an optimum cost for cloud software subscription, implementation cost of the new data platform and its operation. Ability to contain the total operational cost quickly through accelerating the implementation lifecycle and thus retiring the legacy license agreements. 

Approach 

Underpinning the data-driven digital transformation is a comprehensive framework tooled with Software that connects and accelerates the conversion of  Business Strategy -> Analytics Use cases -> Prioritized data platform capabilities to drive digital transformation. Comprehensive framework that defines and continuously enhances the Process, People, Policy and Technology maturity to deliver the Key Performance indicators of a digital initiative are as follows: 

1. The core part of this approach requires organizations to “Identify Business Improvement Opportunities” through Company’s Strategy for the future digital economy that are clearly measured by the Business KPIs 

2. Translate Business KPIs to Analytics use cases with dataset domain that are required to derive the hypothesis 

3. Prove the hypotheses through the datasets and Prioritize Use cases based on business outcome, cost and technology dependencies 

4.  Architect the modern data platform to build and deploy the Analytics capabilities and insights to deliver incremental business value 

5. Consume and adopt the business insights delivered through the data to transform the business 

Conclusion 

Data plays a significant role in connecting customers to the right product / service offering at a right price-point through the right channel at the right time to deliver the right experience. Organization’s digital plan that lays the foundation should factor in many aspects 

• Beyond simply bringing in new technologies, it needs to solve business problems, create unique customer experiences and accelerate business outcomes. Make the data-driven digital transformation “measurable” 

• This is a journey andit takes long-term investment to keep skills fresh as technology evolves 

• It requires well-governed, accessible, and trustworthy data 

• The transformation demands a continuous engagement with stakeholders to evangelize and address risks 

• Legacy data assets must be rationalized to pay for modernization cost thus maintain net neutral cost