CXOs Are Looking For Survival Ideas And Build For Post-COVID Future

How can CXOs take advantage of the economic lull to invest in their data assets and analytical capabilities to setup for long-term success? Here are strategies for building successful data and analytics proficient organizations

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With major economies gradually coming out of government enforced lockdowns and companies cautiously resuming business activity, CXOs have been afforded an opportunity to plan for the post-COVID world while simultaneously ensuring survival in the current times. This pandemic has pushed the need to accelerate adoption of new disruptive business models to stay ahead of the competition and meet the rapidly changing needs of customers.

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Building and scaling ‘firm-wide data and analytics capabilities’ should be at the forefront of this future-looking strategic approach. Success of Texan retailer H-E-B during the pandemic shows how data analytics can enable companies to improve services based on customer needs. H-E-B leveraged data from curbside service to re-structure their supply chain network to prioritize delivery of essentials (for example, their beer distributors were asked to deliver eggs to the stores instead). According to Kearney’s 2019 Analytics Impact Index (Aii) survey, “companies with high data analytics maturity typically generate 83 per cent more profits than companies with low data analytics maturity.”

This article takes a holistic view to introduce key strategic interventions required to build data-led organizations with special focus on challenges and foundational building blocks.

Challenges to becoming data-driven and analytics proficient

According to the 2019 Aii survey, 56 per cent of the companies are yet to create an integral, firm-wide data and analytics strategy. CXOs are struggling to foster this invaluable resource and convert data into real competitive advantage. Some of the challenges include: 

  1. Lack of an organization wide strategic vision leading to siloed decision-making

  2. Data fragmentation, lack of data integrity aggravated by rampant data bureaucracy

  3. Inability to holistically build a people, tools and platforms ecosystem

Foundational building blocks of data-driven organizations

Organizations worldwide are turning to data-driven insights to create business opportunities and increase operational efficiency. Leading data driven organizations can anticipate (instead of reacting to) market conditions and track changing customer needs. For example, retails brands including Kroger’s, Sprouts Farmers Market are leveraging changed consumer behavior data during COVID-19 to plan for post-pandemic growth phase (for example, development of new recipes).

Three major factors to foster a data centric environment are:

Cultivating data-linked outcomes: use-case portfolio approach

Leaders should take a ‘start small and scale fast’ mindset to develop a use-case portfolio approach and accelerate value delivery. Initially, a small intrapreneurial team with minimal seed fund should be set up to build select use-cases and highlight the impact to inspire the entire organization. Each use case must be mapped to operational and financial KPIs to track value generation. Additionally, selection of use-cases should be prioritized based on return on investment. Figure 1 illustrates the portfolio approach to identify and prioritize use-cases.

Further, CXOs should think of both offensive and defensive use-cases in the current COVID-19 related situation. For example, a leading Asian food delivery company is leveraging spatial data and micro-market consumer spending to introduce innovative non-food product categories and services during COVID (offensive strategy), while select logistics companies are implementing network modelling to identify new distribution hubs to optimize costs (defensive strategy).

Nurturing data enablers: develop data transparency and fit for purpose technology stack, and hire/train data science talent

A sustainable data proficient organization needs a well-oiled data management machinery and technology enablers. Technology stack should be gradually built based on the complexity of use cases and develop fit-for-purpose technology stack. Data management should go beyond the traditional IT led activities and focus on elements that reduce data bureaucracy and improve data literacy through transparent data access policies and data cataloguing to establish a single and consistent source of truth. It is also critical to hire and develop the right talent in-house to implement use cases. Additionally, firms are developing ‘citizen data scientists’ who can use analytics knowhow and business knowledge to achieve faster turnaround of projects. 

Harvesting data ecosystems: build new data partnerships 

It is important for CXOs to gradually invest in new external data partnerships to augment first/second party datasets through proof of concept agreements. There are a host of new sources of data enabled by digital consumer footprint that can fuel development of new use cases. Figure 2 shows the new age data companies that can be leveraged to bring new use-cases to life. 

Way forward

These foundational building blocks should be central to developing data analytics proficient organizations with major focus on accelerating business outcomes in a post COVID future world.

Bharath Thota, Vice President, Kearney; Susheel Sethumadhavan, Principal, Kearney; Manish Bindal, Manager, Kearney; Akshay Goel, Consultant, Kearney