Data and Analytics Processes Need a Fresh Approach to Help Businesses Thrive

The COVID-19 pandemic has raised new questions in every industry that the current data and analytics teams are struggling to answer. Sunil Senan, senior vice president and business head of data and analytics at Infosys, explains if it is time to take a relook at data operations to ensure that organizations become resilient.

Data and analytics are well-oiled machinery in most organizations today, refining data to generate insights that help generate more profits. However, the COVID-19 pandemic has thrown open the discussion on the need to review these mature practices. 

The COVID-19 crisis has led to new unknowns that are disrupting business operations. The flow of accurate information in real-time is becoming difficult in many industries, and companies are seeking to find answers to new challenges that their current data and analysis teams are failing to respond to. 

Additionally, the need to cut down on the cost of data and analytics is rising as CFOs are finding themselves working with smaller budgets. It is time for corporations to relook at their data strategies to develop the capabilities that can help them answer the most pressing questions relevant to current times, namely, building resilience, predicting changes, and adapting to these changes at speed.

The first step would be to review the technology stack that drives the data and analytics services. There are many layers to this, from assessing the computing capacity and storage to evaluating software and hardware. Outcomes have to be determined by comparing efficiency versus cost as specific hardware appliances may become obsolete, while certain software might need an upgrade or may have to be discarded. 

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For example, one of our clients had over 400 software tool contracts with different vendors across its data landscape, making it difficult to integrate and synchronize the data operations. Eliminating redundant or outdated tools, and integrating the remaining on a cloud platform can help streamline the data in such cases.

Paying consideration to what new technology the company needs to invest in and why – can answer some critical questions around productivity and cost. The time is ripe to consider investing in advanced analytics such as those powered by machine learning and artificial intelligence to extract valuable insights from data. To combat these uncertain times, the ability to predict changes in customer demand and other business factors with predictive and prescriptive analysis can make the difference between success and failure. 

Also critical is the need to maintain a vendor-agnostic approach to ensure that data and analytics workloads can be shifted between different platforms; this makes open source an inevitable choice. Lastly, security and governance policies have to map up to the changes in data operations to ensure the necessary mandates around regulation, accuracy, and safety are met.

Migrating to the enterprise data cloud is an essential consideration for organizations to make at this point, considering the enhanced flexibility and capabilities it brings while saving costs. Moving to the cloud is the most logical step to modernizing the current data estate. One can save millions of dollars just as one of our clients, a multinational investment bank, did by cutting its software costs by $35 million a year by modernizing its systems.

Once the technology stack is reviewed and renewed, the service layers need to be examined. Automating processes Opens a new window or building self-service systems can lead to better productivity. Questions such as can the service layer be more streamlined, can it be centralized, and is it aligned to strategic business operations need to be answered. The aim should be to build the capability to generate richer data sets and precise insights to answer new questions arising in businesses.

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While revisiting your data strategy, it is essential to have a buy-in from your data scientists and analysts. They may have concerns about losing control over their data, or they may be resistant to change. Bringing them onboard the organizational goal of mapping their data strategy with the new business strategy can play a significant role in ensuring the latest data strategy has the necessary support from your data team.

The data and analytics market offers different options, such as service bundling, contract partners, and technologies. This ensures that companies have the flexibility to choose between consumption-based pricing and pay-as-a-service model, allowing them to manage the data-related operations with greater efficiency and cost-effectiveness. Now is the time to revisit these decisions.

With shrinking budgets, Companies must reduce the cost of their data operations by standardizing processes, automating nonstrategic work, and optimizing technology. Overall, it is time to question the traditional data and analytics processes and frame new strategies that will help organizations become data-driven with the ability to sense and respond in real-time to the volatile business environment.

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