The Role of Big Data Analytics in Accelerating Digital Transformation

In an era where data reigns supreme, phrases such as ‘data is the new oil/currency/fuel,’ etc., underscore the value of data for modern businesses. According to Gartner, “91% of organisations are engaged in some form of digital initiative.” Further, they say while 87% of business leaders confirmed digitalisation is a top priority, only 40% of organisations have reached scale for their initiatives.

To understand why this statistic is concerning, let us look at McKinsey's definition of digital transformation. It says, “Digital transformation is the rewiring of an organization with the goal of creating value by continuously deploying tech at scale.” And for realistically achieving this, businesses must harness the transformative power of Big Data analytics to convert vast troves of their data into business insights. It serves as a beacon, guiding business leaders by bringing clarity and direction to their digital transformation initiatives. Often, organisations fall short of achieving their digital transformation goals because of complex modern business problems such as:

  • Lack of visibility due to data issues
  • Legacy systems
  • Constantly evolving customer expectations
  • Dynamic market conditions

Big Data analytics accelerates the digital transformation of organisations through valuable insights aiding data-driven decisions. It also makes them future-ready through long-term flexibility and agility. It fuels digital transformation by ensuring a core data governance policy that gives the much-required direction to ensure success.

There are four types of Big Data analytics to elicit multi-dimensional data insights. Let us briefly glance at them.

Types of Big Data analytics

Descriptive: This technique helps analyse situations and draw conclusions, especially from the identified business KPIs. It helps significantly with a crucial business function - reporting, which relies on historical data, comparisons and similar analytical factors.

Diagnostic: Techniques like regression analysis, data mining and diagnostic analysis help businesses understand the cause and effect of events. It helps understand why something happened, or did not happen.

Predictive: In this technique, machine learning models study vast amounts of data to identify patterns and trends and predict future scenarios to help businesses prepare and stay ready.

Prescriptive: This technique follows the predictive analysis to devise possible solutions for future scenarios. It helps businesses make proactive decisions to meet the evolving market needs.

Impact of data analytics and digital transformation

Data analytics and digital transformation is transforming every sector. Here is a look at some significant changes taking place in a few key sectors.


The potential of big data analytics in transforming healthcare is immense. From making timely decisions that can save lives to streamlining insurance claims, it covers all aspects of healthcare. Here are a few scenarios that are seeing the impact in real-time.

  • Improved patient care through remote monitoring and thereby reducing the burden of in-person visits on the healthcare system.
  • Enhanced preventive care by identifying potential risks to pre-empt events like heart attacks.
  • Enhanced public health strategies by identifying affected demographics for chronic diseases.
  • Reduced fraud in health insurance claims, streamlined disability claims, etc.
  • Improved compliance and security with cloud-based service models.
  • Enhanced diagnosis, especially in areas of medical imaging, with advanced algorithms.

Banking and financial services

The reluctance to adopt digital transformation business processes in this sector stems from challenges like legacy systems, sensitive data issues and strict compliance requirements. Nevertheless, notable progress is seen in areas such as, customer segmentation, targeted marketing and delivery of personalised customer experiences. Other areas are:

  • Identifying fraudulent behaviours to mitigate potential risks and promptly alerting the bank and customers on time.
  • Gauging creditworthiness to enhance lending decision-making by studying customer spending patterns.
  • Gaining market insights to strengthen investment banking strategies.


Big Data analytics is the driving force behind the Industry 4.0 revolution. Improved operational efficiency and product quality, reduced costs, etc., are notable benefits in this sector. Some other specific use cases of accelerated digital transformation are:

  • Real-time monitoring of the entire production process, leading to data-driven manufacturing.
  • Minimising downtime through preventive maintenance of machinery.
  • Enhancing supply chain management through improved visibility on demand, better strategies and more efficient planning, pre-empting disruptions, etc.


Personalised offerings that suit every learner are the need of the hour. Whether school and college education or professional upskilling, digital transformation is helping edtech leapfrog into hitherto unseen horizons.

Here are some specific use cases.

  • Automated content moderation to ensure the safety of learners.
  • Online proctoring for ensuring integrity and security during tests.
  • Focussed identification of improvement areas for students through comprehensive performance insights.
  • Skill assessment and upskilling recommendations for professionals.

These are just a few examples of how Big Data analytics is accelerating digital transformation in a few sectors. There are many more examples across other sectors and the possibilities are endless.

How can Infosys BPM help?

Infosys BPM’s Digital Transformation Business Processes apply a diverse stack of tools and accelerators in a fit-for-purpose method to generate meaningful business impact and insights. Our bespoke processes and digital solutions help your business convert digital disruptions into opportunities through a preemptive approach. Embrace the change and create sustainable transformations through our proven business transformation models.

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