The Impact of Big Data Analytics Across Industries

Big data has long evolved from being confined to IT sectors to becoming a business imperative. In 2018, the International Data Corporation (IDC) forecasted that global revenue for big data and business analytics solutions would reach $60 billion in 2022 with a compound annual growth rate of 11.9% from 2017 to 2022. However, the IDC’s latest Spending Guide placed that figure at $215.7 billion in 2021. 

As companies continue to find new ways to better leverage the massive amounts of data being collected every moment to enable solutions and retain a competitive edge, we take a look at several case studies of how big data is applied in five different industries.  

   

Human Resources: Driving business performance via people analytics 

 

A McKinsey case study details a major restaurant chain with thousands of outlets around the world looking to improve customer satisfaction and grow revenue. Business leaders believed could be done by solving the company’s high staff turnover problem by better understanding people. 

New and existing data were collected from individuals, shifts, and restaurants across the US market including the financial and operational performance of each outlet. Some points considered include personality traits of employees, day-to-day management practices, as well as staff interactions and behaviors.  

The more than 10,000 data points were used to build a series of models to determine the relationship, if any, between the desired outcomes and drivers. The model was used to test over 100 hypotheses, many of which were posited by senior management based on their own observations and instincts from years of experience. 

Noting that some of the hypotheses were proven while others were disproven, McKinsey reported: “This part of the exercise proved to be especially powerful, confronting senior individuals with evidence that in some cases contradicted deeply held and often conflicting instincts about what drives success.” 

Ultimately, the analysis revealed four insights that have gone on to inform the company’s day-to-day people management in its pilot market.  

Just four months in, the company experienced: 

• Over 100% increase in customer satisfaction scores 
• 30 seconds improvement in speed of service  
• Decrease in attrition for new joiners 
• 5% increase in sales  

 

Supply Chain: Improving cost and service efficiency 

 

A multi-location manufacturer sought to mine its vast library of inventory, shipping, and freight billing data to find ways to improve spending while maintaining service levels. They also wanted to identify opportunities for better inventory management, trip reductions, and order consolidation.  

Using available data, the solution provider created an integrated data management and analytics platform. This was supplemented by a custom order management algorithm.  

The system helped the company consolidate orders heading out to the same location in order to ship them out in one go, thereby reducing congestion at the shipping dock and reducing freight cost by 25%.   

Predictive analysis applied to the company’s supply chain management also led to: 

• 10% increase in shipping capacity 
• Improved service-level metrics 
• 10% decline in inventory levels  
• Less shipment backlog during peak seasons 
• Clarity on freight spend drivers 

 

Healthcare: Effective screening and treatment of diseases 

 

In China, there has been a rise in cerebrovascular diseases such as strokes. In response, the government launched a Healthy China 2020 plan aimed at improving public health. 

Following that, medical professionals investigated how best to treat strokes and related medical conditions by identifying three key areas: accurate screenings, precise treatments, and meticulous rehabilitation.  

They wanted a more effective way to analyze data than just using the traditional manual paperwork system, which was not scalable.  

Partnering with IBM, the Shanghai Changjang Science and Technology Department along with China’s top three hospitals developed an intelligent stroke assessment and management platform. The AI-enabled platform analyzes patient information, applies a screening model, and compares these details with known risk factors.  

Patients that have been identified as high risk are then channeled to the appropriate physician with treatment recommendations and corresponding probabilities of success.  

This application of big data analysis led to: 

• 15% improvement in diagnostic accuracy of stroke risks in patients 
• 80.89% accuracy in predicting treatment outcomes 
• Scaling risk screenings to cover a larger population and encouraging early treatment 

 

Financial Services: Post-trade analysis 

 

The National Bank of Canada’s Global Equity Derivatives Group (GED) provides trading solutions that manage securities such as stocks, futures, funds, and options. It collects and processes a high volume of stock-market financial data, but faces a challenge when it comes to data analysis.  

The bank sought to find a more effective and scalable way to process and analyze structures and unstructured data, as well as historical data, in order to develop a better analytical solution.  

Using an open-source big data processing framework and moving its processes to the cloud allowed the bank to achieve its goal of scalability. The GED were able to analyze hundreds of terabytes of trade and historical data. This now enables their business analysts to conduct quicker post-trade analysis.  

Big data analysis allowed the bank to: 

• Reduce post-trade analysis process from a few weeks to a few hours 
• More robust post-trade analysis 
• Improved trading operations 
• Increase revenue 
• Increased customer satisfaction 

 

Manufacturing: Predicting Equipment Anomalies 

 

A major manufacturing company looked to deploy digital twin technology to make manufacturing more flexible and efficient. The company, which was struggling to meet its production targets due to unscheduled downtime, created an IoT sensor-enabled digital copy of its critical equipment to predict potential anomalies and maintain the flow of its assembly lines. 

Falling short of its production target also meant that the company faced increased operating costs, customer dissatisfaction, and lost market share to its competitors.  

Applying IoT-supported digital twins technology allowed the company to collect real-time data. When analyzed with other data sets – historical and maintenance-related – the company was able to remotely monitor and assess their physical assets.  

The ML-based algorithm sifted through plenty of data to help detect abnormal equipment behavior and proactively suggested corrective actions before failure. This led to: 

• 100% achievement of production target 
• 25% reduction in operation costs 
• 54% increase in profit margins 
• Timely product delivery 
• Higher customer satisfaction and increased market share