We often think of data-driven decision-making as the realm of high-tech companies like Google, Amazon, and Meta.
But traditional brick-and-mortar industries are also embracing the power of data analytics and artificial intelligence to transform how they operate.
Across sectors like manufacturing, transportation, healthcare, retail, and more, established businesses are integrating data-fueled insights into their DNA.
They’re using the tools of the digital age to boost efficiency, better serve customers, uncover opportunities, and stay competitive.
One of the key tools they’re using is predictive analytics, the demand for which is growing rapidly, as organizations use data to forecast and respond to future scenarios.
By 2026, the market for predictive analytics is expected to reach $22 billion.
Let’s dive into some prime examples of how traditional industries are leveraging data-driven approaches to upgrade everything from supply chains to sales processes.
Spoiler alert – The results are dramatic.
Manufacturing: Using IoT, Sensors, and Big Data For Precision Optimization
The manufacturing sector has long relied on process optimization and automation to maximize productivity.
But the rise of smart sensors, Internet of Things (IoT) devices, cloud computing, and big data analytics takes it to the next level.
This combination of technologies enables what’s called Industry 4.0 – the fourth industrial revolution that leverages connected devices, big data, and advanced analytics.
Manufacturers are using these tools to collect data from across the operation – from suppliers and inventory to equipment on the factory floor – and gain insights no human could possibly analyze manually.
For example, IoT sensors on manufacturing equipment can detect vibration, temperature, and other parameters in real time.
This data feeds into predictive maintenance algorithms to identify parts in need of service before failure. This prevents costly downtime and optimizes maintenance resource allocation.
Manufacturers also use sensor data for deeper process analysis. Identifying quality variations as they occur allows issues to be addressed immediately, reducing waste.
Historical process data combined with advanced analytics powers “digital twin” simulations for the virtual design and testing of new products.
Big data analytics provides macro-level visibility as well – into supply chains, demand forecasting, and emerging market trends. This allows manufacturers to tailor production planning to align with real-time shifts.
According to McKinsey research, these capabilities can result in
- a 30-50% reduction in machine downtime,
- a 15-30% improvement in labor productivity,
- a 10-20% decrease in the cost of quality, and
- a 20-50% reduction in time to market.
How General Electric Optimizes Jet Engine Production with Digital Twins?
A great example of leveraging big data analytics comes from General Electric (GE).
The company collects sensor data from its jet engines during flight tests and creates “digital twin” simulations. These physics-based virtual models of the engines enable GE to optimize design and production.
The sensors measure engine parameters like temperature, pressure, vibration, and fuel consumption.
The data feeds into algorithms that can detect anomalies, model performance under different conditions, and predict maintenance needs.
By comparing the real-time data against the digital twin models, GE can identify performance issues early. Engineers can tweak components virtually to improve efficiency before modifying physical production.
This big data-driven approach allows GE to reduce engine emissions, optimize fuel burn, and cut maintenance costs.
According to GE, their digital twin technology saves airlines $1 billion annually by improving fuel efficiency by 1% – reducing carbon emissions by 7.5 million tons per year.
How Toyota Uses Data Analytics to Detect Assembly Line Defects?
Another leader in leveraging manufacturing data is Toyota. They rely heavily on sensor data analytics to spot defects on their auto assembly lines.
Sensors and cameras installed along the production process collect data such as:
- Conveyor belt speed: 10 meters per minute
- Welding machine temperature: 1500 degrees Celsius
- Car door alignment: 0.5 millimeters off
- Paint appearance: glossy and smooth
Machine learning algorithms analyze this data and compare it to expected ranges. For example:
- Conveyor speed: 10 meters/min (within range)
- Welding temperature: 1500°C (within range)
- Door alignment: 0.5mm off (error)
- Paint appearance: glossy and smooth (within range)
The machine learning algorithms then flag any defects, errors, or anomalies and alert the workers or managers. For example:
- The alignment of the doors: 0.5 millimeters off (error) -> alert worker to adjust door alignment
By using this predictive quality monitoring, Toyota quickly identifies the root causes of defects. Early detection and correction results in improved quality, less rework, and near-zero product defects according to Toyota.
Transportation: Leveraging Data Streams for Smarter Mobility
From connected vehicles to intelligent rail systems, data analytics is integral to building a seamless, optimized, and safe transportation infrastructure.
With sensors and telemetry installed across vehicles, roads, railways, and traffic management systems, transport companies have access to rich real-time data streams.
Turning this flood of information into actionable insights is revolutionizing the sector.
For example, the logistics industry taps into telemetry data to optimize fleet routing and capacity utilization. Predictive algorithms help reduce idle time and mileage while increasing the agility to meet real-time shipment demands.
In passenger aviation, aircraft sensors feed terabytes of flight telemetry data to optimizers that help airlines refine routing and operations for efficiency.
This data also powers air traffic control systems to sequence flights and manage airport traffic safely.
When it comes to road transportation, data enables governments to take a systemic approach to improving traffic flow.
Sensors monitor vehicle speeds, queues, and transit times, allowing traffic signal timing to be adjusted dynamically based on real-time conditions.
The widespread adoption of GPS navigation systems also provides a wealth of crowdsourced data for traffic analysis.
The US Department of Transportation’s Safety Data Initiative demonstrates how data mining and analytics can improve road safety as well.
By analyzing vehicle and road attributes involved in past crashes, the DOT builds statistical models to predict risk factors for severe crashes.
These models guide targeted infrastructure improvements and policy changes to reduce accidents.
How UPS Saved 100 Million Gallons of Fuel with Route Optimization
Logistics giant UPS relies on data analytics to optimize routing for its fleet of over 120,000 delivery vehicles.
The company uses a proprietary software called ORION (On-Road Integrated Optimization and Navigation) that leverages algorithmic optimization and real-time data to enhance efficiency.
ORION integrates data from telematics sensors on trucks, GPS tracking, traffic patterns, weather forecasts, and more.
The algorithms analyze all this data to calculate the optimal route for each driver that minimizes mileage, avoids congestion, and factors in pickup/delivery deadlines.
For example, ORION may route a driver to make deliveries in a suburban area first during the morning rush hour.
Then it charts a path through downtown when traffic dies down. ORION also adapts routes dynamically based on new pickup requests and changing conditions.
By optimizing routes daily for over 55,000 drivers, ORION has helped UPS save more than 100 million gallons of fuel and avoid 100,000 metric tons of emissions. In dollars, it’s saving them $300–$400 million annually
This demonstrates how data analytics helps transportation companies boost efficiency, reduce environmental impact, and increase profitability.
Retail: Analytics Power Personalization and Efficiency
Data is money in the world of retail, whether it’s an e-commerce giant like Amazon or a brick-and-mortar store chain.
By capturing every customer interaction and tracking metrics from inventory to sales, retailers utilize data to boost revenue through hyper-personalization and operational efficiency.
E-commerce retailers like Amazon analyze our every click and purchase to curate recommendations calibrated to our tastes.
Offline retailers are also getting in on the personalization game through loyalty programs and apps that track in-store browsing.
Data also allows retailers to optimize pricing dynamically based on demand signals instead of relying on simple fixed price schedules. Integrating geospatial and weather data enables more strategic decisions for inventory distribution across locations.
Even on the back-end side of things, retail players analyze sales data, foot traffic, and other signals to optimize everything from staffing schedules to supply chain logistics to shelf layouts.
How Walmart Leverages Data to Optimize Operations?
Walmart is a pioneer in leveraging big data analytics to transform retail. The company collects vast amounts of data from transactions, sensors, social media, and more.
Walmart collects a massive amount of unstructured data – around 2.5 petabytes per hour from 1 million customers. To put that in perspective, one petabyte equals 20 million filing cabinets filled with text or one quadrillion bytes!
Advanced analytics provides insights to boost sales, productivity, and the customer experience.
For example, Walmart analyzes point-of-sale data to dynamically tweak pricing based on real-time demand signals. During Halloween, this allowed Walmart to promptly fix an incorrectly priced novelty cookie driving low sales in select stores.
Walmart also uses sentiment analysis on social media and survey data to identify customer pain points. This enables corrective actions to improve service and satisfaction.
In addition, Walmart leverages in-store sensor data to optimize everything from shelf layouts to staff schedules.
Walmart has created what it calls its Data Café – a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters.
The Data Café allows huge volumes of internal and external data, including 40 petabytes of recent transactional data, to be rapidly modeled, manipulated, and visualized.
By tracking customer traffic patterns and behaviors, Walmart arranges products and resources for maximum sales and convenience.
Walmart’s data-driven retail strategies achieve an average 19% increase in sales. This demonstrates the competitive advantage analytics creates in the retail industry.
Healthcare: Clinical Data Analytics Boosts Life Sciences
Electronic health records, medical imaging data, genomics, and more – healthcare is awash with vast troves of patient data with life-saving potential.
But it’s only with recent advances in clinical data analytics and data science that this potential is being unlocked.
Pharmaceutical companies analyze longitudinal medical records at scale to uncover correlations and patterns leading to adverse drug events. This pharmacovigilance enables safer drug development and improved post-market surveillance.
Hospital networks tap into clinical data and algorithms to reduce diagnostic errors, hospital readmissions, and preventable complications. Data supports optimized treatment pathways for better patient outcomes.
Crunching studies from millions of patient records allows data scientists to develop predictive models for early disease risk detection.
This enables focus on preventive care instead of waiting for acute events. Data from wearables and home health monitors provides doctors with a more holistic view of patient’s health outside the clinic as well.
Overall, a Deloitte study estimates data analytics applications could result in $3.5 trillion in shifted value (from the traditional system) as a “well-being dividend” for the US healthcare ecosystem alone by 2040.
A Study of IBM Watson Health and Pfizer for New Drug Discovery
A recent study by IBM Watson Health and Pfizer analyzed electronic health records of over 1.6 million rheumatoid arthritis patients.
Using machine learning, they identified correlations between inflammation biomarkers and cardiovascular disease risk.
The study found patients on biologic drugs had lower cardiovascular risk versus those on synthetic drugs like methotrexate. This enables more personalized treatment plans based on a patient’s unique risk factors.
For example, 45-year-old Mary Jones was taking methotrexate for rheumatoid arthritis. Her data showed elevated inflammation markers and cardiovascular risk factors.
By switching to a biologic drug personalized to her profile, Mary reduced inflammation, joint damage, and cardiovascular risk.
The end result is better patient outcomes and satisfaction, reduced healthcare costs, and avoidance of potentially dangerous adverse events. This demonstrates the immense power of clinical data analytics in healthcare.
Finance: Real-Time Risk Analysis and Predictive Modeling
Finance lives and breathes data, so it’s no surprise that financial institutions are applying advanced analytics across the board – from customer service chatbots to AI fraud detection to high-speed algorithmic trading.
With access to vast reserves of financial data, banks, insurance firms and other players in the space are implementing sophisticated machine-learning techniques for data modeling.
This powers everything from real-time credit risk assessment for loan underwriting to predicting claims risk based on demographics and behaviors.
According to a survey by Exasol, 88% of UK financial executives reported pressure to accelerate data-driven decision-making during the pandemic.
With increased volatility and lending risk, financial institutions relied on data to quickly pivot strategies while optimizing profits.
Banks are also personalizing services by leveraging data and predictive models. Your deposit account balance, spending patterns, and credit profile determine the loan offers and card upgrades you receive.
Portfolio management platforms crunch market data at millisecond timescales to inform automated algorithmic trading.
How HSBC Leverages AI to Detect Financial Crime
HSBC relies heavily on artificial intelligence to combat money laundering, fraud, and terrorist financing. The bank developed an AI platform called Financial Crime Risk Management Platform (FCRMP) that analyzes billions of transactions.
FCRMP integrates data points across customer records, transaction details, external databases, and more. It uses machine learning algorithms to detect suspicious patterns indicative of financial crime.
During an anti-money laundering trial, FCRMP provided risk scores for transactions that analysts reviewed. HSBC saw a 60% reduction in false positives and 2-4x more accurate alerts compared to legacy rules-based systems.
HSBC now runs over 50 risk factors on all 5.8 million trade finance transactions daily. This allows identifying laundering schemes involving shell companies, unusual fund flows, and other red flags much faster.
According to the UN, money laundering accounts for 2-5% of global GDP, so banks are racing to leverage AI to catch laundering earlier and minimize the economic impact.
Automotive: Connected Cars Generate Data Goldmines
Modern vehicles are equipped with hundreds of sensors and over 100 million lines of code. This instrumentation generates terabytes of data on vehicle performance, driving patterns, GPS trails, and more.
Automakers and third-party apps leverage this data for real-time diagnostics, predictive maintenance, usage-based insurance pricing, and much more.
With driver consent, automakers analyze vehicle sensor data via cloud analytics to identify potential issues before they become major repair problems.
Predictive algorithms also optimize maintenance scheduling and part replacement based on actual usage rather than fixed mileage intervals.
Mapping and traffic app companies like Google integrate crowdsourced driving data from users to provide real-time traffic and routing.
Car insurance companies now offer policies priced based on actual driving behavior tracked via a mobile app. This allows safe drivers to get significant discounts compared to pricing based on demographics alone.
Monetizing data from connected cars is also giving automakers new revenue streams.
As self-driving technology matures, manufacturers view data as the key differentiator, with GM projecting potential revenues of up to $50 billion which transcends to $2,000 per customer per year from data-driven services built atop vehicle data.
Example: FordPass, Tesla, and BMW ConnectedDrive
One example of a connected car service is FordPass, which allows Ford owners to access features such as remote start, lock and unlock, vehicle status, and live chat support through a mobile app.
FordPass also offers rewards, roadside assistance, parking, and fuel station finder services. Ford claims that FordPass has over 1.5 million active users and has generated more than $150 million in revenue from 2017 to 2019.
Another example is Tesla, which is widely recognized as a leader in connected car technology. Tesla uses OTA updates to deliver new features, enhancements, and bug fixes to its vehicles without requiring physical visits to service centers.
Tesla also collects and analyzes data from its vehicles to improve its self-driving capabilities, battery performance, and customer experience.
Tesla has also launched a subscription service for its Full Self-Driving package, which costs $199 per month and gives access to features such as Navigate on Autopilot, Auto Lane Change, Autopark, Summon, and Traffic Light and Stop Sign Control.
As an individual Tesla owner, you could take advantage of the following connected car capabilities:
- Remotely control features like start, lock, and unlock via a smartphone app
- Receive over-the-air software updates to enhance performance, efficiency, and safety
- Share driving data to help Tesla improve its self-driving and customer experience
- Subscribe to Full Self-Driving package for $199/month to access autonomous features like Autopilot, Summon, and Automatic Lane Changes
- Get monthly driving statistics reports, achievements, and suggestions from Tesla to improve your experience
A third example is BMW, which offers a range of connected car services under its BMW ConnectedDrive brand.
These include infotainment, navigation, safety, security, and convenience features that can be accessed through the vehicle’s touchscreen, voice control, or smartphone app.
BMW also uses data from connected cars to provide personalized services such as BMW Intelligent Personal Assistant, which can learn the driver’s preferences and habits and offer suggestions and assistance accordingly
Telecoms: Optimizing the Network and Customer Experience
With the explosion of smart devices, apps, streaming media, and mobile internet usage, telecoms sit on mountains of data.
Call detail records provide insight into usage patterns, network telemetry reveals congestion hot spots and customer support interactions indicate pain points.
With advanced analytics, telecoms optimize networks and tune offerings to usage trends.
By analyzing mobile data usage by location and time, carriers can pinpoint where to upgrade infrastructure like towers. Real-time diagnostics identify service-impacting faults for prompt resolution.
Predictive maintenance modeling guides proactive equipment servicing before outages occur.
Understanding media consumption patterns allows the packaging and pricing of data plans to suit customers’ needs.
Churn analysis powers retention programs targeting subscribers likely to defect. Integrating all this data provides a 360-degree customer-centric view of telecom leverage to improve satisfaction and loyalty.
No wonder analyst firm Gartner predicts the global telecom services market will reach $1.4 trillion in 2022, propelled by growth in mobile data and broadband access.
How Verizon Leveraged Data Analytics to Cut Churn
Verizon Wireless is a leading example of how telecoms use data to boost loyalty and reduce churn.
The company partnered with Intent HQ, a behavioral data and AI company, to improve its churn model. The company analyzed web browsing data and call detail records of over 100 million customers to extract intelligence on customer behavior and preferences.
Verizon Wireless used Intent HQ’s AI platform to process and enrich the behavioral data and generate features for their churn model.
They improved churn prediction accuracy by up to 3.5% in the highest-risk decile and by 2.1% in the top 3 deciles.
Verizon Wireless forecasted an incremental revenue of $180 million over five years from improving its churn model accuracy and associated retention offers.
Agriculture: Insights from Farm to Table
Industrial farming may seem far removed from big data, but even this traditional industry is yielding benefits from analytics.
Connected sensors across fields linked to cloud platforms give agribusinesses unprecedented oversight into crop growth cycles.
By monitoring location-specific parameters like soil chemistry, moisture, and nutrient levels in real-time, data-driven insights guide irrigation, fertilizer application, and other interventions for optimized yields.
When harvest time arrives, agricultural machinery outfitted with sensors provides detailed yield mapping for analysis. Insights on optimal planting density, crop variants, and more refined practices for the next cycle.
Further along the supply chain, data analytics prevents waste and contamination. Sensors track produce conditions like temperature as it gets transported from farms to packing plants to retailers.
Models forecast shelf-life and ripeness. Grocers tap into these insights to reduce spoilage and improve inventory and restocking planning.
According to McKinsey research, data-driven farming practices could unlock over $100 billion in value annually across agriculture.
A Case Study of the Farmers Edge Platform
In 2020, Farmers Edge used satellite imagery and machine learning to monitor over 23 million acres of farmland globally.
The platform analyzes soil, crop, and climate data to provide customized recommendations on irrigation, fertilization, and pest management.
This allows farmers to optimize yields and profitability. For example, a corn farmer using Farmers Edge boosted yields by 35 bushels per acre and reduced input costs by $32 per acre, increasing net profit by 35%.
Key benefits of Farmers Edge’s data-driven approach include:
- Timely crop health insights without on-site scouting
- Optimized irrigation and fertilization based on predictive analytics
- Early detection of pest infestations, drought stress, and other yield threats
- Guidance on mitigation practices to protect crop quality and yields
- Overall optimization of inputs, costs, and profitability through data insights
With data analytics spanning the farm-to-table supply chain, agriculture can boost efficiency, sustainability, and bottom lines.
Data Is Revolutionizing How Traditional Industries Operate
As these examples demonstrate, leveraging data, analytics, and AI is fundamentally transforming traditional businesses in virtually every industry. Data provides unprecedented visibility into processes, inventory, equipment, and customers.
This powers everything from predictive maintenance to personalized customer experiences to hyper-optimized operations. Legacy companies are integrating data-driven insights across their value chains to boost efficiency, accuracy, and growth.
Of course, a data-driven culture requires more than just investing in analytics tools. It requires breaking down data silos within organizations and building the expertise to translate data into meaningful insights.
Then, processes must ensure data guides decisions at all levels – from C-suites setting strategy to frontline employees interacting with customers.
However, those able to bridge this cultural and capability gap will gain a lasting competitive advantage in their industries. Because in today’s data-rich world, leveraging analytics is no longer optional – it’s imperative.
