August 29, 2021
How Big Data Is Transforming Industries in Big Ways
Digital transformation has placed data at the center of organizations of all sizes, across all industries, in both public and private sectors. Several factors are at play, including the rise of the cloud, widespread Internet use, and most recently—the sensors, devices, and systems that make up the Internet of Things (IoT).
Organizations have access to high velocity, high-volume data from a wide range of sources, and as a result, Big Data analytics is now a requirement for operating in today’s competitive landscape. Still, it’s worth noting that harnessing the power of Big Data goes beyond investing in the right equipment. Organizations without a comprehensive data strategy will have a lot of trouble making sense of all that information—and risk falling behind their better-equipped competitors.
Here’s a look at how Big Data analytics applications are transforming all ends of the business world.
How Do Businesses Use Data Analytics?
Before we get into the weeds, let’s make one thing clear: the term “data analytics” has been used by many organizations, business intelligence (BI) vendors, and publications to describe a near-endless range of functions. Therefore, answering the question, “how are businesses using data analytics” is complicated.
How companies use data analytics in their business varies considerably by sector, business size, and access to resources. Business data analytics examples include financial services companies using data analytics to analyze spending patterns to detect and prevent fraud.
Human resource departments are leveraging data analytics to make better decisions about hiring processes and measure employee performance.
And online retailers use data analytics to examine web traffic, track email marketing performance, and run targeted ad campaigns. Additionally, data analytics is used to do good in the world—in ways that extend beyond unlocking commercial value from a company’s heterogeneous collection of data sources.
As an example, according to the World Economic Forum, data plays a critical role in fighting climate change. It helps researchers quantify emissions from oil and gas fields, pinpoint destructive processes in supply chains, and monitor leaks, pollutants, and anomalies at specific locations.
Competitive Advantage of Data Analytics
As the Harvard Business Review points out here, gathering information and applying it to improve products, processes, and services is nothing new. Paper surveys, sales reports, focus groups, and other studies have long been used to identify problems and inform business strategies.
The problem is, these strategies are too slow to provide meaningful insights, and the sample sizes are too small to ensure data accuracy. The amount of data generated by businesses in every sector is unprecedented, and it’s those organizations that can quickly extract usable, accurate insights from their data that stand to gain a competitive edge.
Here are a few areas where the use of data analytics delivers significant gains:
- Cost Reduction. Using Big Data technologies like Hadoop or cloud-based analytics allows organizations to store large amounts of data in a cost-effective, efficient manner. Additionally, real-time data analytics allows businesses to identify and fix inefficiencies, incorporate feedback, and streamline processes—enabling organizations to avoid waste, work faster, and even increase profits.
- Better Decision-Making. Big Data’s primary value comes from its ability to facilitate smarter, faster decision-making. Businesses can now analyze a ton of information in near real-time and make strategic decisions based on accurate data.
- Improved Products and Services. Big Data analytic applications also allow companies to come up with new and improved solutions and products. Again, this benefit comes from the ability to identify customer needs, gauge satisfaction, and incorporate changes as insights are uncovered. For brands, analytics allows them to avoid the guesswork associated with product development, and instead, make it easy to give customers exactly what they want.
Data alone doesn’t provide much as far as a competitive advantage is concerned. Companies need an effective system for gathering, storing, and analyzing data from several heterogeneous sources to extract and act on valuable insights.
Top Sectors Shaped By Big Data Analytics
Big Data has changed just about every industry, from professional sports and social media to manufacturing, finance, and education. Here’s a quick look at some of the key use cases for data analytics by sector:
The healthcare sector generates huge data sets with insights spread across multiple systems that house consumer data, patient information, clinical data, hospital capacities, community health, and more. This industry faces several challenges coming from all angles, including rising costs, crowded facilities, and increased pressure to maximize the number of patients seen each day.
The healthcare system has also long been plagued by failures in putting data to good use, as electronic data is often unavailable, inadequate, or unusable. Privacy laws like HIPAA and outdated systems have made it difficult to connect disparate data sources that can reveal useful patterns and trends in the medical field.
Today, things are turning around. Healthcare data analytics is used to improve patient outcomes and provide better experiences. For example, this web-based app uses Big Data to help prioritize cancer treatments during the COVID-19 outbreak. Predictive analytics are also used to enhance palliative care, speed up the diagnostic process with AI-enabled chest X-rays, and reduce cases of end-stage renal disease by using predictive modeling to weigh the risks and benefits of kidney disease treatments.
Data collected from loyalty programs, credit card transactions, website behavior, social media and email engagement, IP addresses, mobile applications, user log-ins, purchase histories, and more now give retailers a 360-degree view of the customer. That granular visibility allows online retailers to analyze consumer behavior to predict future spending and create personalized content and hand-picked recommendations for every customer (Amazon’s recommendation engine is probably the best-known example of this).
Big Data also gives retailers the ability to identify how customers research product information, how they feel about the brand, why they’re unsubscribing, and what compels someone to make a purchase.
While manufacturing is historically a “low-tech” sector, Big Data is shaking up the industry across the board. Big Data analytics in manufacturing allows organizations to gain end-to-end visibility into production processes, supply chain metrics, and environmental conditions that impact productivity and deliverables.
In the world of manufacturing, sensors are one of the most common data analytics application examples, and play a significant role in detecting potential maintenance issues, preventing downtime, and avoiding costly repairs. Pattern recognition and predictive analytics can be used to create a more efficient quality testing process, while data from production machinery and employee output can be linked and analyzed alongside financial information and customer satisfaction metrics.
Banking and Finance
The financial sector generates a ton of data. We’re talking petabytes of structured and unstructured data that, with the right tools, can be used to anticipate consumer behaviors and help financial services companies make better decisions. Big Data analytics in finance allows companies to create convenient, personalized products and services without compromising consumer security.
Financial decisions like investments and loans are now placed in the hands of AI, which uses machine learning technologies to process loan applications, evaluate potential investments, and calculate risk. For example, Big Data analytics can evaluate stock prices alongside social trends, economic factors, and the political landscape that might impact the stock market.
Additionally, data has made it easier for companies to detect and prevent credit card fraud, making it safer for consumers to make purchases online. Today, banks and credit card companies can use data analytics applications to detect anomalies, instantly freeze consumer accounts, and inform the customer about the breach.
Transportation and Logistics
On the transportation side, companies are gathering and analyzing telematics data from their fleets and applying those insights to improve driving behavior, optimize delivery routes for faster arrival times and better gas mileage, and to take a more proactive approach to vehicle maintenance. In the warehouse, you often see shelf-level sensors and digital cameras monitoring stock levels with programs that provide alerts when it’s time to re-order.
Big Data analytics also allows organizations to improve forecasting, as shelf-level data can be fed through machine learning algorithms to train an intelligent system to predict when to stock up on supplies.
Another one of the more notable Big Data analytics application examples is how data is used to transform sports marketing and gameplay. Professional teams are increasingly hiring machine learning experts to help them with everything from driving ticket sales to engaging fans as well as recruiting players and making on-the-field decisions. Away from the field, analysts, media outlets, and fans use sports data analytics to make predictions, inform fantasy league strategies, and offer play-by-play breakdowns of last night’s game.
For sports marketers and business leaders, Big Data analytics enables them to answer questions about sports teams and their fans. Like the retail sector, sports teams rely on analytical tools to learn more about their audiences and use insights to drive revenue.
In the education sector, Big Data can identify and improve teaching strategies to help students succeed academically. Big Data might also be used to measure teacher performance and ensure a positive learning experience for students. Teachers can be measured based on a range of variables such as subject matter expertise, student engagement, student performance, classroom demographics, and more.
In addition, the US Department of Education’s Office of Educational Technology is experimenting with Big Data analytics to prevent students from slipping through the cracks while learning online. The agency is using click patterns to detect boredom.
Higher education institutions are also increasingly experimenting—with systems that track things like when students log into an online learning portal, how much time they spend on different pages, and how they progress through their coursework. Given that most education happens online now due to the pandemic, we’ll likely learn a lot more about how people learn as more data becomes available. Long-term, this may lead to advances in personalized, adaptive learning experiences.
For social media marketers, Big Data analytics is key in tracking campaign performance, researching competitors, and learning what makes their audience tick. Marketers can take that information and create audience segments, and then create personalized content that speaks to their unique needs—allowing them to anticipate needs and exceed expectations. It also allows marketers to test campaigns before launching, analyze test results, and make changes on the fly—potentially saving significant amounts of money on ad spend.
Big Data also helps marketers gauge brand sentiment, identify what types of content are most effective for engaging customers, and which platforms their audiences prefer. Additionally, Big Data analytics applications give social media marketers a big-picture understanding of what works and what doesn’t, and it allows them to incorporate those insights into future campaigns.
Ultimately, it’s clear that we’re well past the “Big Data hype cycle.” Companies now can track and analyze business data in real-time and at-scale—a capability that unlocks a long list of benefits, from faster innovation cycles and quick decision-making to better customer experiences and more efficient production lines.
Those still wondering whether Big Data analytics fits into their business strategy are losing ground fast, as early adopters continue to refine and evolve how they capture, access, and manage their ever-expanding data sets.