Businesses want their data to be an asset — that’s just a fact. In reality, 60% to 70% of all enterprise data hasn’t been turned into business insights.
But there’s hope.
The business value data-mature companies are tapping into coupling with data analytics service providers is encouraging laggards to jump on the digital transformation bandwagon, too.
The early adopters cite the following benefits of big data:
· Faster innovation cycles
· Higher business efficiency
· Improved R&D
· Better product development
Offensive drivers, such as the desire for innovation and transformation, are overriding defensive ones as change is becoming the new constant in the business landscape.
Let’s see what prompts enterprises to turn to big data consulting services and look into the business impact of big data.
But first, a short note on the state of big data
The concept of big data, which has been around for about a decade, emerged in response to the surge in global data volumes. About 90% of all data has been generated during the last couple of years. In 2021, for example, we created and exchanged 79 zettabytes of data. That is roughly 2.2 quintillion bytes a day.
But big volumes aren’t the only problem of big data — it’s complex, too. Spanning structured, semi-structured, and unstructured data, it’s hard to make sense of with traditional analytics systems. So, it’s only natural that all this data has just waited there, completely unused.
The good news is that with the recent advances in AI, big data has finally started talking and driving real value for businesses.
So, what’s the value of big data for businesses?
Retail and ecommerce
With the challenges posed by COVID-19 and online tycoons like Amazon competing with almost every business, retailers are increasingly relying on big data to tap into new value.
The possibilities of big data analytics in retail are vast: from helping retailers win new customers with personalized recommendations to forecasting demand for specific items to optimizing shop floor operations.
Take Walmart, for example. The world’s leading retailer has rolled out an analytics hub for its employees to access data from hundreds of sources. With all the necessary info at hand, employees can make data-backed decisions and find quick solutions to problems that used to take time, like a sudden drop in sales for a particular SKU.
Another area where big data drives value is helping retailers understand customer behavior patterns. Digging in footage from CCTV cameras, retailers identify blind spots that are overlooked by customers, make better staffing decisions, and restock items that are in high demand. Studying customer demographics and buying patterns, retailers make sure to win shoppers with optimum pricing and optimized layout.
Inventory management gets better, too. End-to-end visibility into inventory helps prevent missed opportunities bound to stockouts, overselling, or slow deliveries.
Attracting new customers has also become easier with big data and AI integrated into CRM systems. The new, advanced systems not only track all kinds of unstructured data, like email or events but also allow automating follow-ups and flagging warm leads.
Ecommerce retailers, on the other hand, have an opportunity to drive higher sales by tapping into customers’ online activity. Analyzing the insights about sales channels driving more traffic, products attracting more visitors, and other factors, ecommerce companies optimize the online buying journey and help customers find the desired product much faster.
Marketing
Making sense of the three types of data — customer, operational, and financial — marketers get a more accurate view of their target audiences and design products and services that sell themselves.
For example, Nexflix’s recommendations and production decisions rely on a bunch of factors, like the titles people search for and watch more often, how often playback is stopped, and more. Data-based advertising has helped the team fuel drastic subscriber growth — even despite such controversial decisions as a VPN ban and a 2016 price spike.
Google and Facebook know a thing or two about data-powered advertising as well. Leveraging the personal data we all share online, the tech giants serve targeted, personalized ads of products and services we might actually buy.
Another application of big data for marketing is hyper-localized advertising. Based on location and demographics data, analytics platforms can prompt shoppers who happen to be nearby to drop in by offering personalized discounts or showing targeted ads.
Healthcare
Big data in healthcare can help predict the next pandemic, cut down treatment costs, and improve clinical operations. The data sources for the numerous improvements span: electronic health records, clinical data, patient portals, wearables, smartphones, and others.
One of the most popular use cases of big data in medical settings, electronic health records provide medical staff with a complete look into a patient’s medical history. EHRs contain demographics, diagnoses, prescriptions, allergies, and other data. Such systems improve care delivery by automating clinical workflows and making collaboration between care providers faster and easier.
An example of a smart EHR system with predictive capabilities comes from North Oaks Health System. The ML-powered EHR solution helps reduce sepsis mortality rates and improve antibiotics treatment.
Big data also helps optimize staffing. For example, a French hospital group is using a data analytics solution to avoid crowds. The software predicts emergency department visits for the next 15 days. The insights are derived from a database of more than 470,000 patient visits and third-party data on factors that correspond with hospital visits, for example, holidays, weather, or endemics.
Another use case of big data in healthcare is managing epidemics. During the outbreak of Ebola in West Africa in 2014-2016, for instance, data scientists leveraged the data about the frequency and location of helpline calls. This helped the researchers trace the transmission of the disease in the region.
The same approach was taken after the 2010’s Haiti earthquake. Mobile data was processed for coordinating relief operations and foreseeing the spread of the cholera following the disaster.
Big data helps improve diagnosis and treatment as well. For example, Tempus, a precise medicine company, is relying on an extensive database of molecular and clinical data to augment each patient’s clinical context and develop more personalized cancer treatment plans.
In dermatology, advanced AI algorithms successfully flag malignant skin lesions — with the accuracy of human clinicians. The classification algorithm in such systems relies on images of benign and malignant lesions. What’s important is that such systems can be deployed to smartphones, expanding access to vital diagnostic care.
Finance
Increasing reliance on data is one of the major factors shaping the banking industry in the coming years. The value of big data for financial institutions concentrates on uncompromised security and increased customer loyalty stemming from personalized offerings.
One example of a bank resorting to big data analytics for powering customer service comes from the Royal Bank of Scotland (RBS). The RBS uses big data to analyze customer complaints so they can come up with a faster resolution. The bank also analyzes packaged accounts to identify customers who might be overpaying for the same service.
Risk management is another area where big data drives big impact. One example of an enterprise leveraging data to foresee risks is United Overseas Bank. The Singapore-based organization cut down the time it takes to forecast the value at risk, i.e., the risk of losing investments, from hours to minutes.
Another common use case of big data in banking is preventing fraud. For example, Lloyds Banking Group analyzes the data about customer calls, including location, call history, and background noises, to create what the organization describes as an audio fingerprint of a customer. American Express, in turn, reports a 100% improvement in the fraud resolution rate and a 21% decrease in POS disruption since the adoption of AI-based data analysis.
Education
Coupled with AI, big data is changing education, too. It makes it possible to track student performance and engagement, develop more effective approaches to teaching, and redesign entire education systems.
Relying on the data about students’ scores, response times, or questions that the students answer successfully or fail, educators may develop better, personalized learning plans.
Big data can also help measure students’ engagement by tracking their facial expressions or other biometric data from CCTV cameras or wearables. When it comes to such solutions, the ethical issues are still intact though.
Big data also helps students find the right career path. College readiness platforms, such as Overgrad, help students identify their career interests, keep track of their progress toward academic targets, and follow educational institutions of their preference.
To sum it all up
Many businesses across sectors are uncovering big data and the business value it drives. According to Statista, the global big data market value is expected to reach $103 billion by 2027. With the competition getting bigger, it’s time to go big too.