Exploring Big Data Examples Across Industries

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Large amounts of data (structured, semi-structured, and unstructured) that are too complex for traditional data processing technologies have come to be defined as big data.

Sensors, social media feeds, payment devices, and electronic health records are just a handful of big data sources that businesses use to make informed decisions. But what do the applications of big data look like in the real world? To help you understand, here are five examples of big data analytics in different industries and use cases.

Big data in e-commerce

Personalization matters, regardless of the industry. But in e-commerce, where competition is fierce and customers are two clicks away from going to a competitor’s website, requires personalized customer journeys.

With big data, e-commerce businesses can use predictive analytics to better understand user intent: segmenting audiences based on their propensity to buy or should be included in a re-engagement campaign. 

Segment’s role in e-commerce data management

Segment helps retailers collect and unify all of their customer data into real-time profiles. These profiles provide a holistic view of customer journeys, and help businesses better understand behavior, craft more targeted campaigns, and even build better recommendation engines. 

Take retailer Bonobos as an example. They leveraged Segment’s CDP to unify online and offline first-party customer data and optimize Facebook ads. This led to a 3x increase in online and offline purchases. What’s more, Bonobos could link offline purchases to their Facebook campaign, giving them additional insights into campaign performance.

Big data in the healthcare industry

Big data analysis supports healthcare providers on multiple fronts – the most important one being the quality of care and patient outcomes.

Researchers at the Massachusetts Institute of Technology (MIT) are developing different digital health tools backed by big data. Dimitris Bertsimas, professor and associate dean for the Master of Business Analytics at MIT, has created over 20 applications that support clinical decision-making for diabetes, cancer, and other illnesses.

Such applications allow healthcare workers to consider knowledge gleaned from millions of cases when treating patients instead of just their own experience.

Another MIT research team (Jónas Jónasson and Erez Yoeli) is working on personalized treatment pathways for tuberculosis, a disease that takes about six months to cure. The team is using data to evaluate when patients should receive human intervention to assist with treatment adherence as opposed to only using a digital platform.

Ensuring data privacy and security with Segment

Collecting and using big data in healthcare is more complex than in other industries like retail. Health information is sensitive and subject to strict regulations. (In the US, this would be HIPAA.) So healthcare organizations need to use data platforms that include guardrails for maintaining data privacy and security.

Segment allows companies to build a HIPAA-compliant data infrastructure, which includes automated PHI screening. We can also sign business associate agreements (BAAs) for compliant PHI management.

From a security standpoint, the platform uses encryption to protect patient data from threats, and we conduct regular penetration testing.

Learn more: Data Privacy vs. Data Security: Differences & How They Work Together

Big data in financial services

Financial services companies, from fintechs to big banks, use data to increase revenue and mitigate risk. When a person applies for a loan, for example, the lender can aggregate data from their credit report and other alternative sources (such as income or employment) to gain a complete picture of their financial health.

Analyzing spending and saving data can help forecast when a customer is preparing for a big life event. This allows a bank to recommend the right financial product for their needs, which will improve the customer relationship and increase lifetime value.

Big data also prevents fraud by helping banks understand what “normal” behavior looks like for each customer. Then, they can leverage these insights to build fraud prevention technology that flags suspicious behavior in real time.

Segment’s contribution to financial data analysis

Financial services companies utilize Segment’s CDP to orchestrate and protect their data.

In the case of insurance provider Toggle, they used Segment to pull all of their customer data into a single hub, automate data governance, and design personalized journeys across different channels. 

“We can send emails and newsletters to different customer segments with curated content and offers that they are actually interested in, all based on the insurance they have already purchased. For instance, we send information about keeping pets healthy to our pet insurance customers,  and we can promote complementary insurance packages based on a user’s purchase history,” explains Todd Wright, senior technologist at Toggle.

Big data in marketing

Marketing teams depend on data to craft and analyze campaigns. 

Gathering customer data from multiple channels gives businesses an overview of how each customer interacts with their brand across platforms. So, they’re able to reach out to the customer on the channel with the highest likelihood of conversion, whether it be SMS, email, or web.

Utilizing Segment for marketing data strategies

Now that browsers are phasing out third-party cookies, collecting first-party data is a top priority for marketing teams. Segment helps marketing teams do just that by aggregating all of their first-party customer data. 

Once the CDP collects data from different sources, you’ll be able to create unified customer profiles to deliver tailored messages and build propensity-to-purchase models. Segment has over 450 pre-built connectors to tools and platforms, allowing you to activate data in your audience’s preferred channels.

The role of IoT data in big data analytics

Internet-of-Things (IoT) devices contribute to the growing volume and variety of data that organizations collect. They include different sensors, home appliances, and wearable devices like smartwatches. 

For example, a doctor researching hypertension will want to access the data collected by their patients’ wearable blood pressure monitors. Then, they can combine it with data from electronic health records and other sources to better understand their conditions.

In financial services, banks are using IoT data from beacons to detect when a customer has entered a branch. Then, they send a tailored offer to their phone to capture their interest while they’re at the branch and can easily sign up.

Segment’s integration with IoT platforms

Segment’s CDP connects to multiple sources: websites, mobile devices, servers, cloud apps, and event streams. Plus, it can integrate with your data warehouse. Any IoT data that’s stored in these sources will be processed by the CDP and unified with other data sources, creating enriched customer profiles. 

Segment’s CDP collects and processes data from numerous sources.

Segment pulls the next steps from your big data

From healthcare to financial services, Segment’s CDP has helped organizations across industries turn their big data into insights that impact revenue. With Segment, you’re able to lower customer acquisition costs with highly targeted ad campaigns, predict future purchases, and see where each customer is on their unique journey.

Interested in hearing more about how Segment can help you?

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