Why are you collecting data? Why are you instrumenting products, digital twinning infrastructure, and requesting customer feedback? Perhaps, if you’re a modern business, it’s to embed real-time analytics into every part of your operations.
OK.
As we said last week, you need a great modern data stack to run a great modern enterprise.
But it’s more than that.
You need real-time analytics across your enterprise to power action-oriented business intelligence everywhere decisions are being made. This is essential for agility, speed, and scale in operations. And it’s increasingly powered by ELT for real-time analytics, which provide the ability to ingest data from anywhere, immediately make it available wherever, and transform or enrich it whenever for analysis and action.
This powers 2 things:
- Real-time visibility into operations and customers that legacy data processes can’t provide
- Quick iteration, fast decisions, and just-in-time action that’s informed by data, not just your gut, or the highest-paid person’s opinion
Modern, fast, and efficient ELT powers it all.
Real-time analytics in software companies
Software is an obvious use case, especially for software as a service solution. Tech companies need to know how customers use their software, so they capture telemetry like clicks, feature usage, and errors. Feeding that directly to devops and product people helps improve the product and reduce churn.
This kind of software usage data is often high-volume and it can be semi-structured. To be truly actionable, it needs to be correlated with business data like customer accounts, payments, and support tickets.
A SaaS enterprise might stream application event logs via ELT into a cloud data warehouse. There it can be used immediately for highly-critical issues like crashes or data loss, as well as combined with CRM records and support data to build a customer health dashboard that updates in near real time.
Tools like Extract play a central role in this pipeline, clearly. Extract can automate data ingestion from various apps while your data warehouse’s compute power handles the heavy transformation logic.
The result: an up-to-date single source of truth for product and customer analytics.
Speed is critical here.
A high-profile SaaS tool might have hundreds of thousands of customers, each of whom is potentially blocked from mission-critical work if the product is down. By loading granular product data quickly and deferring modeling to the warehouse, tech companies boost speed and responsiveness.
Marketers and advertisers need ELT too
Our parent company Singular has already seen that marketing ETL is a big deal for marketing and advertising data. But increasingly marketers are turning to ELT for more diverse sources of data and faster access.
Marketing teams handle data from a huge number of channels: web analytics, Google Ads, Facebook/Instagram ads, email campaigns, CRM, e-commerce platforms, social tools, and much more. But they’re also now grabbing data from the iOS App Store, Google Play, and other sources that give them intel on how consumers are adopting their mobile apps.
ELT pipelines are increasingly important for cross-channel marketing analytics and real-time campaign optimization.
For example, a brand’s marketing team might integrate data from literally dozens of ad partners (impressions, clicks, spend), social media engagement, resulting website and mobile app activity and behavior, plus sales and other conversions into a single data warehouse. EL can get these streams of data in near real time, helping marketers keep track of not just campaign performance, but also bottom funnel results.
Some marketers spend literally hundreds of thousands of dollars monthly in advertising.
Mistakes that don’t get caught can therefore easily cost 10s of thousands of dollars.
The speed of ELT means dashboards and alerts always use the latest data, which is crucial in fast-moving ad campaigns. Plus, of course, there’s reduced manual effort and cost to get the data where it needs to be. The result is that ELT-driven marketing analytics allows large brands and agencies to be more responsive and data-driven.
Real-time analytics in banking and fintech
Adtech is fast-paced and high-volume, but fintech and banks add an additional element: huge financial impact if you get it wrong. Banks, payment processors, and fintech startups process millions of transactions and log events daily that need constant and real-time monitoring for anomalies.
Real-time analytics here are often mission-critical: data comes in at high velocity and fraud detection needs to happen basically instantaneously.
That’s why banks are increasingly using ELT tools like Extract to ingest streaming transaction authorizations from their credit and debit card networks into a cloud analytics platform. By analyzing patterns in real time to look for tell-tales like rapid-fire charges or unusual spending locations, banks can flag and stop fraud quickly.
Or fire off an SMS to a customer: was that purchase of $500 in steak in Hawaii yours?
Because they’ve been in operation for decades and decades, many banks have particularly challenging data environments: transactional databases for core banking operations, payment gateways, log streams, old-school mainframes, plus third-party feeds like market data or credit bureaus information.
A modern ELT architecture, therefore, probably starts by streaming data from sources like these into a secure data lake or warehouse. Tools like Extract can capture raw transaction logs, event streams, and reference data and load it all into cloud data warehouses like Snowflake or BigQuery at high frequency.
That frequency and velocity is the key: fresh data gets analyzed with minimal latency.
By skipping transformations and loading data immediately, detection algorithms can run on the latest transactions and trigger alerts instantly: crucial for preventing fraud and reducing loss.
There’s also a long-term benefit: because all granular data is stored centrally, analysts and data scientists in a bank can explore whenever they wish and create new models for risk assessment, fraud detection, customer segmentation, and more.
That accelerates innovation while minimizing risk: perfect for banking.
Retail & e-commerce need this too
ELT is a two-headed monster for modern data stacks in retail and e-commerce companies.
- On the 1 side, you have needs around customer-facing analytics
- On the other, there’s operational intelligence: using real-time analytics to improve the commerce engine
Retailers gather data from point-of-sale systems, e-commerce websites, mobile commerce enabled apps, loyalty programs, marketing channels, and customer service interactions. Unifying these data streams allows for real-time insights like personalized product recommendations, targeted promotions, and dynamic pricing adjustments based on current demand.
Then data from the ecommerce engine itself provides insight for better operations, user interfaces, and customer communications.
Of course, another critical use case is inventory and supply chain analytics: you can’t sell (or support) what you can’t deliver.
(Or track during the delivery process.)
ELT pipelines can consolidate data from ERPs (inventory and orders), warehouse management systems (stock levels, locations), transportation systems (location, shipping, and delivery status), and external feeds like weather or market trends. That kind of real-time visibility helps a retailer avoid either stockouts or overstock situations, both of which are bad for business.
This is important for keeping the e-commerce motor running, but it’s also critical for delivering a live 360° customer view that the marketing team can use to make instant, personalized offers, or relevant recommendations or substitutes.
Real-time analytics in healthcare
Healthcare data is notoriously siloed:
- Electronic health records contain clinical notes and labs
- Separate lab information systems handle test results
- Pharmacy systems dispense medication
- Radiology systems store images
- Insurance systems have claims data
- Medical-grade monitoring devices have their own event streams and software to read them
- And now there are patient-generated data streams from wearables and fitness tech
Real-time analytics via ELT can help create a unified patient record, bringing all this data together for a comprehensive view of a patient’s health status and care journey
In time, this could even enable real-time clinical dashboards, perhaps monitored by AI systems.
In healthcare, ELT systems like Extract could get data feeds from databases, clinical systems, and streaming sources to load it all into a centralized warehouse. A key advantage is that clinicians and analysts could gain a more complete picture of each patient while administrators could get a better sense of capacity, load, and scaling issues.
Media and entertainment in the streaming era
Media is increasingly a streaming game: all the big players have their streaming channels and are competing for the largest audience in FAST (free and ad-supported TV) and the largest customer base in SVOD, subscription-based streaming platforms.
Understanding audience engagement, therefore, is critical.
As millions of users interact with a streaming platform, that platform collects data about every play, pause, search, and rating. All of this goes into an analytics pipeline, allowing the streaming brand to identify emerging trends like a sudden spike in viewing of a particular show and respond with timely recommendations or network optimizations.
We’re increasingly seeing real-time sporting and news on streaming platforms as well, and that opens up additional possibilities for real-time analytics. A sports program might use ELT to aggregate web analytics, social media mentions, and video view counts in real time, so editors can see what’s most popular right now and adjust the content accordingly.
Getting it right means people see something they want to see. And when that happens, they keep watching … and keep their subscriptions — or ad viewing activity — alive.
Real-time analytics makes everything better
We said recently that that data drives business. And a modern data stack powers a modern enterprise.
Getting real-time analytics via ELT systems like Extract enables flexibility, agility, situational awareness, and quick data-driven decisions.
Today, that’s no longer optional. In fact, it’s the baseline.
If it doesn’t reflect how you feel about your enterprise, book a demo. Or, if you’re already doing this but it’s costing too much, book a demo. We can definitely save you money over legacy solutions.
And hey, if maybe you don’t like talking to people, you can also just jump right into the free tier and try stuff.