Preparing for Data Ubiquity: How to Thrive in the Era of Infinite Data

Think about all the devices around us: our phones, watches, cars, home sensors, and even factory machines. They’re all collecting bits of information as we move, click, scroll, or go about our day. And they do it constantly. That’s why the flow of data today feels endless.

But collecting data is just one part. What makes data feel like it’s “everywhere” is how quickly and easily it moves. 5G gives us the speed to send and receive information in real-time, while edge computing means the data doesn’t have to travel all the way to the cloud but can be processed right where it’s created (think a smart fridge or a city-wide traffic sensor).

Still, having information everywhere isn’t enough. Three things matter for it to be useful.

• First, it has to come from various sources, not just one type of device or system.
• Second, it needs to be live, so we’re not relying on yesterday’s numbers.
• And third, it has to be easy to access and work with across different platforms.

When all that clicks into place, we stop thinking of data as something stored in spreadsheets. It becomes part of how we live, work, and make decisions.

Ubiquitous data touches nearly every part of business today. It helps companies in every sector understand what customers want when supply chains need adjusting and where new opportunities might be hiding.

Here are some of the most practical ways it’s used.

Businesses don’t have to guess anymore. Real-time data means they can spot and act on trends before it’s too late. A global fast-food chain, for instance, can use in-store and mobile app data to coordinate kitchen timing and customer orders and cut wait times. Some airlines adjust ticket prices on the fly based on current demand, weather, or competitor moves. In finance, banks detect fraud in seconds by spotting unusual patterns in live transaction data.

The more you know about a customer, the more relevant you can be. That’s why companies like Spotify and Netflix use viewing and listening habits to optimize individual recommendations. Retailers do the same, adjusting product pages or promotions depending on what someone browsed a minute ago. That said, none of this works without trust. People need to know how their data is used, and they need the option to opt out.

With enough data, it’s possible to understand the past and prepare for what’s coming. Manufacturers use sensor data to predict when machines are likely to fail so they can fix them before anything breaks. In retail, demand forecasting helps businesses know what to stock and where to move it before a shopping surge begins. These data analytics models suggest the best actions.

Walmart uses live data from stores, warehouses, and shipping routes to shift stock as needed. This helps avoid empty shelves and overflows. UPS uses GPS and sensor data to update delivery routes on the go, saving fuel and time. Even smaller businesses are starting to plug into these tools to react faster to disruptions like weather, demand changes, or supplier delays.

Data is also built into the products themselves. Tesla collects driving information from its cars to improve navigation and battery performance. Smart thermostats like Nest learn your daily habits and adjust home temperatures automatically. In healthcare, wearable devices help doctors track patient health remotely. These tools rely on continuous data, not one-off snapshots.

Information ubiquity also means increased control. Cybersecurity teams watch for minor changes in network behavior, like unusual login times or traffic spikes, to catch threats in advance. Some city governments do the same with emergency services. They combine traffic, weather, and sensor data to reroute ambulances in real-time. During natural disasters, satellites help map damage and coordinate response efforts within hours.

Large banks and credit agencies combine spending habits, job history, and even mobile phone data to get a clearer picture of someone’s economic health. Game developers, meanwhile, observe how players interact with different features to improve levels, tweak challenges, or change reward systems. In environmental science, data collected from air and water sensors helps spot pollution sources and track climate shifts across time.

As we’ve seen, ubiquitous data can transform how businesses grow, operate, and make decisions. But this kind of access also creates a new layer of responsibility.

For business leaders, data teams, and product owners, it’s not enough to focus on how to use data, though. The more information you collect, store, and analyze, the more you open the door to potential risks: privacy violations, system failures, legal trouble, and more. If you rely on data to make decisions, the cost of getting it wrong can be high.

Below are ten critical challenges you might face, along with practical advice on how to prepare.

• Privacy is harder to protect when data is everywhere.
  Collecting detailed information from phones, sensors, or online behavior can quickly become invasive if not handled with care.
  → Make privacy part of your design process. Limit data collection to what’s necessary, use strong anonymization, and keep users informed and in control.

• Security risks multiply with every connected device.
  Each sensor, platform, or cloud tool expands your attack surface. More integration brings more vulnerability to breaches, leaks, and unauthorized access.
  → Use layered security across your stack, encrypt everything, enforce strict access controls, and monitor for unusual behavior. Keep up with compliance frameworks like GDPR and CCPA.

• Poor data quality leads to bad decisions.
  Inconsistent, outdated, or incomplete data undermines trust and can cause models and reports to fail silently.
  → Establish a data lifecycle. Clean, validate, and regularly audit your datasets. Use tools to track where data came from and how it’s being used.

• Volume without structure creates an overload.
  Sometimes, the issue isn’t the amount of data but the fact that it lacks context. If it’s not organized correctly, even good insights can be missed.
  → Set up smart storage systems. Keep the data you need right away easy to access, and move older or less critical data to a separate database—set rules for what to keep and when to delete.

• Lack of interoperability slows you down.
  When your systems don’t talk to each other, everything slows down. You end up with duplicate data, delays, and blind spots.
  → Choose tools that work well together. Open APIs and shared standards make it easier to connect platforms across teams. Data fabrics or integration tools can help pull everything into one place.

• Generated data is more complex to use at scale.
  Most data today is unstructured: chat logs, documents, emails, call transcripts, product reviews, and videos. It’s harder to clean, label, and connect with other sources. Without proper handling, it stays siloed and adds little value.
  → Use tools like NLP libraries or vector databases to process the data and build clear workflows that connect it to existing systems.

• Ethical and legal boundaries aren’t always clear.
  It’s not always clear what’s allowed when it comes to using data. Laws differ by country and even between industries. What’s legal in one place could cause serious issues in another.
  → Make sure legal and compliance teams are involved early. Keep up with changing rules and review how data is collected and used across the business.

• Storage and computing have real-world limits.
  Processing petabytes of data can be slow and expensive. It also adds to your environmental footprint.
  → Use scalable infrastructure, such as cloud, serverless functions, and edge computing, to bring processing closer to the source. Deleting what you don’t need keeps your footprint lean.

• Bias in your data becomes bias in your outcomes.
  If your data reflects existing inequalities, so will your predictions and decisions.
  → Audit your datasets for representation gaps. Use fairness checks and include diverse voices in the design of AI and analytics systems.

• The technology is still catching up.
  Not every team is equipped to handle continuous data at scale, and many tools aren’t yet built for real-time collaboration or processing.
  → Build for flexibility. Hybrid cloud, serverless computing, and edge infrastructure let you grow without overhauling your stack yearly.

You don’t need to do everything at once. Start by building a culture where people use data to make everyday decisions. Give people tools that make insights easy to find and work with. Review your infrastructure to check if it can grow with your needs. Flexible, cloud-based systems make it easier to adjust without major rewrites.

Help teams understand what data is available and how it’s meant to be used. This includes setting clear naming rules, keeping shared definitions, and agreeing on how to flag reliable sources.

Don’t try to organize or clean all your unstructured data at once. Instead, pick one business task that matters right now and focus only on the data that helps with that task.

For example:

• If you’re managing contracts, look for ways to automatically extract renewal dates or clauses to avoid missed deadlines.
• If you’re running customer support, tag incoming messages by topic or urgency to shorten response times.

The idea is to tie the data effort to a specific, useful result, not to tackle everything in your data backlog.

Pick one improvement area, like cutting delivery delays or speeding up customer support. Test what works, track the results, and build from there.

As you make progress, consider how to reuse the tools and data you’ve already implemented. McKinsey recommends creating “capability pathways,” which are setups that can be used for more than one task. For example, a pipeline built to group customers by behavior can also help predict churn or suggest personalised offers. With a few adjustments, the same setup can support different goals.

Many teams still treat data as a side job. However, efforts fall apart without clear roles and joined-up thinking across business, tech, and compliance. Some leaders focus only on risk. Others are tasked with growth but do not influence how data is managed.

Companies that make progress tend to create mixed teams with shared goals and support from the top. The structure matters less than the outcome: decisions about data should be made by people who understand how it supports the business.

Prepare for new skills and roles.

As automation and generative AI take over routine tasks like reporting, tagging, and code generation, data teams are expected to do more with less. That changes what roles look like and what skills are needed.

Some of the new work fits into existing roles. For example, data engineers are already being asked to tune database performance, build cleaner data pipelines, and work with unstructured sources. There’s also a growing demand for cross-functional skills, such as DataOps, which combines software development, engineering, and data science.

Other needs are entirely new. Many companies add roles like prompt engineers, who guide how AI systems respond, or AI ethics leads, who check models for bias and fairness. Separate unstructured data specialist roles also appear in teams that rely on chat logs, customer feedback, or scanned documents.

Some of these skills can be added to existing roles. Others need training, coaching, or time set aside for hands-on learning.

Build Step By Step.

The businesses that succeed with data are the ones that prepare early, stay focused, and build step by step.

Now is a good time to start.