Big Data Defined: Examples and Benefits | Google Cloud (2024)

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Data can be a company’s most valuable asset. Using big data to reveal insights can help you understand the areas that affect your business—from market conditions and customer purchasing behaviors to your business processes.

Here are some big data examples that are helping transform organizations across every industry:

• Tracking consumer behavior and shopping habits to deliver hyper-personalized retail product recommendations tailored to individual customers
• Monitoring payment patterns and analyzing them against historical customer activity to detect fraud in real time
• Combining data and information from every stage of an order’s shipment journey with hyperlocal traffic insights to help fleet operators optimize last-mile delivery
• Using AI-powered technologies like natural language processing to analyze unstructured medical data (such as research reports, clinical notes, and lab results) to gain new insights for improved treatment development and enhanced patient care
• Using image data from cameras and sensors, as well as GPS data, to detect potholes and improve road maintenance in cities
• Analyzing public datasets of satellite imagery and geospatial datasets to visualize, monitor, measure, and predict the social and environmental impacts of supply chain operations

These are just a few ways organizations are using big data to become more data-driven so they can adapt better to the needs and expectations of their customers and the world around them.

Big data definitions may vary slightly, but it will always be described in terms of volume, velocity, and variety. These big data characteristics are often referred to as the “3 Vs of big data” and were first defined by Gartner in 2001.

Volume

As its name suggests, the most common characteristic associated with big data is its high volume. This describes the enormous amount of data that is available for collection and produced from a variety of sources and devices on a continuous basis.

Velocity

Big data velocity refers to the speed at which data is generated. Today, data is often produced in real time or near real time, and therefore, it must also be processed, accessed, and analyzed at the same rate to have any meaningful impact.

Variety

Data is heterogeneous, meaning it can come from many different sources and can be structured, unstructured, or semi-structured. More traditional structured data (such as data in spreadsheets or relational databases) is now supplemented by unstructured text, images, audio, video files, or semi-structured formats like sensor data that can’t be organized in a fixed data schema.

In addition to these three original Vs, three others that are often mentioned in relation to harnessing the power of big data: veracity, variability, and value.

• Veracity: Big data can be messy, noisy, and error-prone, which makes it difficult to control the quality and accuracy of the data. Large datasets can be unwieldy and confusing, while smaller datasets could present an incomplete picture. The higher the veracity of the data, the more trustworthy it is.
• Variability: The meaning of collected data is constantly changing, which can lead to inconsistency over time. These shifts include not only changes in context and interpretation but also data collection methods based on the information that companies want to capture and analyze.
• Value: It’s essential to determine the business value of the data you collect. Big data must contain the right data and then be effectively analyzed in order to yield insights that can help drive decision-making.

The central concept of big data is that the more visibility you have into anything, the more effectively you can gain insights to make better decisions, uncover growth opportunities, and improve your business model.

Making big data work requires three main actions:

• Integration: Big data collects terabytes, and sometimes even petabytes, of raw data from many sources that must be received, processed, and transformed into the format that business users and analysts need to start analyzing it.
• Management: Big data needs big storage, whether in the cloud, on-premises, or both. Data must also be stored in whatever form required. It also needs to be processed and made available in real time. Increasingly, companies are turning to cloud solutions to take advantage of the unlimited compute and scalability.
• Analysis: The final step is analyzing and acting on big data—otherwise, the investment won’t be worth it. Beyond exploring the data itself, it’s also critical to communicate and share insights across the business in a way that everyone can understand. This includes using tools to create data visualizations like charts, graphs, and dashboards.

While big data has many advantages, it does present some challenges that organizations must be ready to tackle when collecting, managing, and taking action on such an enormous amount of data.

The most commonly reported big data challenges include:

• Lack of data talent and skills. Data scientists, data analysts, and data engineers are in short supply—and are some of the most highly sought after (and highly paid) professionals in the IT industry. Lack of big data skills and experience with advanced data tools is one of the primary barriers to realizing value from big data environments.
• Speed of data growth. Big data, by nature, is always rapidly changing and increasing. Without a solid infrastructure in place that can handle your processing, storage, network, and security needs, it can become extremely difficult to manage.
• Problems with data quality. Data quality directly impacts the quality of decision-making, data analytics, and planning strategies. Raw data is messy and can be difficult to curate. Having big data doesn’t guarantee results unless the data is accurate, relevant, and properly organized for analysis. This can slow down reporting, but if not addressed, you can end up with misleading results and worthless insights.
• Compliance violations. Big data contains a lot of sensitive data and information, making it a tricky task to continuously ensure data processing and storage meet data privacy and regulatory requirements, such as data localization and data residency laws.
• Integration complexity. Most companies work with data siloed across various systems and applications across the organization. Integrating disparate data sources and making data accessible for business users is complex, but vital, if you hope to realize any value from your big data.
• Security concerns. Big data contains valuable business and customer information, making big data stores high-value targets for attackers. Since these datasets are varied and complex, it can be harder to implement comprehensive strategies and policies to protect them.

Some organizations remain wary of going all in on big data because of the time, effort, and commitment it requires to leverage it successfully. In particular, businesses struggle to rework established processes and facilitate the cultural change needed to put data at the heart of every decision.

But becoming a data-driven business is worth the work. Recent research shows:

• 58% of companies that make data-based decisions are more likely to beat revenue targets than those that don't
• Organizations with advanced insights-driven business capabilities are 2.8x more likely to report double-digit year-over-year growth
• Data-driven organizations generate, on average, more than 30% growth per year

The enterprises that take steps now and make significant progress toward implementing big data stand to come as winners in the future.

Developing a solid data strategy starts with understanding what you want to achieve, identifying specific use cases, and the data you currently have available to use. You will also need to evaluate what additional data might be needed to meet your business goals and the new systems or tools you will need to support those.

Unlike traditional data management solutions, big data technologies and tools are made to help you deal with large and complex datasets to extract value from them. Tools for big data can help with the volume of the data collected, the speed at which that data becomes available to an organization for analysis, and the complexity or varieties of that data.

For example, data lakes ingest, process, and store structured, unstructured, and semi-structured data at any scale in its native format. Data lakes act as a foundation to run different types of smart analytics, including visualizations, real-time analytics, and machine learning.

It’s important to keep in mind that when it comes to big data—there is no one-size-fits-all strategy. What works for one company may not be the right approach for your organization’s specific needs.

Here are four key concepts that our Google Cloud customers have taught us about shaping a winning approach to big data:

Open

Today, organizations need the freedom to build what they want using the tools and solutions they want. As data sources continue to grow and new technology innovations become available, the reality of big data is one that contains multiple interfaces, open source technology stacks, and clouds. Big data environments will need to be architected to be both open and adaptable to allow for companies to build the solutions and get the data it needs to win.

Intelligent

Big data requires data capabilities that will allow them to leverage smart analytics and AI and ML technologies to save time and effort delivering insights that improve business decisions and managing your overall big data infrastructure. For example, you should consider automating processes or enabling self-service analytics so that people can work with data on their own, with minimal support from other teams.

Flexible

Big data analytics need to support innovation, not hinder it. This requires building a data foundation that will offer on-demand access to compute and storage resources and unify data so that it can be easily discovered and accessed. It’s also important to be able to choose technologies and solutions that can be easily combined and used in tandem to create the perfect data toolsets that fit the workload and use case.

Trusted

For big data to be useful, it must be trusted. That means it’s imperative to build trust into your data—trust that it’s accurate, relevant, and protected. No matter where data comes from, it should be secure by default and your strategy will also need to consider what security capabilities will be necessary to ensure compliance, redundancy, and reliability