Conquering the Three Vs of Big Data Analytics with the Cloud

Devon Tackels

Content Marketing Manager, Sigma

What makes Big Data, Big Data? It’s big, yes — the sheer volume of data being produced by software systems, apps, IoT devices, websites, etc. is astounding. According to an IBM Marketing Cloud study, 90% of the data on the internet has been created since 2016. And IDC predicts that by 2025, the total amount of digital data created worldwide will rise to 163 zettabytes.

What are the Three Vs of Big Data?

Volume is only one of Big Data’s defining characteristics. It has two others: velocity, which describes how quickly data is being generated, and variety, which alludes to the various formats that data comes in (not just structured data, but also unstructured and semi-structured). These three characteristics make up the Three Vs of Big Data.

Why do the three Vs matter?

It’s convenient that Big Data’s characteristics come in a well-packaged alliteration, but why do they matter? Because data is being generated in massive amounts in a variety of formats at nearly-incomprehensible speeds, this significantly affects how we do Big Data analytics. Legacy tools quickly become insufficient to handle the quantities and speed of this data. And without a lot of time and technical expertise, using these tools to structure Big Data is simply impractical. In this post, we’ll look at the challenges created by the Three Vs, the opportunities inherent in them, and how you can use the modern cloud data warehouse and BI software to overcome the challenges and reap the benefits.

Challenges involved in each of the three Vs

The challenges that come with Big Data are significant, and they’re only becoming more so as data generated by devices balloons across the world. Each of the three V’s has its own challenges.

Challenges of volume

First, the huge amounts of data being cranked out require scalable storage. On-prem data warehouses can’t keep up with Big Data’s requirements. Additionally, the volume aspect means that organizations must use a distributed approach to querying (where data is managed at multiple sites). Otherwise, they’ll struggle to process the large amounts of data being generated by their systems, apps, and software.

Challenges of velocity

Velocity is a challenge because speed in the big data generation requires speed in ingesting, processing, filing, and retrieving or querying. If you’re unable to do these things in real-time, as data is being generated, you fall behind and become unable to make use of the data. For example, think of the speed at which millions of people are creating data with every tweet, post, and comment on social media. It’s overwhelming without the right tools to meet this challenge.

Challenges of variety

Before the big data generation, data was less robust, being created at slower speeds, and much less unruly. Now, sensors and devices are creating raw data feeds, and to meet the need for speed, websites, apps, mobile devices, wearables, and IoT devices all use the semi-structured JSON format. So organizations are faced with the need to extract ordered meaning from huge amounts of disparate data being generated at speed.


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Opportunities inherent in the Three Vs

For companies that can tame each of the Three Vs, opportunities abound. These opportunities lead to competitive advantages, which is why everyone is talking about Big Data and trying to figure out how to master it.

Opportunities that come with volume

The large amount of data that’s being created by all of these systems and devices means that organizations have more data to analyze. And more data points are always better than fewer when it comes to seeing a full picture of what’s happening and, more importantly, why things are happening. For example, if you’re forecasting, you’ll get a more accurate prediction if you take into account all 150 factors that affect the forecast, rather than relying on 15.

Opportunities that come with velocity

The ability to process and analyze big data in real-time is invaluable. You can have confidence that you’re not being limited by data that’s out-of-date, facing the very likely possibility that things have changed in the meantime. An obvious example is financial traders who risk disaster if they rely on old information.

Opportunities that come with variety

JSON data is one of the most valuable types of data due to its pervasiveness. So much of the data an organization could benefit from comes as semi-structured JSON data. If you’re able to put this data to use, you’ll see more and better insights, and be able to do it quickly.

How the modern cloud data warehouse and BI software can help

Because Big Data is a modern-day Gold Rush, tools and techniques have been built and created to overcome the challenges inherent in the Three Vs. The modern cloud data stack, including the modern cloud data warehouse and CDW-native BI software (like Sigma) work together to give organizations the capabilities they need to enjoy their advantages.

The modern cloud data warehouse provides a flexible solution for storing vast amounts of data cost-effectively — and these solutions scale up or down quickly, as needed. They effectively address the problems associated with volume while giving companies the ability to use much more of the data they have available.

To address the challenges associated with velocity and variety of big data, the modern CDW and BI software also facilitate new ELT processes that allow organizations to transform raw data at any time, rather than in a set sequence, essentially removing an intermediate step to streamline the data loading process. Data vault modeling gives organizations even more flexibility, allowing them to bypass the judgment of what data is valuable and what isn’t, at the same time integrating data from various systems and tracing the origin of all data. This allows companies to process data in real-time.

Unstructured and semi-structured data reside together in the modern CDW, allowing it to act as a blend of data warehouse and data lake. Companies can now take advantage of data in a variety of formats, bringing it into their cloud-native analytics tool, connecting to various repositories and working with the data directly, using the built-in tools that the CDW provider offers. 

The Three Vs of Big Data: Rich opportunities for those with the right tools

Clearly, there are real advantages to equipping yourself with modern tools that have the necessary capabilities to mine insights from Big Data. Those with the right tools can overcome the challenges presented by the Three Vs and experience the benefits they hold.

Want to learn more on Big Data and see where it may be headed in the future? Check out our Big Data Analytics — The Definitive Guide.

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