5 Vs of Big Data

Being data driven Informed decision-making, especially when powered by automated business intelligence, delivers higher ROI and better business outcomes.

From AI engineering to hyperautomation, more efficient ways to use data across all business areas are at the heart of each of Gartner’s Top Strategic Technology Trends for 2021.

Data-centricity isn’t anymore optional to thrive Post-Covid. It’s a requirement.

Despite this, the reports that 77% of executives consider Big Data and AI initiatives their biggest challenge. Even worse, this percentage has grown exponentially over the past few years.

Is this trend likely to continue in the next decade? As companies attempt to convert to a data-driven mindset, they struggle to use their data effectively. A Catch-22 most businesses don’t know how to get out of.

You’ve collected massive amounts of transaction data. Now what?

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Collecting and accessing data isn’t the challenge. Businesses generate billions of data points every day. By 2025, 463 exabytes of data will be created each day anew. That’s over 90 times larger than the number of exabytes needed to store all the words spoken in human history! A good chunk of that data is social media, Tik Toks, emails and selfies — but even more is transaction data.

Retail businesses, in particular, gain some of their most valuable business intelligence from transaction data. This used to be a simple process. We could calculate demand and ideal pricing using Excel or even by hand. Nowadays, traditional methods of analysing transaction data fall unacceptably short. The scale of data makes it impossible even to wrap our heads around the numbers, much less derive useful insights from them.

Big Data has changed the nature of the problem.

We no longer struggle to collect data; we struggle to use it efficiently.

Once you have so much transaction data, it feels impossible to understand what should come next.

The traditional 4 Vs of Big Data

Luckily, there is an industry standard that can guide us. There are 4 Vs of Big Data that you need to address to use it effectively. You have to understand, standardize and validate each of these elements, or you will never successfully extract the intelligence available.

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1. Volume

Exactly how much data do you have? Sure, Big Data means a lot of data, but how much are you using? Volume establishes how much data your analysis must accommodate.

2. Velocity

How quickly is data being collected, stored and processed? Are you close to having real-time data, or is there a lag? How often does the model need to account for new data? Velocity establishes how timely and relevant your analysis is.

3. Variety

What kinds of data do you have? How is it structured? How diverse are your sources of data? Is there sufficient variation in the types of data for it to deliver applicable recommendations? Variety establishes how siloed or holistic your analysis is.

4. Veracity

Is the data correct? Are there missing pieces? How much of your data is noise? Veracity establishes how accurate your analysis is.

Value: the new 5th V

These Vs of Big Data may be the industry standard, but data scientists increasingly recognize a fifth even more important V: value.

In other words, what matters most about Big Data in business settings is your ability to turn data into decisions that increase ROI for the company. Data must be actionable and bring more value than the cost to analyse it. At scale, quality of data matters more than quantity. Value assesses the ultimate quality of the data available.

This 5th V is critical for companies to get right. Just because we’ve collected good data according to the other Vs, it doesn’t mean that it is actually useful. Take away value, and the data serves no purpose.

Better data ingestion + better analytics = success

The value factor of data is why so many companies fail in their attempts to be an effective data-driven business. Some spend so much time trying to manage the first four Vs that they are overwhelmed and can’t extract useful insights by the time it comes to value. Others focus entirely on analytics without worrying about the base Vs of Big Data that such that the data’s flaws limit its value. Without a perfect balance, you throw away critical intelligence.

Fortunately, balance is easier than you might think when you automate the basics: data ingestion and analytics.

Understanding data ingestion

Data ingestion has to do with the way you manage and create models for your data. Essentially, data ingestion concerns itself with the first 4 Vs. This is the process by which you address the volume, mitigate the velocity, delineate according to variety, and monitor the veracity. Data is siloed appropriately, and any discrepancies or gaps are identified and fixed.

Data ingestion can be incredibly complex, but some tools can now automate this process for you. , We created orchestrate data flows and runs a series of checks to ensure that the data we use is in order. AirFlow and other tools can achieve similar goals. These tools automate and double-check the processes that validate the data’s conformity with the first 4 Vs, allowing you to focus on value.

Automating analytics

A focus on value does not mean that you should sacrifice automation, however. Analytics should also be automated to avoid human errors. We have found that this is the difference between data science and business science: automation mitigates usage and input errors, the most significant sources of under-performance.

Autonomous systems on this side of the balance maximize value, the element of Big Data every company should care about most. Analytics is the step where you finally boil down the massive amounts of transaction data and other business data into insights. It’s where Big Data can actually make a difference in KPIs and your company’s success in the market. Without automated analytics, you will always fail to optimize value.

Avoiding the data-driven trap: AI automation

Digital transformation remains an elusive goal, but every business can become a genuinely data-driven operation with automated, AI-driven Big Data use. The trap of Big Data only can catch us if we try to use data without the aid of technology that can process, validate, and analyse data faster than any human. Instead of drowning in transaction data, you dig into its value to drive better results.