The 7 V’s of Big Data - impact.com (2024)

Drive partner and program growth and succeed with partnership marketing by uncovering the seven Vs that define big data.

4 mins

The 7 V’s of Big Data - impact.com (1)

The 7 V’s of Big Data - impact.com (2)

Matt Moore

Associate Manager of Product Marketing

Read time:4 mins

Defining big data allows you to derive more value from partnership marketing, and the seven Vs sum it up pretty well:

  1. Volume
  2. Velocity
  3. Variety
  4. Variability
  5. Veracity
  6. Visualization
  7. Value

How big data makes a major difference in partnership marketing

Whether you employ traditional marketing tactics like paid ads or leverage cutting-edge partnership strategies, data plays a crucial role in growth.

There’s no shortage of applications for big data in partnership marketing. Big data is the fuel that delivers real-time analytics. Visualizing these analytics helps partnership managers understand the performance of every partner and their program at large. Using data in a thoughtful, effective manner will pave the way for confident decisions about how to grow your program and where to invest.

Those same analytics are the actionable insights that drive program decisions. The more information you have on your performance, the better you can optimize the partnership program to deliver the highest return on ad spend.

Big data provides detailed insights about the customer’s website experience, helping you identify the partners contributing to conversions, even if they aren’t the last click. It enables a better understanding of which customers have the highest lifetime value so that you can adapt your program to reach similar buyers.

Investing in partnerships that deliver maximum return and possibly even diverting spending away from search and display creates value for your business.

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Discover how you can tap into the power of partner marketing and gain benefits like, increased revenue, brand awareness, and conversions.

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The 7 Vs explained

As more brands turn to partnership marketing as a revenue-generating channel, understanding the 7 Vs of big data results in a winning partnership program and, ultimately, business growth.

The 7 V’s of Big Data - impact.com (4)

1. Volume

Volume defines how much data we have – what we used to measure in Gigabytes is now measured in Zettabytes (ZB) or even Yottabytes (YB). The Internet of Things (IoT) creates exponential growth in data. Projections show the volume of data changing significantly in the coming years.

2. Velocity

Velocity represents the speed at which data is processed and becomes accessible. Today, if delivery is not real-time, it’s usually not fast enough.

3. Variety

Variety describes one of the biggest challenges of big data. The insights may come without structure. The total asset may include many data types, from XML to video to SMS. Organizing the data in a meaningful way is no simple task when the data itself changes rapidly.

4. Variability

Variability is different from variety. A coffee shop may offer six different blends of coffee, but if you get the same blend every day and it tastes different every day, that is variability. The same is true of data. If the meaning constantly changes, it can significantly impact your data hom*ogenization.

5. Veracity

Veracity ensures the data is accurate, which requires processes to keep the insufficient data from accumulating in your systems. The simplest example is when contacts enter your marketing automation system with false names and inaccurate contact information. How many times have you seen Mickey Mouse in your database? It’s the classic “garbage in, garbage out” challenge.

6. Visualization

Visualization is critical in today’s world. Using charts and graphs to visualize large amounts of complex data is much more effective in conveying meaning than spreadsheets and reports chock-full of numbers and formulas.

7. Value

Value is the end game. After addressing volume, velocity, variety, variability, veracity, and visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.

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The ultimate guide to partnership marketing

Discover how you can tap into the power of partnership marketing and harness its benefits, like increased revenue, brand awareness, and conversion rates.

Get my free guide

Deciphering data with the right partnership platform

Brands’ main challenge is digesting the big data available to make sense of the data and identify actionable insights. Visualization allows partnership managers to highlight patterns and outliers, saving time and making sharing insights with internal stakeholders easier.

Many brands explore partnership platforms to manage their program to unite their partners and automate the six stages of the partnership life cycle.

While automation is critical, brands also need a platform to function as that single source of truth that can provide all seven V’s of big data. Investing in a single, easy-to-understand partnership platform, like impact.com, helps eliminate siloes and unites all data to better understand your program’s performance.

The impact.com platform provides an extensive library of performance reports where brands can view data and create customizable reports. With tools like the impact.com Data Lab, brands create visualizations that tell compelling stories about the data, answer unique questions, and provide valuable insights. Request a demo to ignite growth through data with impact.com today.

Expand your knowledge of partnership marketing with these informative impact.com resources

  • What’s the channel hotter than paid search? [ebook]
  • Best practices for optimizing your partnerships [ebook]
  • Gain a deeper, wider perspective on partnership performance with Optimize reports [one sheet]
  • Impact Data Lab: Discover insights, build reports, and share your data story [one sheet]

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The 7 V’s of Big Data - impact.com (2024)

FAQs

The 7 V’s of Big Data - impact.com? ›

After addressing volume, velocity, variety, variability, veracity, and visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.

What are the 7 V's of big data? ›

The Seven V's of Big Data Analytics are Volume, Velocity, Variety, Variability, Veracity, Value, and Visualization.

What are the important V's in big data? ›

The 5 V's of big data -- velocity, volume, value, variety and veracity -- are the five main and innate characteristics of big data.

What are the 4 V's that impact the creation of big data? ›

There are generally four characteristics that must be part of a dataset to qualify it as big data—volume, velocity, variety and veracity.

What is the most important V of the big data issue? ›

Data veracity is the one area that still has the potential for improvement and poses the biggest challenge when it comes to big data.

What is big data and its importance? ›

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

What are the 17 V's of big data? ›

The study of the 17 V's and 1C (volume, velocity, value, variety, veracity, validity, visualization, virality, viscosity, variability, volatility, venue, vocabulary, vagueness, verbosity, voluntariness, and versatility) identified characteristics is anticipated to offer simple and efficient management of big data that ...

What are the 8 V's of big data? ›

The 8 V's of Big Data

First, there were the 3 V's of Big Data – volume, velocity and variety. Then, there was an expansion to include 3 more – veracity, variability and value. Gurucul has since expanded the list to include two more descriptions – venue and vector.

What are the 10 V's of big data? ›

The 10 Vs of big data are Volume, Velocity, Variety, Veracity, Variability, Value, Viscosity, Volume growth rate, Volume change rate, and Variance in volume change rate. These are the characteristics of big data and help to understand its complexity.

What are the main components of big data? ›

The three major components of big data are: Volume (large amount of data) Velocity (high speed of data generation) Variety (diverse data formats)

What are the 9 V's of big data? ›

Big Data has 9V's characteristics (Veracity, Variety, Velocity, Volume, Validity, Variability, Volatility, Visualization and Value). The 9V's characteristics were studied and taken into consideration when any organization need to move from traditional use of systems to use data in the Big Data.

What are the 6 V's of BigData? ›

Six V's of big data (value, volume, velocity, variety, veracity, and variability), which also apply to health data.

What are the 11 V's of big data? ›

So, researchers and practitioners have explored the big data in terms of volume, velocity, variety, variability, velocity, variety, value, virality, volatility, visualization, viscosity and validity [10].

What are the 5 P's of big data? ›

In this article, we define the 5P of D&A measurement, i.e., purpose, plan, process, people and performance. These rules can help enterprises in measuring business outcomes in a reliable manner, avoid some of the common mistakes and achieve better business outcomes.

What is the biggest problem with big data? ›

Storage. With vast amounts of data generated daily, the greatest challenge is storage (especially when the data is in different formats) within legacy systems. Unstructured data cannot be stored in traditional databases.

Which V of big data is concerned with the usefulness of data? ›

Value refers to how useful the data is in decision making. We need to extract the value of the Big Data using proper analytics.

What are the 8 V's of data? ›

The 8 Vs begin from the volume of data to be processed, the velocity at which the data is processed, the variety of the data that is processed, the viability of the data to march with the reality, the value that the data holds to eventually help the customers, the veracity and the trust factor of the data, the validity ...

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