A lot has been written about the V’s of Big Data. While beneficial in describing the technical challenges in managing and processing Big Data, they neglect the needs of the business leaders. As technologists, we often fail to consider the need to present business benefits to our business counterparts. This was my reason for creating the 5 R’s, so that business people will be able to understand and relate to the complexities and value Big Data brings to the enterprise in terms to which they can relate.
Let’s be honest, Volume, Variety, Velocity, and Veracity are not business words and in this context even Value does not relate in business terms to items accounted for in a corporation’s annual report like Brand Recognition and Goodwill. Business people understand the V words, but not always how they relate to the business. V's are technical issues, not business issues. As technologists, we need to speak in a language business leaders will understand and therefore be willing to fund the projects that address their drivers. Think of the V’s as one side of the coin and the R’s as the other.
Relevant - (Data Fit) Disambiguation of the incoming data with existing enterprise data – define what is potentially relevant and useful to the business outcome
This refers to Signal-to-Noise and it is a problem for every business. How do we distinguish between what is important to us to drive better business insights, verses distractions that lead us away from critical business insights? Technically, we are hindered by Volume and Veracity. We can apply various tools to sift through the noise to better focus the signal.
Real time - (Data on Time) Accelerating time-to-value from data creation to data usage
The #1 complaint I hear from businesses is I need the data and I need it now! V's that stymie us are Volume, Velocity and Variety. As source systems generate data we need to process it, extract and transform it. The higher Volume, Velocity and Variety these three Vs are, the greater the latency in getting useful data to our customers.
Realistic - (Data Insights) Data acquisition, analytics processing and appropriate data skill sets support the defined business use case
The business must be able to extract the business insight needed to affect business outcomes. Veracity and Variety can give the business the confidence that the data is accurate and from sufficient varied data sources to derive critical business insights.
Reliable - (Data Quality) Data quality is critical to the reliability & efficacy of the result sets – strong data quality measures correlate to good results
While directly addressed in the V's, the quality of data is predominantly impacted by Veracity. However, I feel this simple relationship is inadequate. The R’s are not intended to have a one-for-one relationship with the V’s. While managing your data processing systems, quality matters and we should consider that more data is not always better. Knowing when to place limits on Volume, Velocity and Variety is as important of a consideration, as to when to include data.
ROI - (Data as an Asset) Effective management & analysis of the data enables better business decisions, thereby maximizing the return on investment (ROI) of your data processing system
By understanding the Value potential of your data, you can calculate ROI. It is my professional opinion that the ratio of ROI to Value should be equal to or less than 1. If you Value your data assets at $10 million, you can’t expect an ROI of $20 million. If you have, you’ve incorrectly Valued your assets. Year-to-year your ROI to value may be off slightly, but demonstrating to the business you have a firm understanding of the R’s of their business and the V’s will win over your business leaders.
Amber E. Watson at Concurrent Inc. did well addressing the business aspect when she wrote, “Organizations must determine the best way to collect massive amounts of data from various sources, but the challenge lies in how that data is managed and analyzed for the best return on investment (ROI). The ultimate goal for organizations is to harness relevant data and use it to make the best decisions for growth.” To grow a business is to increase its net worth or… Yes, that’s right. It’s value and a business’ ability to generate the highest Return on Investments possible. Simply put, whether you are processing wood, steel, food or data you're doing it to enhance their value. In this context, processed items have an intrinsically higher value once enhanced via the processing of the raw inputs.
I tried to correlate the V’s & R’s and to show how they are tightly knitted together in a Yin and Yang relationship. While not perfect, the R’s should make it easier to articulate the importance of what is happening in the Big Data space to both technologists and business leaders. At the end of the day, we share the same goal and that is to move the enterprise forward. Speaking in terms that enhance our business counterparts understanding is always a good thing.