Variety describes the different types of data, which is expanding every day. Structured data that has been commonly stored in databases is now joining unstructured data, including social media information, wearable data, and video streaming.

Volume is the scale of data, how it’s acquired and stored. The volume of data is expected to increase from 2.3 trillion gigabytes of transfer today, to over 43 trillion by 2020.

Velocity is the speed of data processing and Veracity is the uncertainty vs. the reliability of data. IBM reports that poor data quality costs the U.S. economy over $3 trillion dollars a year.

Value is how to make data profitable by using analyzed data to increase revenue and decrease cost.

The variety and volume of data that is being passed through the networks is staggering which effects the velocity, or how quickly technology and organizations can break down and analyze the entire overload of information that’s collected. We need to use efficient network connections, monitors and sensors to map out behavioral patterns and applications to shape these patterns. And, the bottom line with all of these V’s is Value: monetizing on Big Data.

There is no ownership over the Big Data ecosystem.  It’s constantly changing and legacy systems are having a hard time playing catch up. And it’s no longer just data.  It’s also the analysis of the data which is critical as well. Long gone are the days when the most effective strategies that emerged from the operators’ IT departments loaded up on new platforms, systems and  “critical” functionality that were often underutilized, or even the worst yet, not fully deployed. Operators must now focus on making their infrastructures “lean and mean” and be ready for innovative technologies on the horizon. 

With the Big Data forecast for 2025, we need to respond, sooner rather than later, with systems and processes that make this data manageable and able to be monetized. Within every industry, everyone is asking the same question: how can we make Big Data profitable? As with any new technological paradigm, there are hurdles. The biggest challenges to monetizing Big Data can be divided into three silos: business, technology and regulatory.

Business Challenges

On the business side, we need to focus on analytics, accurate forecasting, adopting new tools and technologies, and getting real-time insights. We need to determine how we can gain profit from something that costs time, money and resources to keep up with.

1. Business analytics: We need to use analytics to increase our operational intelligence and measure whether it’s working and who it’s working for.
2. Accurate forecasting: Using these analytics to make reliable inferences about the future of the market. We know what we did, so how can we do it better?  What’s next for our demographic? What can we infer that our demographic will respond to?

Real-time insights are the key to making sure that the analytics and forecasting are worth it.  Time is essential.  Companies want to know what’s going on immediately so they can figure out how to respond accordingly. Big Data can do this.