Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data? Some data management platforms (DMPs) have over 900 million consumer profiles globally with 10,0000 different data points associated with it. To get even close to that amount of data from consumer surveys, you’d have to run about half a billion surveys.
So while big data has been most impactful to programmatic advertising and media, it also provides a lot of opportunity for other marketing efforts and market research. Due to the insight it provides into consumer behavior, as budgets continue to shrink and the speed of decision making continues to increase, big data is necessary.
The 4 V’s of Big Data
Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, incorporating big data is a marathon, not a sprint and companies have to take the right steps before making the leap. If you’re not already using big data, before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:
- Volume: the amount of data available
- Variety: the different types of data
- Velocity: how frequent, real-time, or up to date the data is
- Veracity: how accurate and applicable the data is
The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. In other words, veracity helps to filter through what is important and what is not, and in the end, it generates a deeper understanding of data and how to contextualize it in order to take action.
Data Veracity: The Most Important “V”
Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is. Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.
Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media data, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.
The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and without a method of identifying what data points are relevant. As a result, the velocity of data and agile methods come into play here — big data should be analyzed in a timely manner, as is difficult, otherwise the insights would fail to be useful.
Big data is highly complex, and as a result, the means for understanding and interpreting it are still being fully conceptualized. While many think machine learning will have a large use for big data analysis, statistical methods are still needed in order to ensure data quality and practical application of big data for better marketing activation. For example, you wouldn’t download an industry report off the internet and use it to take action. Instead you’d likely validate it or use it to inform additional research before formulating your own findings. Big data is no different; you cannot take big data as it is without validating or explaining it. But unlike most market research practices, big data does not have a strong foundation with statistics—luckily, integrating it with survey data can help.
Integrating Big Data With Survey Data for Market Insights
While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the velocity of big data, can help to analyze and connect the dots — easier said than done.
The obvious benefits of using big data in marketing includes gaining a better understanding of people, content, and media. By combining big data with survey data, you can identify a market opportunity, understand your target audience, and incorporate findings into your messaging and creative execution. Infusing survey research with big data also means the volume of questions that need to be asked are reduced as big data provides more answers. So our understanding of consumer behavior is going to grow exponentially over time as we bring these two worlds together. The ability to incorporate big data to use fewer questions can also deliver more speed and value.
The result is unique audience intelligence. The benefits of this approach mean removing the guesswork during activation, letting the audience identify the opportunity for you, creating more effective messaging, and ultimately increasing value on ad purchases. There are certainly challenges to infusing survey research with big data before organizations can reap the benefits. Since DMP’s were originally meant for advertising, they haven’t been made research-grade, so oftentimes there are errors or inconsistencies in the data related to an audience. However, there are solutions out there — and more to come for sure — that will be able to overcome the challenges and provide an accurate depiction of audience insights through big data.