In our last article we discussed how the advent of IoT is bringing marketers an overwhelming amount of data, behemoth data, that can be synthesized into usable knowledge that can drive more effective customer journeys. With companies having access to all of this data, we’d like to talk more about how this data can be optimized and utilized to have the largest impact on your organization.
Beware the overzealous that want to board the big data train too quickly, although they have the very best of intentions. The same “bad data in – bad data out” (incorrect insights or conclusions) rule holds just as true, if not more so, in the world of big data analytics compared to traditional statistical analytics. Big data is compiled from an ever growing number of sources, much of which is unstructured. And simple rules of probability apply here – the larger the pool of data, the higher the likelihood that analysts will miss “dirty” data that can ultimately lead to identifying false positives or false negatives.
Unlike traditional first party data that historically has lived in relational databases, big data often consists of a tremendous amount of unstructured data. Correctly integrating and/or blending this data with more structured first party data is critical so as not to lead to analytic outcomes that are way off in left field. This problem is only exacerbated by the velocity at which data is created, which can largely be attributed to the growing mobile trends discussed earlier where data is transmitted on almost a continuous bases. Also, keep on the lookout for the increasing trend of automobiles being online, yet another massive pool of data generating “devices”. To help ensure that the “signal” can be correctly extracted from the “noise”, it is critical that the appropriate amount of rigor is put behind understanding the quality of the data source, how that data is collected, and how it is integrated and blended with other sources of data.
Despite the value of big data synthesized to be used effectively, there is also extreme value in small data – data that’s about people and emotion (in addition to small datasets gathered from a singular historical event). Small data can be ingested into big data sets, merged with behavioral or trending information derived from machine learning algorithms, and provide clearer insights than we’ve ever had before.
Here’s an example of both: The use of smart labels on medicine bottles is small data which can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling. Is it due to a certain shipping company or a certain retailer? Are there reoccurring patterns that can point to problems in the supply chain that can help determine how to minimize these events? 1
The issue here is that we cannot become so obsessed with Big Data we forget about creativity. You have to remember that Big Data is all about analyzing the past, but it has nothing to do with the future. Small Data, can also be defined as seemingly insignificant observations you identify in consumers’ homes. Things like how you place your shoes to how you hang your paintings. These small data observations are likened to emotional DNA that we leave behind. Big Data is about finding correlations, but Small Data is about finding the causation, the reason why. 2
Optimizing Big and Small data into business processes can not only save companies millions of dollars, but creates a buyer and customer journey that are seamless, continuous and maintains context regardless of the touchpoint. This omnichannel marketing approach should be the ultimate goal of marketers – creating a conversation with their buyers and customers based on trust and value exchange – which leads to strong relationships in an increasingly connected on- and off-line world.
Laura Watson is Strategy Director at Harte Hanks, and Korey Thurber is Chief Analytics & Insights Officer at Harte Hanks. Harte Hanks can help your brand create an omnichannel marketing strategy, contact us for a free assessment.
1 Forbes Tech
2Small Data: The Tiny Clues That Uncover Huge Trends
About the Author
Laura Watson, Director of Strategy, has 16 years of marketing strategy experience in the financial services industry, managing direct and digital marketing teams. Passionate about integrated, omnichannel marketing, Laura combines strategic insight with results-driven methodology to help clients prove the incremental value of their marketing efforts. In her spare time, she enjoys helping with her family’s winery and fifth generation maple syrup business.More Content by Laura Watson and Korey Thurber