Implications of Big Data Evolution on Marketing
In this special guest feature, Jordan Cardonick, Director of Media Analytics at Merkle, discusses the common pitfalls and missteps that companies fall into when trying to democratize data and insights for their company. It isn’t just about what data you have access to, but how do you ensure it is effectively communicated and embraced and not just how to use it to create a chart, graph or model. Jordan has spent the last decade of his career transforming data and information into actionable and achievable insights for world-class brands, totaling over a quarter billion dollars in spend. While primarily focused on digital media, he and his team combine that knowledge along with marketing savvy to help grow the accounts that they support. Jordan has an undergraduate degree and MBA from the University of Pittsburgh, but still remains a dedicated Philadelphia Sports fan.
In this new data-first environment, companies are diving head first into artificial intelligence (AI), machine learning, data lakes, and cloud-based solutions to drive their marketing efforts, while they hire every resume that contains a Data Scientist title. But these organizations need to do some self-reflection to ensure they are capable and ready to not just ingest the mountain of information that is becoming increasingly accessible to them, but make it accessible to those that need it most i.e. those that use the data to make informed decisions. The evolution of big data has led to an ability to provide insightful, comprehensible information for stakeholders to act upon. While the technology is now available to crunch through millions of records of data in mere seconds and visualize it in an appealing way, it is still going to be up to smart, business-savvy minds to craft the story that delivers true value to the business, as well as the consumer. All the models and reports that can be built will not result in successful people-based marketing, unless there are analytically literate individuals who can translate those numbers and data points into powerful insights.
The concept of big data has been around for some time. But its current role in solving core marketing problems emerged when search, social, display, and other forms of digital marketing started to mature with respect to the data that could be captured across channels and media. The ability to predict “right customer, right message, right time” seemed finally at the tips of our fingers. The issue now was the technology. We weren’t quite ready to manage and leverage this influx of data. Data storage was relatively cheap, but processing power was lacking, and we couldn’t easily and quickly access and structure this data. The cost of purchasing additional servers may have been feasible for larger firms, but middle-of-the-road players and smaller shops did not have the capital to invest in bringing their capabilities up to snuff. It seemed as if all this data was simply a tease to bigger and better things.
Within the last few years, though, companies like Google and Amazon began to open their vaults and make available the technology that powered their organizations. And many of the other larger technology providers quickly followed suit. These cloud-based platforms also enabled open source tools like R and Python to tackle these massive data sets and start developing models and deriving insights. BI tools such as Tableau and DOMO, while already growing in popularity, became even more valuable to start visualizing all this rich information. Everything seemed to be falling into place, except that companies couldn’t facilitate the sharing of this information in a way that was conducive to running the business.
