Last summer, my son and I were assembling our humble campsite in the midst of the Cascade mountain range. Having conquered the battle of Friday rush-hour traffic, I asked my son to manually break apart three large pieces of fallen wood for our campfire. I suggested to try banging the wood against a tree trunk — a task to occupy his car-cooped spirit and offer me 15 minutes to sort the remains of our site. Yet two minutes later, he called me, smiling proudly with a pile of perfectly-sized firewood at his feet. Stunned, I demanded the secret to his efficiency. He nonchalantly explained that the wood was too big to snap on a tree trunk, so he found something bigger and heavier to drop onto the wood. He demonstrated by picking up a large rock and releasing it on top of one of the wood pieces which, as predicted, split perfectly in two. Why do I tell this story? Read on.
The Winterberry Group recently published a white paper entitled The Data-Centric Organization: Transforming the Next Generation of Audience Marketing. The IAB’s Data Center of Excellence and DMA co-sponsored the study summarized in the white paper, which included responses from over 200 executive thought leaders. The study offered a holistic lens into the current and projected states of data usage within enterprise-level marketing organizations.
Its conclusion: we are in a period of transition.
The findings of the study confirmed that enterprises are delighted by both the real and potential value of data. While its prospects are undeniable, the path to enlightenment is not absolute. Despite near consensus on the need for data-driven decision making, nearly half of study respondents have developed an overarching data strategy that has not yet been implemented and almost three-quarters of respondents acknowledge that their organizations are not yet optimized to promote the use of data. A mere 2.7 percent of study respondents are “extremely confident” that their organizations optimally use audience data.
While these numbers hint toward the volatile state of data consensus, there is a bright side. About half of study respondents think they will benefit from a deeper data relationship with their suppliers and a full majority expect to “lean heavily on partners” for help with data solutions. There’s a universal recognition of the need to break down organizational silos and reconcile disparities in skills, goals, and processes to form a cohesive vision that will lead to becoming a data-centric organization well into the coming years.
The Typical Approach to Data is a Zero Sum Game
Our traditional instinct to wrangling data is to create a data warehouse — a centralized repository of process-driven events and circumstances. From consensus on resources and technology to process definition, output and consumption, deploying a data warehouse is much more complicated than the name suggests. There’s a process of expectation-setting when scoping out a warehouse initiative, because no matter how easy your problem is to define, the path from consensus to consumption is paved with good intentions.
A data warehouse brings us just close enough to operations to report on happenings and plan for the predictable but it’s too restrictive to truly facilitate innovation– a certain proximity without intimacy. Consider an industrial complex built from a legacy mindset where everything has to be defined, aligned and conformed in order to be considered complete, evolution and agility notwithstanding.
Does this mean that if you are 16 months into a 36-month long data warehouse initiative, you should abandon the initiative? Of course not. But there’s a complementary process you can layer on top of your data warehousing that allows you to mix disparate data sets, automate processes like clustering and fraud detection and facilitate advanced analytics.
Data Needs Intent to Have Value
The responses in the Winterberry study confirm that the greatest data-driven value of marketing and advertising lives in customer segmentation, campaign attribution, and operational efficiency. Despite the functional independence of each objective, a majority of study respondents also agreed that dissolving functional silos would represent the most important step their organization could take toward becoming more data-centric.
When asked, I always reply that my preferred tool for any project involving data is the one in front of me. Would I prefer a freshly-sharpened axe to chop wood for my campfire? Sure. But if throwing a big rock at the wood achieves the same goal, I’d rather get the job done right now.
This leads us to the information repository, more commonly known as a “data lake”. Information repositories help direct the goal of today in sprints of incremental achievements versus a holistic legacy makeover. In this, the data structure and requirements are not defined until the data is ready to be extracted or interpreted. Whereas data warehouses structure and normalize the data ahead of time, a data lake ignores the disconnect. A data warehouse is the iOS to a data lake’s Android — a beautiful prison vs. a messy playground. A beautiful prison is perfect if neither flexibility nor agility is an issue for your business questions. A messy playground is best when your data is high volume, high velocity and requires continuous innovation.
Now consider the compelling data stories that emerge with intent. The Trendsetters. The Outliers. The Frauds. The Future. Whatever the story becomes, the search for meaning is a messy path. In order to truly become a data-centric organization, we have to embrace the mess with deliberate intent. Let the disparate streams course through our veins as we accept that all problems cannot be solved with the same tool.
Carpe Datum
The adoption of data strategy faces a period of growing pains and challenges, though the industry is well aware of its need to evolve. The clear, current demand for skillful guidance and skilled guides throughout the evolution of data is evident in the sincerity of study panelist responses. Through advocacy, education and standards, Valassis looks forward to helping the industry set the vision and best practices for applied data insight.