In today’s landscape, thanks partially to the changing sentiments from executives but also to the need to keep up with competition, everyone across an organization - no matter what team (s)he’s a part of - wants to be data-driven. Yet the harsh reality of the logistics and hurdles required to make data-driven decisions can block teams from really becoming part of a company’s data culture.
This can be especially true of marketing teams in particular, who often face internal silos and blockers along the path to adopting a data-driven culture that prevent or discourage their efforts.
Our experience at Dataiku supports these findings; our prospects and customers often tell us that they struggle to optimize their marketing efforts. Yet the core of the issue isn’t actually solving the problems the marketing team faces; these are relatively straightforward predictive analytics cases (e.g., customer churn or model-based segmentation).
The larger issue at hand is this: how can companies and data teams stop the cycle of addressing single, one-off issues that other teams face and instead empower them to solve these issues themselves?
Fresh Data On Demand
Business, marketing, and BI teams need to be able to build and view reports without regenerating complex and expensive queries on SQL databases. Even if there is someone on the team with SQL skills, having a single point of failure and not involving everyone on the team in the data process is a mistake. Data centralization is the first, and perhaps most important step, in empowering a marketing (or any other) team to drive their own optimization with data.
Empowering non-data teams to drive their own optimization with data is key to scalability.
BlaBlaCar, an online carpool booking service, was able to empower their marketing team to optimize customer lifetime value by actually putting the necessary data at the fingertips of the team.
Trust Teams to be Active Data Consumers
In the past, marketing teams were considered data driven if they had access to a dashboard that they could use for limited analysis of past data. Today, it’s not enough - qualifying data as it comes in real time and allowing teams to do dynamic analysis on that data (not just be passive consumers) is the only way to scale a data team. Allowing, empowering, and trusting teams whose primary role is not managing the company’s data per say to come up with innovative data solutions should be one of the central data team’s primary goals. For example, Coyote, who works with real-time road information, tackled their data management problem in order to see lift in other parts of their organization (like outbound marketing campaigns).
Make Time Savings a Goal
Data teams should always be concerned with saving time. Not just for the marketing team or other teams that have requests, but for themselves. It’s the old “teach a man to fish…” adage - teams that are empowered to solve their own problems means more free time to tackle larger projects with big business impact (and, bonus: more notoriety).
Data teams should always be thinking about saving time on repetitive tasks to take on larger projects.
Trainline was able to build a global view of real time data for their marketing team, and because of it, they’ve multiplied the efficiency of their data engineers by three.
For more insights on how exactly to build a data culture within your company aside from empowering teams in the ways mentioned above, watch How to Become a Large-Scale Data Innovator from the Data Innovation Summit 2017:
Of course, it’s not entirely the responsibility of the data team to empower the marketing team - marketing teams can meet them halfway. Check out our guidebook on advanced analytics for marketers to get more insights on leveraging machine learning to start feeling more comfortable exploring the world of predictive analytics. Then, once you’ve been empowered with the data, it’s all downhill from there!