Ready to get your agency data-enabled for the new decade?
In this post Jonathan Hart, Director of MullenLowe Group’s Data Science & Analytics Centre of Excellence, provides essential navigation for 8 distinct marketing career tracks.
At the close of this decade, the excitement about data science has reached a fever pitch. What has been the biggest catalyst for change over the last decade, promises to continue to reshape our industry and our careers in ways that are still unimaginable today. Whenever I present on this topic, I am greeted afterwards by a long line of hungry minds looking to understand how to incorporate data science into their work, yet unsure how to begin.
To be blunt, there are no good resources for introducing agency folks to data science. Few, if any agencies, have really found a way to realize the promised benefits and as a result the roadmaps and training materials commonly available in other industries are absent. There are a multitude of thought pieces and conference sessions on the topic but they tend towards theory and are not driving fundamental changes in our approaches, so I believe something more is required.
In my experience, the best way to understand new ideas is to work with them hands-on. Perhaps this is my bias as a kinesthetic learner, but when used properly, data science creates a fundamental pivot in the way we think about many conventional approaches which begs time and focus.
What I have attempted to do here is identify which ‘parts’ of data science are most relevant to each job function and curate a set of resources that focus on those areas. To make the curricula as accessible as possible, I have tried to select courses which are free and don’t require prior math or programming knowledge.
Client Partners
Key Topics
- Guiding clients into making evidence based decisions
- Understanding how to create new revenue streams with data science
- Creating competitive advantage through new capabilities
🖥 ️Data-driven Decision Making by PwC
📚 Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel
Strategists
Key Topics
- Shifting the mindset to use data as a means of insight discovery rather than validation- Understanding new methods that can be used to learn from existing data
- Identifying where there are knowledge gaps and new data needs to be created
🖥️ Customer Analytics by Whorton Online
🖥️ Machine Learning for Business Professionals by Google Cloud
📚 Connect: How to Use Data and Experience Marketing to Create Lifetime Customers by Lars Birkholm Petersen
Marketing Researchers
Key Topics
- How to structure research so that it can be properly analyzed
- Generating segmentation from patterns inside the data rather than explicit filters
- Drawing conclusions that are statistically accurate with high confidence levels
🖥️ Data Science Research Methods: Python Edition by Microsoft
🖥️Applying Data Analytics in Marketing by University of Illinois
📚 Content analysis: an introduction to its methodology by Klaus Krippendorff
Project Managers
Key Topics
- Detailed knowledge of the various types of roles and skills necessary for data projects
- Overseeing data work with management processes built for creative development
- Understanding enough technical detail to manage scopes and timelines
- Adapting to managing scientific thinkers
🖥️ Executive Data Science Specialization by Johns Hopkins University
📚 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost
Creatives & Experience Designers
Key Topics
- Understanding how design choices influence what data is being created.
- Learning how to purposefully create data though design
- Effectively making trade-offs between design best practices and creating data
🖥️ Machine Learning for Business Professionals by Google Cloud
📚 Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll
Media Planners & Digital Analysts
Key Topics
- Designing and running statistically valid experiments.
- Creating data-driven attribution models.
🖥️ Experimentation for Improvement by McMaster University
🖥️ Marketing Analytics by University of Virginia
Social Media Managers
Key Topics
- Understand how topic and sentiment analysis work
- Perform custom analysis of social media data
- How to understand social networks
🖥️ Social Media Data Analytics by Rutgers
🖥️ Applied Social Network Analysis in Python by University of Michigan
📚 Python Social Media Analytics by Michal Krystyanczuk, Siddhartha Chatterjee
Chief Executives & Managing Directors
Key Topics
- How to create data-driven organizations
- Understanding the disruption and opportunities being created by data science
- Formulating a perspective on the role of data in a creative company
🖥️ Introduction to Machine Learning for Data Science by The Backyard Data Scientist
📚 Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb
Typical agency approaches to data are centered on standing up AdTech or MarTech solutions, using tags to gather data and then generating reports. Those further along may be leveraging staff members who have an affinity for data or an intuitive understanding of those platforms to find insights and build narratives around them.
However, knowledge of a platform and ability to manipulate data should not be confused with knowledge of data. Without proper training in these, analysis is likely to beg the infamous question ‘where are the insights’?
Good insights come from good questions and good questions come from knowing what is possible with data that has been properly analyzed. My hope is that, with the resources above, we will be positioned to ask better questions, get better insight and deliver more novel solutions for our clients.
If you want to learn how to data-enable your career and use data to create unfair value for brands and their customers, you might well enjoy working with us: https://japan.mullenlowe.com/careers/
