In 2004, Steve Jobs introduced an unheralded app to the Apple family, GarageBand. Jobs promised, and delivered, a revolution in music creation and engineering. He knew that Apple stood at a significant moment when new technology would open up new industries to innovation and rapid change.
As Jobs put it at the time, “[GarageBand] turns your Mac into an anytime, anywhere recording studio packed with hundreds of instruments and a recording engineer or two for good measure.” GarageBand was a catalyst in fundamentally changing the dynamic of the music industry by giving talent access to innovation, which moved power away from the traditional studios and publishers. The democratizing of access to music engineering and then publishing, see iTunes, rebuilt the foundations of the music industry today.
Public affairs is at the precipice of its own moment of transformation with access to big data, artificial intelligence and machine learning. These tools have already transformed marketing, advocacy, political campaigns and journalism by enabling them to have a detailed understanding of how an audience or subscribers are reacting to and using their products.
These advancements in adjacent businesses make public affairs seem like a quaint corner of communications, the last makers of the finest of buggy whips. Those public affairs leaders who think this wave is not coming and overly rely on the storied traditions of advocacy and communications are making the same mistakes that Steve Jobs and Apple exploited to create their own revolution.
Data, data everywhere
The starting point is the vast amount of useful data that is generated from public affairs and policy campaigns that are not being used. This includes streams of digital data from online, website and ad campaign interactions, coupled with data from events, meetings and other offline interactions. If we could harness this data, to bring it together from across a myriad of online and offline interactions, it would help us understand how policy decisions are made and how to pull the levers of influence.
At Signal, we are starting to collect this data in ways that allow us to assess the impact of our advocacy and strategic communications campaigns. We use tools including data harvesting, machine learning, elements of artificial intelligence and other data science techniques that are leading to new insights on how to build and execute smart campaigns.
For example, most recently, we worked with a data science team to build a tool, the Signal Tracking Module, that enables us to create customized attribution between a target audience, i.e. a Senate committee or key stakeholders, and their online engagement across multiple digital channels: digital ads, websites, event invitations or emails. We are also pushing to move offline interactions into our data models. We are planning to launch a set of mobile-based applications that would enable our advocacy and communication executives to easily and simply record their face-to-face — or monitor-to-monitor at the moment — interactions with policymakers.
These tools are just the beginning in using new techniques to capture and organize interactions and relationships across our work. The resulting data forms a rich soup of information that enables us to see patterns and gather new insights to provide evidence of impact and engagement. By bringing all of the data streams together, we can use data science tools to perform analysis that was not possible a few years earlier.
Public affairs’ Garage Band moment
What makes this possible is the steady democratizing of data science technology and affordable access to powerful computing and vast cloud-based storage.
What Jobs did in 2004 was take the highly engineered tools of music recording and engineering and reproduce them in a cost-effective way with a reasonable fidelity to the high-end equipment. The same revolution is happening in data science. When I worked at The New York Times, I saw the finest of data science teams in the news industry at work. When I moved to The Atlantic the work was just as sophisticated, but the team was half the size.
We have been investing in analytical talent, building agile digital products and building partnerships with innovative data science teams. But the whole industry stands to benefit from talent moving out of fields of business intelligence and marketing and looking for new spaces to conquer and new data science tools that have a low barrier to access, but still retain powerful analytics.
A final note about truth
I have experienced this revolution in journalism and media and seen its transformative power. However, I also witnessed the immediate antibodies push back on change and innovation. What those contentious times taught me is that there must be a balance to innovation.
One of the main obstacles has been an overconfidence in the value of data science as understanding human behavior as compared to hard-won knowledge and experience. Over time, I have seen that data is not necessarily “truth,” but is a powerful set of lenses to see relationships and systems in new ways. Data science is a new tool that enables experience to be buoyed by quantitative insight, or thought of in another way, as the distillation of tens of thousands of experiences in a single second.
We are at the beginning of this journey — I suspect we will always be at the beginning — as insights, tools and ideas radically change our ways of communicating and interacting. Because we are pushing ahead into the future, we hack our way forward creating best practices and learning along the way. It is a challenge, but one where we feel we are on the edge of true innovation.
By not embracing the potential of data and data science, public affairs professionals are leaving extraordinary value to their profession and clients on the table. There are pockets of change all across this field, and we hope to work collaboratively with our fellow practitioners.
Is it a “Garage Band moment,” so let’s all grab an instrument and play.
Robert Bole is MD at Signal Group.