The field of professional communications has been evolving for the last 100 years. When it began, with the early work of Eddie Bernays and Ivy Lee, the focus was largely on promotions. This larger communications field split into several over the years. One can argue that public relations, marketing, advertising, investor relations, etc. are all branches of the same communications vine.
Marketing, advertising and to a certain extent, investor relations, had an edge over PR in terms of institutional prestige was a more easily provable cause-effect relationship between actions taken by practitioners and business impacts. For example, marketers make a plan to generate leads and move them through a funnel – a fairly straightforward effect to test empirically.
Public relations dealt in relationships, reputation and brand image for which there was no real established currency, at least until very recently. Historically, the fact that PR was often populated by journalists who crossed the road, made it such that measurement and business fundamentals weren’t necessarily at the centre of how the practice was structured.
With the advent of Grunig’s Excellence Study, the business case for PR started to be made and academic PR programs started to incorporate business, finance and management fundamentals into the strategic lexicon of the field. Master’s programs such as the MCM I teach in at McMaster and Syracuse Universities were built on the premise that business knowledge was a key element for PR to regain the institutional prestige it once enjoyed.
Thus, until recently, the two pillars of public relations and communications management were Creativity and Business/Management Knowledge.
I am of the opinion that there is now a third necessary pillar of the profession: data science. Until now, there were few tools that enabled a broad audience to quantify concepts such as “relationship”, “reputation”, and “brand image”, as well as their components (“trust”, “credibility”, etc.).
Today, we see a proliferation of tools that enable even the less scientifically literate among us to construct experiment-informed communications programs. We are getting to the point of establishing reliable and more finely-grained indices for reputation and relationship that are needed to build solid industry-wide KPIs, metrics and analytics.
Knowing the language of data science has become a key literacy for PR: even if it only to enable you to evaluate pitches or to work with a datasci expert to make your pitches and campaign evaluation more data-driven. Indeed, data-driven decision making has come to PR and we need to get on top of it!
Just as the 1990s introduced the importance of knowing the language and priorities of finance, management and business; so will the 2020s make the language, methods and fundamental concepts of data science a must-know for PR pros.