The PR Big Data Blog Series Part III – Big Data: The Balance of Talent and Technology


The Strategic Research Conference: Insights to Action, hosted by the Institute for Public Relations and PRIME Research on June 9th in New York City, prompts this third installment of a blog series on Big Data and its impact on public relations. 


By Mark Weiner, CEO, PRIME Research – North America

Sarab Kochhar, PhD, IPR’s Director of Research

The public relations profession is evolving, due in part to the Big Data revolution that makes data analysis essential to communications planning, strategy development, evaluation and continuous improvement. To learn more about this revolution and Big Data, read Parts One and Two here and register for the Insights to action conference in June.

What makes Big Data “big?” Typically, it is characterized by a data set too large to be managed, captured, and processed by common business software. At the same time, Big Data is comprised of many “Small Data” streams, of which public relations data is but one example. In these small data streams, organizations import and analyze data from the three main sources of Big Data — internal, shared and external data streams — to improve business decision-making and strategy development.

As organizations collect more and more information, the volume of data collected grows exponentially, making it difficult for some to determine how the data should be handled, what is most important, and how it should be managed.

Big Data, while offering great advantages to those who harness its power, also presents its challenges. Aside from Big Data’s immense volume of information, there are additional issues of privacy, timeliness, and heterogeneity—but much of the challenge with Big Data surrounds the data integration of structured, semi-structured and unstructured data.  To achieve its full potential, data integration is essential as each small data stream yields new insights only after it is combined and analyzed within the context of the larger data set and the discoveries these interrelationships reveal.

The difference between structured, semi-structured, and unstructured data lies within the way the data are organized and how easily the data are managed. Unstructured data, the most commonly held type of data within an organization—according to Gartner, about 80 percent—is raw and unorganized.  Analyzing unstructured data takes ample time, money and resources. Due to the growth of unstructured data within organizations—such as emails, social media feeds, and documents—it is important for organizations to find ways to streamline and improve their ability to better understand their business.

On the other end of the spectrum, structured data is extremely well organized and allows for seamless integration into a database enabling a more efficient commitment to evaluate data. An example of this form of data would be an excel spreadsheet, which can be quickly scanned for information to assimilate intelligence for business operations. Semi-structured data is a mix of both unstructured and structured data.  For example, if you take a piece of unstructured data, like a Facebook post, and add tags and keywords, you create a degree of organization and which is a form of semi-structured data.

Vital to business growth, organizations must make it a necessity to gain insights and develop strategies from unstructured, semi-structured and structured data—all of which are important to insight discovery.

The Human Element

Data alone do not create insights. Organizations must push further than passively gathering data and or relying solely on automated tools for insights generation. To convert data into insights, there are three elements needed:  critical thinking and statistical acumen; subject matter expertise; and access to tools—as identified by Dr. Philip B. Stark, Professor and Chair of Statistics at The University of California, Berkley. All three of these elements rely on the “human element” to drive success.

The Big Data Blog Part III

Data scientists with statistical acumen enable organizations to enhance business operations, decision-making, and strategy development.  The one-percent of those data scientists who understand public relation offer a rarer combination who can apply data in the creative process through approaches like message engineering at the intersection of content and data.  Message engineering is a systematic, target audience-based process of developing message, issue, or corporate positioning.

In the process of message engineering, communicators must be able to determine the following: the most profitable target audience, the motivation behind the target audience, how well the message matches the target audience’s priorities in terms of credibility, relevance, sustainability and profitability, how the competition performs against the same criteria, the consistency of the messaging, and which media channels offer the greatest chance for success and engagement with the target audience.

The Message Engineering Process

A five-stage message engineering research process is the most successful way to determine the answers to the above questions. The first step, which drives the following four stages, is to gather data to generate attributes and benefits of current approaches and future opportunities. The possibilities most likely to succeed—about 25 or so—should be tested through quantitative research and social media analysis.

The second step is to test the 25 attributes and benefits using a survey between 300 to 1,000 respondents. After this is completed, organizations should formally analyze three or four key components revealed in the second step, and to explore the competitive environment. Next, content analysis should be utilized across all media—especially across media highly used within the target audience. The fifth and final step is to evaluate. Evaluation should be constant—both continually and consistently—to ensure past decisions are still effective in the constantly evolving marketplace.

Visit us next week for our next post in the series on Big Data and Public Relations and questions you should consider before embarking on a Big Data initiative within your organization.


Irreversible: The Public Relations Big Data Revolution,” is a white paper primer by Mark Weiner of PRIME Research and Sarab Kochhar, PhD of the Institute for Public Relations which introduces essential information about Big Data and how it is redefining public relations profession.

Sarab Kochhar, PhD, is the Director of Research for the Institute for Public Relations, a nonprofit foundation dedicated to research in, on and for public relations.

Mark Weiner is the Chief Executive Officer of PRIME Research, an international research-based communications consultancy working with many of the world’s most admired companies and brands.

To learn more about research and public relations, the Strategic Research Conference sponsored by The Institute for Public Relations and PRIME Research will be held in New York on June 9.  To learn more and to register, click here.

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