In the era of big data, communication research and measurement can be a daunting prospect. With so much information available, how can communicators find the information they need to make a difference in their organizations? CW Senior Editor Jessica Burnette-Lemon asked Sarab Kochhar, Ph.D., director of research for the Institute for Public Relations and associate director of measurement and analytics for APCO Worldwide, for some insights on what new types of measurement skills communicators need to master.

Jessica Burnette-Lemon: Measurement and data science are becoming more and more a part of the required skill set for communication professionals, but many communicators are still reluctant to embrace these skills. How can they get started, or improve on the skills they have? 

Sarab Kochhar: It's important to measure what "matters" which is defined by the how success is determined. Measurement and data science help assess and enable the communication professional to determine which methods, tactics and tools are working and which are not working. Virtually every aspect of business is now open to data collection and this broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data. PR and communication professionals should view data science as a way to improve decision-making and as a key strategic asset.

JBL: What kind of research and measurement skills do you think will be crucial for PR and other communication professionals in the near future?

SK: One skill that will help PR and communication professionals in their future is the ability to ask the right questions. As communication professionals, we should understand that the data could only give us the right answers if we ask the right questions. We should be able to view business and communication problems from a data perspective and understand principles of extracting useful knowledge from data.

The fundamental structure of data-analytic thinking is a skill that the PR and communication professionals should master. Then there are particular areas where intuition, creativity, and domain knowledge would be critical for success. It is very important for communication professionals to understand data science even if they don't have to do it themselves.

JBL: Gathering large-scale ""big data"" can yield a ton of information. How can communication professionals extract actionable information from research and measurement, rather than a lot of statistics?

SK: The process of extracting actionable insights begins with asking the right question. This requires a thorough understanding of the communication process and the communication program. But a basic knowledge of data analysis will help the communication professional to make sense of the results and apply them to their programs.

Communication professionals don't have to become experts in the tools, storage and the coding of data, or be statisticians, modelers or data scientists. Rather they should focus as much as possible on the techniques in data science and how they are used. Once we treat data as our key strategic asset, we can assess whether, how much, and how we should invest in data. What exactly is the business/communication problem to be solved? Is the data science solution appropriate to solve the problem? These fundamental questions are important for anyone working with data scientists, employing data scientists, investing in data ventures, or directing the application of analytics in their organization and to being the process of extracting actionable information from research data.

JBL: Can you share an example of an organization that used research data to help them improve their processes, reputation or sales?

SK: There are plenty of examples of organizations that are using research data to their advantage. Leading organizations are treating data as a strategic asset and putting processes and systems in place to help improve decision-making and drive actionable results. Data mining is used for general customer relationship management to analyze customer behavior. The finance industry uses data mining for credit scoring and trading. Retailers apply data science in marketing and supply-chain management. In 2004, Walmart used research data to predict the sales during Hurricane Frances. Using data from what happened when Hurricane Charley struck, Walmart found that their stores would need certain products—not just flashlights. Data showed that sales of strawberry Pop-Tarts increase at least seven times their normal sales rate ahead of a hurricane.

Mastercard's "conversation suite" is another such example where a dedicated team listens to what stakeholders are saying about the brand. The web-based tool is led by a global team of social media experts that monitor, analyze and engage in conversations around the world, in real time, 24/7, and collaborates internally with PR, marketing, product and customer service teams to better engage stakeholders. The tool has helped Mastercard to shape ideation and product development, spark brand engagement and awareness, track brand performance and reputation, and provide context around what consumers really want.

Video: Mastercard's conversation suite