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Data Translators – The Must Have Role for the Future

Fusing sophisticated predictive analytics capabilities with marketing research story-telling mastery to offer cogent and compelling conclusions.

Editor’s Intro: Michael brings an interesting perspective to the existential angst of many market researchers – what should my job be in an age of big data and data science? His conception of a role of “data translator” is an intriguing one, and plays to certain historical strengths of market researchers. There is still much thinking to be done to flesh out the concept and execute the role shift – e.g., is this more of a client or research company/consultant role? How is value demonstrated concretely? How can money be made? Nonetheless, Michael’s article should get some good discussion going.

As defined by Google, a data translator is a conduit between data scientists and executive decision-makers. They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distill it to others in the organization in an easy-to-understand manner. In an article for Forbes, Bernard Marr writes, ‘Forget Data Scientists And Hire A Data Translator Instead’.

These themes recur at various industry conferences, such as the ESOMAR Big Data Event in Berlin in November, the Public Relations Corporate Communications Conference, and the Big Data & Analytics for Retail Summit, as well as those held in related industries, such as Predictive Analytics and forecast modeling. Even at a recent decision-theory (often referred to as “Game Theory”) conference, someone took to the stage to discuss how to influence decision makers. In an article in the February 2018 Harvard Business Review, McKinsey Analytics published a piece entitled “Analytics translator: The new must-have role”.

After many years of digesting these presentations, forming strategic alliances across skills sets in the marketing research industry, and waving hello and goodbye to ‘new’ phases, data torrents as well as to the new ‘sexy’ data scientists, I am ready to offer a prediction about a lucrative avenue our industry just may tumble down.

Like the merging of qualitative research with quantitative analysis, the new data/analytics translation is a mixture of traditional marketing research skills and the continuing expansion of bandwidth. There is now a plethora of open-source software available, as well as a tsunami of consumer and social media data. So many tools, so much data. One message is coming through clear as a bell: clients and C-suite executives want to hear the story.

Who, then, will tell the story?

Simple answer: Marketing Researchers.

Big Data and the Problem with Predictive Analytics

Predictive Analytics is a cousin to Marketing Research. The former targets endeavors such as investments, commercial and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, sector timing, and fraud detection.

At an international Predictive Analytics conference that took place several years ago in San Francisco, I met folks in the field of Predictive Analytics. I was surprised to hear that many of those who regularly run optimization models for companies like Unilever, Proctor & Gamble, and Levi’s spend their working hours using Excel to ensure that Levi’s, for example, does not send skinny jeans to dairy farmers in Wisconsin. Yet statistical know-how is in short supply among this group. As is, for that matter, experience in constructing surveys or writing the marketing narratives that constitute the core of marketing research reporting.

Predictive Analytics and Marketing Research are two distinct fields. However, both industries employ data scientists. Marketing research firms regularly mine corporate databases in order to write up conclusions. I myself have done so for Burger King, Pfizer, the Ohio State University Medical Center, the Cheesecake Factory, REI Adventures (a large adventure travel company) as well as voter targeting for a presidential campaign.

Below I summarize the project path these endeavors take.

Predictive Analytics professionals calculate results to maximize model efficiency—the Data and Information side of the chart. They are not equipped to present detailed yet summated reports.

Marketing research reporting skills, combined with sophisticated analytical firepower, position marketing research professionals to interpret for the C-Suite that deluge of data. This takes into account the right side of the project path as well – that of Knowledge and Information. It also opens the door for a researcher to grow into the role of strategic consultant, now commonly referred to as an Analytics or Data Translator. As Bernard Marr writes in Forbes, “A data translator is a conduit between data scientists and executive decision-makers. They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distill it to others in the organization in an easy-to-understand manner.”

Open Source Power: The R-Project

For readers who are not familiar with open source statistical software, R is a free-of-charge programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. While there is a steep learning curve with R,
marketing research professionals can certainly learn to use it.

In the past, one had to purchase expensive SAS licenses or many SPSS modules to achieve the firepower that is now available for free on the internet. Below is a list of a few of the thousands of open-source modules contained in the R-Project. My list includes some of the most well-known and commonly practiced algorithms used in predictive analytics:

  •  Bayesian Inference
  • CHAID Trees
  • Feature Selection Regression
  • General Linear Models
  • Logistic Regression
  • Machine Decision List Functions
  • Neural Networks

There are many, many more.

These days any marketing research firm can partner with a data scientist, and thereby offer to its clients not only a research report, but the capability to mine corporate databases with a sophistication provided by predictive analytics companies. Each of the above-mentioned algorithms can be utilized in day-to-day marketing research. Training for these additional skills is also open-sourced. Dozens of free, short, online courses on, say, Coursera can train any experienced marketing researcher how to analyze and distill analytic output.

Ubiquitous Tools: Excel

When I perform a conjoint analysis, I always give my clients the simulator in Excel. Why? Because unlike specialty conjoint software, everyone has Excel. I employ an Excel programmer to give us the penetration for a simple discrete model, or the ability to easily create ‘what-if’ scenarios for a multi-dimensional exponential probability simulator.

Excel capacity is huge. The published limits on Excel spreadsheets are 1,048,576 rows by 16,384 columns. In other words, over 16,000 variables with more than one million data points. And that is for one workbook! Databases can easily be separate workbooks. Furthermore, all Excel files can be brought into SPSS for data management and then imported into R for the superpowered statistical analysis.

Exchange of these large files is simple via most data exchange services, such as Dropbox. In short, now even boutique marketing research firms have the ability to receive and analyze large client databases that, only a few years ago, required too much space.

Bringing Analytics Translation into the Mainstream

Thus far, we have demonstrated that the tools and bandwidth for marketing researchers to perform predictive analytics or database mining are readily available today, and will be increasingly so over time.

So: how might the marketing research industry best exploit this new frontier?

Marketing researchers are experts at constructing questionnaires and summarizing results. Predictive analytic people, by contrast, are not. While they can certainly calculate a dataset, when it comes to summarizing findings and presenting them to a CMO, marketing researchers win hands-down.

Why? Because marketing researchers are trained in inductive reasoning, a crucial component of a project report—the Knowledge and Wisdom side of the graphic. This is the C-Suite deliverable. We are natural data translators.

Summing Up

We can indeed do it all. We take the data, process it with high-power, open-source software; we summarize the results and provide our clients with the ability to leverage strategic thinking. We fuse sophisticated predictive analytics capabilities with marketing research story-telling prowess to offer cogent and compelling conclusions. We are data translators, the ‘must-have’ role for the future.

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