Analytics Translation

The sheer amount and variety of existing and new data generated in the world today are unprecedented. As this growth continues, so do the opportunities for organizations to use their data to create actionable results. In an article for Forbes Magazine, Bernard Marr writes, ‘Forget Data Scientists And Hire A Data Translator Instead’.

An Analytics Translator is a conduit between data scientists and executive decision-makers. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations.

Analytics Translators are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distil it to others in the organization in an easy-to-understand manner. This professional must be someone who can “talk the talk” of both the executives and the data scientists.”

Multivariate Solutions helps clients identify and capture the most value and meaningful insights from data, and turn them into competitive advantages.

Analytics Translation Articles

Data Translators – The Must Have Role for the Future 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? Our conception of a role of “data translator” is an intriguing one, and plays to certain historical strengths of market researchers.

Principles of sampling are analyzed in this guest blog post article.

A cruise line example illustrates how to use discrete choice to determine marginal value in this piece published in Quirk’s Marketing Research Review, How to Price an Island.

Twitter Network Analysis: Nordstrom at the Center of Resistance?, published in Greenbook, examines how visualization of social networks is now coming online to make sense of Big Data and convey the results of analyses through emerging, open-source programs.
Read More
Using open-source software to make sense of small big data, published in Quirk’s Media, describes how to use new capabilities with traditional marketing research deliverables.
Read More

Michael Lieberman’s Quirk’s Marketing Research Review article, Using NodeXL to Visualize Social Media, demonstrates how to leverage social networks for promotion using the NodeXL open-source Excel add-on tool.

Regression Analysis – Predicting Key Drivers analyzes a studies in which the client sought to uncover the attribute or attributes among those you measured that best define your client’s customer satisfaction, usage drivers, or leading cause of switching to a competitor’s brand.

A Look Inside the Choice Toolbox examines how to select the most appropriate choice model from these five most commonly used ones: Paid-comparison Analysis, Conjoint Analysis, Discrete Choice Modeling, Max-Diff, and Adaptive Choice Analysis.

Small Data Visualization by Michael Lieberman presents two examples for examining primary research – a two-step cluster analysis and a data-mining example that takes existing consumer purchase data and reports the items in which the client should specialize to connect to the greatest number of other products.