Terms are divided into three categories
Analytics team consists of data scientists, data analysts, database managers, and analytic translators.
Data Analyst is a technical role focused on implementing, supporting, and optimizing data analytics technology. They are skilled administrators and users of data tools.
Data Scientist is a scientific role focused on collecting, interpreting, analyzing and displaying data through advanced analytic techniques. Data scientists are experienced in applying analytic programming languages and usually have advanced degrees that include interdisciplinary training in scientific inquiry.
Database managers design, maintain, update, and protect complex databases. They are trained in computer application systems that control backups, storage management, performance tuning, and software for accessing data.
Business Analysts analyze an organization's data, processes, and systems to provide guidance for improving business processes, products, services and software through data analysis. Sometimes a member of a data team, sometimes a member of the business team.
Business Team (in the context of analytics) include the members of marketing, sales, operations or other business functions who request analytic support from the analytics team.
Analytic Translator: An advisor trusted by data analysts and business leaders to crystalize, explain, and shepherd complex analytic projects efficiently and collaboratively from initial concept to a relevant, insightful decision, or application, in ways that recognize and elevate the contribution of everyone involved. Translators interpret information in both directions: from business to analytics and the reverse direction from analytics to business.
Analytics Translator is a term introduced by McKinsey in 2018. They describe the role as playing a critical for bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization.
Methods and Activities
Big Data is a widely-used term referring to the derivation of insights from large data sets. Big data is often associated with large, diverse or unstructured data sets and analysis techniques beyond simple, straightforward models. Big Data projects often use machine learning.
Artificial Intelligence (AI) was originally conceived as methods to allow computers to “think for themselves” and become capable of developing new insights without being programmed to do so. It has become a broad term for the use of computing systems to iteratively “learn” more about data to promote greater levels of understanding and predictability.
Machine Learning is a subset of AI that refers to methodologies, technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience. These methods are usually applied to improve the accuracy of predictive models.
Statistical Analysis is the application of math and statistics to data in order to summarize, compare, explain and interpret patterns. It is used descriptive, comparative and predictive analytics.
Descriptive Analytics applies statistical methods to summarize and describe sets of data.
Comparative Analytics applies statistics to determine similarities and differences.
Predictive Analytics applies statistical analysis to predict future outcomes.
Data Visualization is the science and art of deriving meaning from data sets by using graphical and other non-tabular presentations. It is a specialized skill, and a key component of Analytic Translation.
Project Phases. Analytic translation identifies three phases of a project.
Design. The Design phase consists of all conversations, data review and goal-setting that occur until an analytic design is agreed upon by the business and analytic teams.
Do. The Do phase consists of all analytic work, adjustments, changes and updates that occur until a result is ready to be shared with the business team/client.
Deliver. The deliver phase includes all preparation and activities related to transferring results back to the intended audience(s).
Translation components. Analytic Translation focuses on three components of communication to promote understanding and collaboration.
Discover. In discovery, the Analytic Translator uses listening and questioning tools to crystalize the intention, purpose and context of an issue.
Distill. Next, the Analytic Translator distills what to share with the other team, and in what order, according to how it will influence the project.
Decipher. Then, the Analytic Translator converts the essential messages into language understandable to the other team.