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Eight Personas Found in Every Data-Driven Organization

Scaling data fluency learning programs successfully is no small feat. Learn about data personas and how a persona-driven methodology can help you scale your data training program.
Oct 2020  · 5 min read

On October 15, we're hosting a webinar on What L&D Leaders Need to Know About Data Fluency. One key focus area is building the right skills for each of your key data personas. Here's a sneak preview on what you'll learn.

The challenges in upskilling for data fluency

Data fluency is a methodology for answering business questions rather than a singular skill to be taught and learned, like traditional learning and development initiatives. When scaling learning and development programs for data fluency, learning journeys will vary depending on the level of interaction different individuals may have with data. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data.

Why a persona-driven methodology is a way to go

A role-based, persona-driven learning journey is more effective at scaling data fluency training programs. While each organization and the data they produce is different, there are commonalities in the different relationships individuals have with data. A useful way of thinking about and scaling data-focused upskilling efforts is with data personas. Each data persona has a different relationship with data and requires different data fluency competencies to become empowered to do their best work. Organizations can then map different roles within the organization to that persona and create a curated, personalized learning experience depending on what they need to learn.

1. Data Consumers and Leaders

Data Consumers and Leaders often work in non-technical roles, but they consume data insights and analytics to make data-driven decisions. They often need to have conversations with data professionals and should be able to distinguish when data can and cannot be used to answer business questions.

Commonly used technology and tools

Spreadsheets: Google Sheets, Microsoft Excel Business Intelligence: Power BI, Tableau

Example job titles

Chief Marketing Officer, Human Resources Manager, Head of Sales

2. Business Analysts

Business Analysts are responsible for tying data insights to actionable results that increase profitability or efficiency. They have deep knowledge of the business domain and often use SQL alongside non-coding tools to communicate insights derived from data.

Commonly used technology and tools

Spreadsheets: Google Sheets, Microsoft Excel Business Intelligence: Power BI, Tableau SQL: PostgreSQL, SQL Server, Oracle SQL

Example job titles

Business Analyst, Supply Chain Analyst, Operations Analyst, Financial Analyst

3. Data Analysts

Similar to Business Analysts, Data Analysts are responsible for analyzing data and reporting insights from their analysis. They have a deep understanding of the data analysis workflow and report their insights through a combination of coding and non-coding tools.

Commonly used technology and tools

Programming languages: Python, R Spreadsheets: Google Sheets, Microsoft Excel Business Intelligence: Power BI, Tableau SQL: PostgreSQL, SQL Server, Oracle SQL

Example job titles

Data Analyst, Business Analyst, Supply Chain Analyst, Operations Analyst, Financial Analyst

4. Data Scientists

Data Scientists investigate, extract, and report meaningful insights with the organization’s data. They communicate these insights to non-technical stakeholders and have a good understanding of machine learning workflows and how to tie them back to business applications. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools.

Commonly used technology and tools

Programming languages: Python, R, Scala SQL: PostgreSQL, SQL Server, Oracle SQL Big data tools: Airflow, Spark

Example job titles

Data Scientist, Data Analyst, can include a “citizen data scientist” (i.e., someone who performs the tasks of a data scientist, but does not have the title “Data Scientist”).

5. Machine Learning Scientists

Machine Learning Scientists are responsible for developing machine learning systems at scale. They derive predictions from data using machine learning models to solve problems like predicting churn and customer lifetime value, and are responsible for deploying these models for the organization to use.

Commonly used technology and tools

Programming languages: Python, R, Scala SQL: PostgreSQL, SQL Server, Oracle SQL Big data tools: Airflow, Spark Command-line tools: Git, Shell

Example job titles

Data Scientist, Research Scientist, Machine Learning Scientist, Machine Learning Engineer

6. Statisticians

Similar to Data Scientists, Statisticians work on highly rigorous analysis, which involves designing and maintaining experiments such as A/B tests and hypothesis testing. They focus on quantifying uncertainty and presenting findings that require exceptional degrees of rigor, like in finance or healthcare.

Commonly used technology and tools

Programming languages: Python, R SQL: PostgreSQL, SQL Server, Oracle SQL

Example job titles

Quantitative Analyst, Inference Data Scientist, Data Scientist

7. Programmers

Programmers are highly technical individuals that work on data teams and work on automating repetitive tasks when accessing and working with an organization’s data. They bridge the gap between traditional software engineering and data science and have a thorough understanding of deploying and sharing code at scale

Commonly used technology and tools

Programming languages: Python, R, Scala Command-line tools: Git, Shell

Example job titles

Software Engineer, Data Scientist, Dev-Ops Engineer

8. Data Engineers

Data Engineers are responsible for getting the right data in the hands of the right people. They create and maintain the infrastructure and data pipelines that take terabytes of raw data coming from different sources into one centralized location with clean, relevant data for the organization.

Commonly used technology and tools

Programming languages: Python, R, Scala SQL: PostgreSQL, SQL Server, Oracle SQL Command-line tools: Git, Shell Big data tools: Airflow, Spark Cloud Platforms (e.g., Amazon Web Services)

Example job titles

Software Engineer, Data Engineer, Dev-Ops Engineer

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