Practical Data Literacy for Leaders

Develop data literacy by learning to explore, understand, and communicate with data using critical thinking, interpret data, make decisions with data, and convey its significance to others.

Work on a practical project with Kylie Fashion as an external data consultant to analyze two years of sales data and identify drivers of revenue growth for year three.

Download the course materials in the resource section, including the presentation and the Excel file used to explore the Kaili fashion project data, and follow along with the exercises.

Set the data analysis goal by defining what you want to find, who uses it, and which decisions follow; identify data sources and owners to save time.

Get the right data by validating sources, consulting a data SME, and planning for multi-source fashion sales datasets to answer step-one questions, and set expectations if data gaps emerge.

Learn to examine an Excel file with 17 fields, identify data fields, KPIs, and visualizations, understand data sources, and assess data quality for informed stakeholder analysis.

Assess data quality by confirming accuracy with the data owner, then remove unnecessary fields to speed analysis and improve decision making for leaders.

Explore the six data quality dimensions—accuracy, completeness, validity, uniqueness, timeliness, and consistency—and see how these measures define data quality with practical examples.

Explore how data accuracy shapes trusted reporting by verifying records across the data journey, and understand governance practices that prevent costly, high-stakes errors in healthcare, finance, and beyond.

Explore how data validity defines data quality by enforcing format, type, and range. See how inconsistencies in phone numbers, zip codes, and dates affect completeness and require business rules.

Learn how data timeliness ensures data is available when needed, not just updated; tailor access speed to context—from seconds in emergencies to days for quarterly reports, via a central system.

Learn how data completeness ensures critical fields like allergies, date of birth, phone number, and location are present, while non-critical details may be optional, and distinguish completeness from accuracy.

Identify and resolve duplicate records by evaluating data uniqueness, recognizing that records can duplicate despite partial differences, and ensure completeness and accuracy to boost data trust.

Apply data consistency by ensuring identical employee records across datasets, preventing mismatched names and locations, to enable reliable data linking and improve accuracy and completeness.

Learn to read charts critically, spot tricks that hide details or mislead, compare profit at the segment level instead of just overall figures for sound decisions.

Explore chart games and how axis scale and labeling mask differences in units sold per product, and learn to spot misleading visuals in leadership presentations.

Learn to spot misleading charts and demand proper scales, using bar charts over pie charts to quantify changes in sales and improve data visualizations.

Examine how partial data, like a half-year shirt sale, can mislead. Always request full-year data and ask intelligent questions to support data-driven decisions for leadership.

Evaluate two-year charts of total sales by country for 2022 and 2023, identify issues, and propose three concrete improvements to improve clarity and comparability.

Improve charts by placing 2022 and 2023 on one chart, label each bar clearly, and set the axis from zero to 30,000 to show sales per country.

Learn how to base conclusions strictly on data by identifying and avoiding common data analysis biases, including confirmation bias, to enable true data-driven leadership decisions.

Identify and avoid confirmation bias in data analysis by acknowledging assumptions, analyzing data with an open mind, and comparing findings to beliefs to reach accurate conclusions.

Identify outliers bias in KPI data by examining distributions instead of averages. Use distribution graphs to assess outliers and base decisions on their origins and impact.

Develop practical data literacy for leaders by recognizing and avoiding selection bias, using random data samples to ensure representative data for data-driven decisions, and separating data analysis from personal opinions.

Identify the rush-to-solve bias that drives decisions on limited data in fast-paced business environments, weigh quick decisions against potential worst-case outcomes, and decide what matters most for the business.

Guard against availability bias by recognizing that leaders decide from readily accessible data, across systems; ask three questions: have you received all data, can you wait to gather all data.

Learn to recognize anchor bias in data analysis, avoid rushing to conclusions from initial information, and conduct a comprehensive, thorough data exploration before making decisions.

Apply the 80/20 Pareto principle to data analysis, focusing on what drives profits and using pivot tables and Pareto charts to visualize key drivers.

Derive insightful questions from data by checking KPIs, data quality, and reporting, and engage SMEs to deepen analysis and drive improvements.

Engage business SMEs to interpret data, uncover drivers behind numbers, and gain insights from data leads, managers, or partners before selecting KPIs and data points.

Align KPIs with business goals and targets, focusing on a few key metrics. Assign ownership, set timelines, and track them regularly to govern data-driven performance.

Make sound data-driven decisions by relying on clearly defined kpis and high-quality data. Ensure transparency and accountability, address data silos and overload, and seek expert input to avoid mis interpretation.

Explore how Netflix uses data to drive decisions: predicting show success, fueling a personalized recommendation engine, and optimizing production costs and efficiency from viewer behavior and content analytics.

present data effectively by following seven steps: know your audience, know your go, select critical KPIs, choose the right format, rank KPI importance, and don't present the story with data.

Know your audience before presenting data by clarifying who attends, what they need to learn, and how you establish trust, then tailor the data narrative.

Identify your presentation goal by choosing from five categories—inform, educate, convince, inspire, or entertain—and set a specific post-presentation outcome.

Identify and select the most relevant KPIs that answer the audience's questions and help achieve your presentation goal, favoring a concise set like country sales, product sales, and monthly sales.

Choose an engaging data presentation format by blending PowerPoint with live demos or dashboards, like Tableau, to tell a data-driven story through KPIs and transformations.

Choose effective visualizations for KPIs, using bar charts for trends, a dual axis graph for sales versus profits, and a donut chart for segments.

Rank the KPIs to identify the most important data points that deliver audience insights, and allocate more presentation time to the top KPI, such as sales versus profit trend.

Tell a data story by setting the scene with stakeholders and characters. Reveal root causes with visuals and propose data-driven recommendations based on KPIs.

Practice by filling the presentation worksheet after your last in-organization talk, rank the KPI, and tell a data-driven story to improve your next presentation.

Explore essential data terms in this optional module to boost leaders' confidence in data conversations. Learn about data warehouse, data architecture, data dictionary, ETL, and data governance with practical explanations.

Explain what big data is and why Excel can't handle millions of rows, then show how BI and big data analytics reveal patterns to improve fact-based decision making.

Explore how business intelligence leverages software and services to transform data into actionable insights that inform an organization's strategic and tactical decisions.

Define data architecture as the rules, policies, standards, and models that govern data flow, storage, and integration from sources to analytics, linking business strategy to data strategy.

Discover how data dictionaries catalog data structures, content, and descriptions of named data items to standardize, organize, and share data across departments.

Discover how a data catalog inventories data assets, describes datasets, and speeds access with a data dictionary. Increase data efficiency, reduce errors, and empower leaders with clearer insights.

Learn how data engineering designs and builds systems to collect, clean, and move data from multiple sources into data warehouses for analysis with BI tools.

Explore the data management body of knowledge framework and its 11 subject areas, from data governance as the foundation to data architecture, data quality, metadata, and data warehousing.

Explore data governance by establishing rules, processes, and accountability to secure and make data available to the right people while complying with regulations like GDPR.

Explore what a data set is—a structured collection of related data points, illustrated by a sample sales dataset with fields like order id, segment, country, and product.

Define data quality as how well a dataset meets a user’s needs, guiding accurate, timely, and non-duplicated information for data-driven decisions that influence business outcomes.

Explore data science and its advanced analytics to extract information for business decision making, and learn the six steps from identifying questions to deploying models.

Explore the data warehouse, or dw/dwh, as a central repository that integrates current and historical data from multiple sources, and differentiate it from databases, data marts, and data lakes.

Data mart is a department focused subset of a data warehouse that speeds analysis and tightens access for teams like marketing, HR, or finance.

Explore data lakes as a central storage for structured, semi-structured, and unstructured data, contrasted with warehouses that require predefined schemas, enabling scalable, cost-efficient advanced analytics.

Visualize information to help leaders understand data and gain insights. Use charts and graphs to highlight trends and outliers, telling a compelling data story that informs decisions.

Learn how ETL extracts data from sources like Salesforce, transforms it for the data warehouse, and loads it into reports. Explore how scheduled intervals delay the latest data.

Explore metadata using a photo and a book cover, illustrating how width and height, camera make and model, exposure time, and other attributes describe data about other data.

Develop practical data literacy for leaders by mastering how to read, work with, analyze, and present data, with hands-on Excel exercises and essential analytics terminology.