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Data-Based Decision Making for Managers: Practical Analytics

Name: Data-Based Decision Making for Managers: Practical Analytics
Rating: 4.1 (4 reviews)

Use data (not guesswork) to set KPIs/OKRs, read dashboards, run tests, and build a culture of evidence-based decisions.

Role Play

Created byPracticalGrowth Learning

Last updated 6/2026

English

What you'll learn

Define objectives and translate them into measurable questions, metrics, and KPIs.
Apply a 6-step data-driven decision cycle and validate outcomes with feedback loops.
Choose and interpret descriptive, diagnostic, predictive, and prescriptive analytics.
Read dashboards/visuals critically and communicate insights with clear data storytelling.
Use frameworks like OKRs, Balanced Scorecard, A/B testing, and PDCA/DMAIC.
Identify bias, bad KPIs, and data-quality issues that lead to wrong decisions.

Course content

4 sections • 12 lectures • 1h 45m total length

Introduction7:26
What does it really mean to be data-driven—and why does it matter now more than ever?
This opening lecture sets the stage for the course by exploring the core idea behind data-based decision making: using facts, not just instincts, to make smarter calls at work. You’ll learn why relying on experience alone can lead you astray, how data helps sharpen your judgment, and what to expect in the rest of the course.
Understand what data-based decision making actually means (and what it doesn’t)
Learn why data matters in every industry—not just tech
Explore the risks of gut-only decisions and the benefits of evidence-backed thinking
Preview the frameworks, tools, and real-world examples covered throughout the course
Get inspired to start using data in your own decisions, even without fancy tools or big budgets
Why Data-Driven Decision Making Matters7:57
Why do data-driven companies consistently outperform the competition?
In this lecture, we explore the real-world value of making decisions backed by data—not just as a best practice, but as a measurable advantage in performance, profitability, and risk management. You'll learn why relying on instinct alone can lead to costly missteps, and how companies that prioritize data make smarter, more strategic moves.
Understand how data improves clarity, accuracy, and reduces decision risk
Learn from real companies like Red Roof Inn, Capital One, and Walmart
See how data-driven thinking helps avoid bias and challenge assumptions
Explore why a strong decision culture matters just as much as the tools
Discover what goes wrong when companies skip the data and trust gut alone
Core Concepts and Terminology9:21
Ever feel like you’re swimming in data but still don’t know what it means or what to do with it?
In this lecture, we break down the core language and mental models behind data-based decision making—so you can move from confusion to clarity. You’ll learn how to interpret the right metrics, recognize the difference between data and insight, and avoid common mistakes that even experienced managers make when using numbers to guide decisions.
Understand the difference between data, information, and actionable insight
Learn how to define and evaluate meaningful KPIs (not just easy-to-track metrics)
Explore the concept of evidence-based management and why it matters
Avoid common data traps like correlation errors, confirmation bias, and bad inputs
Get comfortable with the vocabulary you need to confidently use data in your role
The Data-Driven Decision Process9:55
What makes a decision truly data-driven? It’s not just the data—it’s the process behind how you use it.
In this lecture, you’ll learn a practical, step-by-step framework for turning raw information into confident action. Whether you're solving a problem or evaluating a new opportunity, this process helps you ask the right questions, gather the right data, and follow through with decisions that stick.
Learn a six-step loop for making and validating data-driven decisions
Discover how to define clear objectives and translate them into measurable questions
Understand how to collect, clean, and analyze relevant data without getting overwhelmed
See how to turn insights into action—and why validation is the key to long-term success
Explore how real organizations use this process to test, learn, and improve continuously
Section 1 Knowledge Check

Types of Data and Analysis8:34
Not all data is created equal—and not every analysis answers the same question.
This lecture breaks down the different kinds of data you’ll encounter at work, from structured spreadsheets to messy support transcripts, and shows you how to make sense of each. You’ll also learn the four key types of analytics and how to choose the right one depending on what you’re trying to solve.
Understand the difference between structured, unstructured, and big data
Learn when and why to use descriptive, diagnostic, predictive, or prescriptive analysis
Discover real-world examples of companies turning raw data into insight
Explore how AI is making it easier to work with text, voice, and other complex data
Get clarity on what kind of data and analysis to use for everyday business questions
Tools and Technologies for Data-Driven Decision Making10:12
Ever feel like you have all the numbers—but none of the clarity?
This lecture walks you through the most common tools that turn raw data into smart decisions, from everyday spreadsheets to AI-powered dashboards. Whether you're leading a team or collaborating with analysts, you’ll learn how to navigate the landscape of data tools with more confidence—and understand when each one is worth using.
Compare spreadsheets, BI platforms, SQL, and advanced analytical tools like Python or R
Discover how real companies like JetBlue, Spotify, and Cox Communications use data tools to move faster
Understand what self-service analytics can do for non-technical managers
Learn why infrastructure—data warehouses, integrations, and governance—is critical behind the scenes
Get practical insights into picking the right tool for the right task (without overcomplicating things)
Data Visualization and Communication8:12
If your charts aren’t helping people make decisions, they’re just decoration.
This lecture shows you how to turn raw numbers into clear, compelling visuals that people can actually use. You’ll learn how to choose the right charts, avoid misleading designs, and combine visuals with storytelling to create reports and dashboards that drive action—not confusion.
Learn which chart types best match the questions you're answering
Understand the key principles behind clean, accurate, and honest visual design
Practice interpreting dashboards to spot trends, outliers, and next steps
Explore how to tell a meaningful data story that inspires decisions
See how effective visuals guide attention and build confidence across teams
Churn Dashboard Readout to a Skeptical VP
Section 2 Knowledge Check

Building a Data-Driven Culture8:51
Dashboards don’t build culture—people do.
This lecture explores what it really takes to embed data into the everyday habits, values, and decision-making routines of your team. You’ll learn how to lead by example, overcome common barriers like resistance or siloed systems, and foster an environment where data becomes part of the conversation—not an afterthought.
Discover how leaders at companies like Procter & Gamble and Lloyds Banking Group drive data-first mindsets
Learn practical ways to build data literacy and comfort across your team
Identify cultural blockers like mistrust or conflicting definitions—and how to fix them
Understand the importance of transparency, experimentation, and cross-team collaboration
Walk away with steps to make your team more data-aware, even without fancy tools
Frameworks and Techniques for Data-Driven Decisions10:10
Even the best data won’t help if you don’t have a plan for how to use it.
This lecture introduces practical frameworks that bring structure, clarity, and repeatability to your decision-making. From setting measurable goals to testing ideas and refining processes, you’ll learn proven techniques used by high-performing teams to move from insight to action.
Explore when and how to use tools like the Balanced Scorecard, OKRs, and KPI dashboards
Understand how A/B testing helps eliminate guesswork from tough decisions
Learn continuous improvement models like PDCA and DMAIC for long-term progress
Discover how companies like LinkedIn and Meta use these frameworks to guide real decisions
Build your own decision-making toolkit to apply immediately in your role
Case Studies of Data-Driven Decision Making8:03
What do Amazon, Netflix, Spotify, and Walmart have in common? They all turn data into decisions—and results.
In this lecture, we’ll explore how leading companies use data to drive real business outcomes, from product design to personalized customer experiences to operational efficiency. These aren’t just stories—they’re blueprints you can learn from, regardless of your industry or team size.
See how Amazon uses testing and personalization to boost sales
Learn how Netflix evaluates content strategy and audience engagement through behavioral data
Explore how Spotify improves app design and feature adoption with A/B testing
Understand how Walmart uses real-time data to forecast demand and optimize inventory
Take away practical lessons from each example that you can scale to your own work
Emerging Trends and Challenges in Data-Driven Decision Making10:21
More data. More tools. More pressure. So what’s next for decision-making at work?
This lecture explores the evolving landscape of data-driven leadership, from AI-powered analytics and self-service tools to the ethical and cultural challenges that come with them. You’ll learn how to navigate new technologies while staying grounded in good judgment, governance, and human insight.
Understand how AI and machine learning are shaping the next generation of business decisions
Learn the risks of bias, bad data, and over-reliance on algorithms—and how to avoid them
Explore how self-service BI and data democratization are empowering more employees
See why privacy, transparency, and ethical use of data are becoming non-negotiables
Get a glimpse into future trends like decision intelligence and AI-assisted leadership
Ethics Review Before Launching an AI Feature
Section 3 Knowledge Check

Recap and Next Steps6:29
What does it really take to become a data-driven leader—not just in theory, but in practice?
This closing lecture brings together the key lessons from the course and gives you a clear path for applying them in your own role. You’ll walk away with simple, practical steps to start making smarter decisions right away, even if your organization isn’t fully data-driven yet.
Recap core ideas like goal-setting, measurement, and data-informed thinking
Learn five habits that help managers use data consistently and effectively
Get advice on overcoming common blockers like tool limitations or cultural resistance
Find out how to start small—using simple analysis to make everyday decisions better
Leave with tips and resources to keep building your data skills beyond the course
Section 4 Knowledge Check
Final Challenge

Requirements

There are no pre-requisites for this course

Description

Companies are drowning in dashboards, reports, and “insights”, yet many managers feel less confident, not more. Here’s the opportunity: research consistently shows that organizations that use data effectively outperform their peers—one MIT study found data-driven firms are about 5–6% more productive and profitable, and McKinsey reports data-driven companies are far more likely to be profitable.

In other words, using data well isn’t a “nice to have.” It’s a competitive advantage.

But data-based decision making doesn’t mean ignoring experience or “letting the numbers decide.” It means combining judgment with evidence: defining the right question, using the right data, interpreting results correctly, and validating outcomes so you improve with every decision.

That’s exactly what this course is designed to help you do.

In this course, you’ll learn how to:

- Define data-based decision making (and what it is NOT)

- Turn goals into measurable questions, metrics, and KPIs that drive the right behavior

- Follow a repeatable decision process: define → collect → analyze → interpret → act → validate

- Understand structured vs. unstructured vs. “big data,” and when each matters

- Use the analytics ladder: descriptive, diagnostic, predictive, and prescriptive analytics

- Make sense of dashboards and visualizations—and communicate insights with clarity

- Apply practical frameworks: Balanced Scorecard, OKRs, KPI dashboards, A/B testing, PDCA/DMAIC

- Build a data-driven culture (trust, literacy, experimentation, and shared definitions)

- Spot common traps: correlation vs. causation, confirmation bias, and “garbage in, garbage out”

- Navigate modern challenges: AI/augmented analytics, ethics, privacy, governance, and bias

You’ll also see real-world case studies from companies like Amazon, Netflix, Spotify, and Walmart—so you can model what works and avoid what doesn’t.

This is a practical, manager-friendly course. You won’t need coding skills or advanced math. You’ll learn how to think clearly with data, ask better questions, and make decisions you can defend—whether you’re working with Excel, BI dashboards, or an analytics team.

If you’re ready to stop guessing, reduce risk, and lead with more confidence, this course will give you the toolkit and habits to make data-based decision making part of your daily work.

Who this course is for:

Managers and team leads who make operational or strategic decisions
Business professionals who want to use data with more confidence
Product, marketing, sales, and customer success leaders
Operations, supply chain, and service delivery managers
HR and people leaders making hiring and performance decisions
Entrepreneurs and small business owners who need practical analytics
Analysts or aspiring analysts who want stronger decision frameworks
Anyone working with dashboards, KPIs, reports, or performance metrics

Data-Based Decision Making for Managers: Practical Analytics

What you'll learn

Explore related topics

Course content

Introduction and Foundations4 lectures • 35min

Data and Analytical Tools4 lectures • 27min

Application in the Business Environment5 lectures • 37min

Conclusion and Next Steps1 lecture • 6min

Requirements

Description

Who this course is for: