Data-Driven Decision-Making (Complete Training Course) Session-01

 

COMPLETE TRAINING COURSE Data-Driven Decision-Making Eight sessions · Units 1–8 · From first principles to practical use Based on Saylor University BUS607 (CC BY 3.0). Examples adapted for the apparel industry.

By Md. Tarikul Islam

How to use this course

This course follows the eight units of Saylor University’s BUS607, Data-Driven Decision-Making. It is written as eight sessions, one per unit, so you can read or teach one session at a time. Every session follows the same flow: why it matters, the key ideas explained step by step, an example, an activity and a recap.

 

Eight sessions	One session for each unit of the course. You can pause between sessions; every session stands on its own and ends with a recap.
Same flow each time	Why it matters, then the key ideas explained step by step, then an example, then an activity, then a recap.
Factory examples	Slides tagged EXAMPLE use garment-factory cases such as line efficiency, defects and buyers. The numbers are illustrative.
Activities	Slides tagged ACTIVITY are short group exercises. They turn ideas into practice, so please take part.

Course roadmap

Session 1	Introduction to Data-Driven Decision-Making
Session 2	Transforming to a Data-Driven Enterprise
Session 3	The Role of Leadership
Session 4	Types of Data
Session 5	Deriving Data Insights
Session 6	Creating Effective Visualizations
Session 7	Database Marketing and CRM
Session 8	Data-Driven Uses and Misuses

 

Sessions 1–4 build the foundation (why, what changes, who leads, what data). Sessions 5–8 build the practical skills (insight, visuals, customers, responsible use).

What you will be able to do

•    Describe why relying on data analytics matters for organizational decisions

•    Tell apart the three continuums needed to implement data-driven decision-making

•    Explain the role of leadership and why it matters

•    Differentiate data types and describe the levels of analytics

•    Analyze how data is organized and summarized, based on customer trends

•    Apply best practices for effective visualizations for different audiences

•    Describe business strategies that data-driven decision-making can improve

SESSION 1

  Introduction to Data-Driven Decision-Making

  Unit 1: what it is, why it matters, how it works

By the end of this session you will be able to:

•    Explain what data-driven information is and how it helps business decisions

•    Describe the steps in the data-driven decision-making process

•    Show how data-driven techniques guide decisions across business areas

Why businesses rely on data

Leaders and managers decide every day. Data is much more reliable than intuition for those decisions.

Smarter decisions Facts and metrics show what is really happening, so choices rest on evidence and not on opinion.

Captured information Data records what the business has learned, so knowledge stays in the company and not only in people's heads.

Better decision process Decisions become easier to explain, repeat and improve over time.

 

Data helps companies decide smarter, capture information and improve how they decide.

 

Without data versus with data

 

Without data	With data
•    Decisions lean on personal bias •    False assumptions go unchallenged •    Judgment becomes clouded •    Poor decisions become more likely •    Hard to explain to others	•    Facts and metrics guide choices •    Decisions match organizational goals, objectives and initiatives •    Reasons are clear and can be explained •    Results can be measured and improved •    Everyone works from the same facts

 

Data informs the decision. Experience helps us interpret it.

 

 EXAMPLE

Example: Google People Analytics

Google created a People Analytics department and used performance reviews and employee survey data to answer its toughest HR questions. The data showed that managers with specific characteristics made employees happier, which made them stay longer and work more productively.

 

What is data-driven decision-making?

DDDM means using facts from data, and not intuition or stereotypes, to guide business decisions.

1.  Collect data	Gather data from daily work: output, quality, time, orders, costs, customers.
2.  Extract patterns and facts	Study the data to find what is really happening, and what the pattern is.
3.  Guide decisions	Use those facts to choose what to do, and keep the decision tied to company goals.

 

Being data-driven keeps decisions aligned with goals, objectives and initiatives.

 

The DDDM process: five steps

A clear process makes every decision follow the same logic, so results can be checked and improved.

1.  Define the decision	State the question clearly. What must be decided, and which goal does it support?
2.  Collect the data	Gather relevant and reliable data from the right sources.
3.  Analyze the data	Organize and summarize the data to find patterns and facts.
4.  Decide and act	Use the facts to choose an option and put the decision into action.
5.  Review the result	Measure what happened, learn from it, and use it in the next decision.

 

 EXAMPLE

Example: five steps on Line 7

 

Step	What we did	What we found or decided
1  Define	Asked: why is DHU rising on Line 7?	A clear, measurable question
2  Collect	Pulled four weeks of defect logs by operation	Complete records for all shifts
3  Analyze	Grouped defects by type and by operation	Two defect types made up 61% of all defects
4  Decide and act	Retrained operators on two operations and fixed material feeding	Action plan with owners
5  Review	Compared DHU two weeks later	DHU fell from 6.2 to 4.1

 

Each step builds on the one before, and step 5 feeds the next decision.

 

Decision-making styles

Managers differ in how they approach a choice. Knowing your style shows where data can balance it.

Directive Fast and decisive. Wants quick, clear answers and may use little information.	Analytical Wants more data and careful analysis before deciding. Comfortable with complexity.
Conceptual Looks at the big picture and the long term. Likes new and creative ideas.	Behavioral Cares about people and harmony. Seeks input and agreement from others.

 

Whatever the style, data gives everyone a shared base of facts.

 

Decision-making approaches

How an organization approaches decisions shapes how useful its data can be.

Avoidance	Not making a decision at all, hoping the problem goes away. Data cannot help if no decision is attempted.
Problem solving	Acting after a problem appears. Data helps find the cause and test whether a fix works.
Problem seeking	Looking ahead for risks and opportunities before they become problems. Data makes this possible.

 

DDDM moves an organization from avoiding and reacting toward actively seeking.

 

DDDM across business domains

Data-driven techniques appear in many areas. Each one uses data to guide the next step.

Data-driven learning Use data on how people perform to adjust training. Example: a skill matrix shows which operators need which training.	Data-driven science Use large amounts of data to discover patterns, and not only to test existing theories.
Data-driven development Use real usage and market data to choose what to build or improve next.	Digital decision-making Digital tools collect and analyze data quickly, so managers decide faster with better facts.

 

Big data analytics helps managers find patterns in very large data sets.

 

 ACTIVITY

Activity: gut feeling or data?

The situation A new, urgent style arrives. It can go to only one sewing line. The supervisor says: “I feel Line 4 is our best line.” The data says: over the last three months, Line 4 averaged 71% efficiency and Line 7 averaged 78%.

Your tasks 1.  Which would you trust, and why? 2.  What other data would you want before deciding? 3.  What decision would you make, and how will you check it afterward?

 

Suggested time: 10 minutes in small groups

 

 RECAP

Session 1 recap

 

Why data	Data is more reliable than intuition and protects decisions from bias and false assumptions.
What DDDM is	Collect data, extract patterns and facts, and use them to guide decisions.
The process	Define, collect, analyze, decide and act, then review the result.
Styles and approaches	Styles differ; data helps all of them. Move from avoiding and reacting to seeking.

 

About the author

Md. Tarikul Islam More than 15 years of experience in industrial engineering and planning in Bangladesh’s ready-made garment (RMG) industry. He is based in Dhaka and has led IE and planning work at Next Collections Ltd. (Hameem Group) in Ashulia since 2018. On this blog he shares practical apparel and sewing technical knowledge. He prepared this course so that teams and top management can make everyday decisions with facts and data, and not with guesswork alone. If you found this course useful, please share it with your team and leave a comment with your questions or your own experience. Happy learning!

•    Analyze how insights from business data drive strategy