Data Driven Insights

Data Driven Insights

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"Unlock the power of data! 📊 From Excel tricks to SQL queries and Python automation—master the skills that drive insights.

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20/06/2026

Prompting is not just writing a sentence.

It is a skill that improves in levels.

Beginner → simple task
Skilled → task + context
Advanced → task + context + output format
Specialist → add role and structure
Expert → add limits and stop rules
Elite → add reasoning and quality checks

The better your prompt, the clearer the output becomes.

A simple rule to remember:

Don’t just tell AI what to do.
Tell it the role, context, format, limits, and success criteria.

Save this as a quick prompting roadmap.

20/06/2026

VLOOKUP is one of the classic Excel lookup functions.

It searches for a value in the first column of a table and returns related information from a column to the right.

Example:

Product ID = P103
Return value = Price
Result = $500

A simple rule to remember:

VLOOKUP searches vertically
The lookup value must be in the first column
For exact matches, use FALSE

Useful for product prices, employee details, student grades, and table-based lookups.

Save this for your Excel formula toolkit.

20/06/2026

Duplicate emails can quietly create messy customer data.

This SQL example uses GROUP BY with HAVING to find email addresses used by more than one customer.

Simple idea:

Group the rows by email
Count how many times each email appears
Keep only the emails where the count is greater than 1

Perfect for checking duplicates before reporting, cleaning data, or fixing customer records.

Save this for your SQL revision.

Photos from Data Driven Insights 's post 20/06/2026

SQL Roadmap Series — Post 32
GROUP BY with aggregate functions helps you summarise data by category.
Instead of calculating one overall result, SQL can calculate a separate summary for each group.
For example, you can count employees by department, calculate total salary by department, or find the average salary for each department.
This is one of the most useful patterns for reports, dashboards, and real-world data analysis.
Save this post and follow the full SQL roadmap series.

20/06/2026

Excel, Python, and SQL can often solve the same data task.

The difference is choosing the right tool for the situation.

Excel → quick checks, small datasets, reports
Python → automation, flexible analysis, repeatable workflows
SQL → large datasets, databases, reliable querying

Common tasks like filtering, sorting, grouping, handling missing values, calculated columns, and joins can be done in all three.

The real skill is not picking one tool forever.
It is knowing which tool fits the job.

Save this for your next data project.

20/06/2026

Copilot Studio is becoming more than a chatbot builder.

The new direction is about building structured AI workflows with skills, memory, agent nodes, human oversight, and Microsoft 365 integration.

The key shift:

From simple bot conversations
To reusable AI workflow systems

This means agents can follow steps, use knowledge, call tools, remember context, and include human approval when needed.

For anyone working with automation, business apps, or Microsoft 365, Copilot Studio is worth watching closely.

Save this if you’re exploring AI workflows and business automation.

19/06/2026

IN and EXISTS can both help you check whether matching records exist in another table.

In simple cases, both can return the same result.

Use IN when you are checking whether a value appears in a list or subquery result.
Use EXISTS when you want to check whether at least one matching row exists.

The key idea:

IN checks matching values
EXISTS checks matching rows
NOT EXISTS is useful for finding missing records

A simple SQL pattern to remember when comparing tables.

Save this for your SQL problem-solving toolkit.

19/06/2026

SQL data cleaning is where messy data becomes usable data.

When raw CRM exports arrive with spaces, mixed casing, inconsistent phone formats, NULL values, and text-based dates, SQL functions help clean everything before analysis.

Useful functions to remember:

TRIM() removes hidden spaces
LOWER() standardises text
REPLACE() fixes inconsistent formats
CAST() converts data types
COALESCE() handles NULL values
NULLIF() prevents misleading values
CASE WHEN creates business-friendly labels
CONCAT() builds clean reporting fields

Clean data first.
Analyse second.

Save this for your next SQL project.

19/06/2026

XLOOKUP is the modern way to find matching values in Excel.

It searches for a value in one range and returns the related result from another range.

Example:

Product ID = P103
Return value = Price
Result = $500

Why XLOOKUP is useful:

It can return values from the left or right
It handles missing values more clearly
It replaces older lookup formulas in many cases
It makes lookup formulas easier to read

A simple rule to remember:

Use XLOOKUP when you want to search for one value and return related information from another column.

Save this for your Excel formula toolkit.

19/06/2026

Correlated subqueries are useful when the inner query depends on the current row from the outer query.

In this example, each employee is compared with the average salary of their own department.

That means the subquery is not calculated once for everyone — it is linked to each row being checked.

Use it when you need row-by-row comparison with related data.

Save this for your SQL revision.

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