How A Data Analyst Accidentally Became a Data Therapist

There was a time in my life when I believed data was honest.

Back then, I thought being a data analyst meant writing elegant SQL, building colorful dashboards, and occasionally explaining to someone why a pie chart was a bad idea. My datasets were clean. Column names were meaningful. Dates behaved like dates. Numbers behaved like numbers. If a query failed, it was usually my fault—and that felt fair.

I was young. I was optimistic. I had not yet met real-world data.

The Golden Age (Also Known as the Tutorial Phase)

In the beginning, everything worked. The database had one source of truth. The schema made sense. Tables were normalized, keys were consistent, and business definitions were written down somewhere that actually existed. It was called my LinkedIn Learning and Udemy phase.

I would run a query and get an answer. A real answer. On the first try.

I thought: Is this what being “good at data” feels like?

Then the universe laughed.

The First Crack: “Why Is the Doctor Called Dr. Dr. A?”

It started with a harmless column called Doctor Name.

At first glance, it looked fine. But when I actually looked at the values, I saw things like:

• Dr. A B C

• Dr. Dr. A

• DRABC

• A, Dr

Same doctor. Multiple personalities.

This is when I learned my first important lesson:

Free-text fields are where consistency goes to die.

Somewhere, someone typed “Dr.” twice and no system stopped them. Somewhere else, someone decided punctuation was optional. And now, years later, I was expected to magically reconcile it.

I stopped smiling a little.

When Columns Have Identity Issues

Just as I was finishing the great Doctor Name Cleanup of 2025, I discovered something beautiful.

Data A had a column called Client.

Data B had a column called Organization.

Different tables. Different systems. Different teams.

Same thing.

One lived in Column 2.

The other lived in Column 24.

No documentation. No comments. Just vibes.

This is the phase where you stop being a data analyst and become a detective. Or an archaeologist. Or both.

You don’t analyze data anymore. You interpret intent.

Not All Organizations Are Created Equal

Next came the organizations.

Some were digitally mature. Clean systems. IDs everywhere. Structured data flowing in neatly like a textbook example.

Others looked like they were powered by spreadsheets that had survived floods, migrations, and at least three people who “just added one column”.

Same business.

Same KPIs.

Wildly different data quality.

And there I was, expected to compare them fairly in one dashboard.

This is when my anxiety crossed from mild concern to visible forehead tension.

The Great Merge (Also Known as “We’ll Fix It Later”)

At some point, someone said the words every analyst fears:

“Let’s just merge it.”

Dates.

Numbers.

Text.

Flags.

All lovingly pushed into a single column because “we’ll clean it later”.

My laptop started heating up like it was training an AI model instead of running SQL. Fans screamed. Queries slowed. I stopped drinking coffee and started drinking water. Lots of water.

This is when you realize:

Technical debt is not a future problem. It is a present lifestyle.

Multiple Databases, One Analyst, Zero Sleep

Then came the databases.

Not one.

Not two.

Multiple.

• On-premises

• Cloud

• CSVs emailed weekly

• One mysterious source nobody wanted to explain but everyone wanted included

  
  

Each had different keys, refresh times, and definitions of truth.

Somewhere between the fourth join and the fifth reconciliation mismatch, I fainted mentally.

The laptop did not.

R.I.P. Analyst, Long Live the Databases

Eventually, it became clear that the databases were immortal.

Locked.

Secured.

Guarded by permissions tighter than Fort Knox, protected by access controls, approval workflows, VPNs, jump servers, and a ticketing system that required three managers, one escalation, and a mild existential crisis.

The databases were treated like crown jewels.

The analyst?

Entirely replaceable.

When the analyst left, the databases remained — silent, encrypted, and smug. Context was never documented. Assumptions lived in people’s heads. Knowledge was passed down verbally, usually starting with “I think this column means…”

No one knew why a join existed.

No one remembered who created the table.

But everyone agreed it was too risky to touch.

Wisdom was lost.

Queries broke.

New analysts stared into the abyss.

R.I.P. Analyst.

Long live the databases.

The Alternate Universe Where Anxiety Drops (But Not to Zero)

Now imagine a different world.

No, not a fantasy. Just… slightly better governance.

Organizations are digitally ready.

Definitions are agreed upon before dashboards are built.

Schemas are enforced.

Data contracts exist and—plot twist—people actually follow them.

The analyst is still busy. Very busy.

But no longer staring at the screen like it personally betrayed them.

There are still bugs.

There are still edge cases.

There is still at least one query that runs suspiciously long.

But the panic is gone.

Anxiety doesn’t vanish—it just drops from “send help” to “this is fine.”

And honestly, in the world of data… that’s success.