Data Analyst | Business Intelligence Specialist | BI Analyst | SQL | Power BI | Python | Excel

Hi! My name is Mikhail.

I help my clients turn slow, confusing Power BI dashboards into tools that actually support decisions.
Most teams I work with already have data. The problem is different: reports are slow, KPIs are unclear or constantly debated, and data models are messy.

Dashboards look fine, but they do not change how people act.

That is where I usually come in.
What I focus on:

- Power BI performance and model design,
- Clear, decision-oriented KPIs,
- Simplifying complex data structures,
- Making reports fast, reliable, and easier to maintain.

I work mainly with SaaS and e-commerce companies, where data grows quickly and early bad modeling decisions will hurt later.

Experience and background:

I have 2+ years of hands-on experience working with SaaS and e-commerce data:
subscriptions, events, users, revenue, funnels, cohorts, and time-based analysis.

Earlier in my work, I built dashboards that looked good but were slow and hard to scale.
Over time, the focus shifted to creating less visual noise and more attention to modeling, relationships, and context.


A point of view:

Many teams underestimate data modeling and overcomplicate visuals. They spend time polishing charts while the underlying model stays bad. Clean modeling and clear definitions I implement solve more problems than another fancy chart type.


How I work with my clients:

- Power BI audits to identify performance and modeling issues.
- One-on-one consultations.
- End-to-end data analytics projects, from data model to published dashboard.

If you already feel that your reports are slower than they should be or that your dashboards do not help you decide faster, we can start with a simple review and go from there.

DM me if you want to discuss your situation.

How to Query Billions of Rows in Milliseconds Using SQL?

Find out when to use materialized views, how to refresh them, and which mistakes to avoid.

Most SQL queries slow down when they hit billions of rows. A GROUP BY query run on a large table can take minutes. 

Materialized views can fix this problem.

Today I will cover:

- The difference between standard and materialized views.
- When to use them.
- Three refresh strategies.
- Examples.
- Common mistakes and constraints.

If you work with large datasets and BI dashboards, this is worth a look.

🔻You can find a detailed guide in the comments below.

What is your biggest challenge with slow SQL queries?

Drop it in the comments.👇

Mikhail Mikushin

I turn data into insights, optimise processes, and automate tasks for businesses.

Technical Skills and tools: MySQL, Power BI, Python (Pandas, NumPy, SciPy, Matplotlib, Seaborn, Sklearn), VBA, Excel.

Data analytics is my passion, and I love creating reports, dashboards, data models, and performance insights. Over 12 years of experience in the telecommunications industry.

I have developed and integrated an automation process that led to reduce ordering and acceptance time by 75%.

I have organised the database, document flow, and Financial planning in SalesForce CRM.

I get excited about opportunities where I am able to leverage big data to discover insights and identify patterns that have real impact.

Besides my professional interests, I also have a range of hobbies that enrich my life and help me relax. I love cycling, brewing my own beer, experimenting with different flavours, playing electric guitar, and learning more about myself and others.

I love connecting with new people; give me a shout at mikhail.mikushin@yahoo.com or here on LinkedIn!

MATERIALIZED_VIEWS_IN_SQL

Data Analyst | Power BI, SQL & Python Expert | Business Intelligence Specialist | Freelance Consultant | Transforming Data into Insights

Stop Answering the Same Questions Twice: Build a Data Dictionary

The Lazy Analyst’s Guide to Power BI Documentation.

Today I will tell you how to build a proper Power BI data dictionary, including automated methods, AI-assisted descriptions, and best practices that save hours of going back and forth.

💡You can find a detailed guide in the comments section.

How do you document your Power BI measures and data model?

Let me know in the comments.👇

Data Dictionary

Data Monitoring Made Simple With Power BI.

Learn how alerts work after dataset refresh.

Today, I will show you four layers of alerts monitoring in Power BI: from simple visual cues to workflows with Power Automate and Fabric.

Here is a detailed guide: https://lnkd.in/dvVYycqN

What else do you want to see next?

Let me know in the comments👇

Power BI Alerts

Stop Overcomplicating Time Intelligence in Power BI!

When I started learning data analysis, I put single numbers in my KPI cards, which was a mistake.

A single metric alone tells you almost nothing. Is $30 000 in sales good? Bad? Who knows?

Metrics require context and a baseline. One way to get a baseline is to take values from the past.

Time intelligence is a period comparison :

- This year vs. last year.
- Month-to-date totals.
- Growth rates between periods.

Tools like Power BI have built-in features for this, and I am going to show you today how to do it the best way today.

Here is a detailed guide: https://lnkd.in/dHQnYYH5

What context do you add to your dashboards? Let me know below in the comments. 👇

#Data #PowerBI

Time Intelligence

Missing Data. The Problem Every Analyst Faces.

Why you should understand the role of missing data in model performance.

Today I will show you how to deal with missing values and help you get your data clean and show the true picture in analysis.

Here is a detailed guide: https://lnkd.in/dauMeVj4

What are your missing data strategies? 

Drop it in the comments .👇

Follow me for advanced SQL and analytics content.

Missing Data

The Secret of Getting Stakeholders to Trust Your Numbers.

A Simple Framework for the Most Complicated Part of Data Analysis.

Today I will show you the framework covering everything that happens after you have finished doing the technical part: how to understand your audience, structure your message, tell a story with data, and deal with stakeholder expectations.

💡You can find a detailed guide in the comments.

There is one question I keep coming back to at the end of every project:

What is the hardest part of communicating with non-technical stakeholders?

Tell me in the comments.👇

#DataAnalysis #DataCommunication #Storytelling

Data Communication

Clean Code Starts With Clean Names.

Why Returning to Your Own Code Feels Like Reading a Foreign Language? 

Today I will show you how to set up naming conventions across different data analytic tools, dashboards, and documentation.  

Here is a detailed guide: https://lnkd.in/d2tzMWj6

What naming rules have saved you the most time in your projects? 

Let me know in the comments👇

Naming Conventions

Bi Sunsoa

A Better Way to Match Records Across Systems and Databases.

Data in the real world is a mess. When you want to merge or clean datasets, exact matching doesn't work. Fuzzy matching is the best option in these situations.

Today I will show you how it works in SQL, Python, and Power BI and how to get the maximum out of that method.

Here is the detailed guide: https://lnkd.in/dNNxhMKw

Have you applied fuzzy matching in your projects?

Let me know in the comments. 👇

#Data #SQL #Python

Fuzzy Matching

Stop deleting outliers before you understand the story they tell.

Anomalies are the early signs of shifts, viral hits, or process failures.

My first thought when something is wrong with data is that I missed something or there is a mistake in the system. But I have learned that rushing to clean data is a trap.

An outlier is a data point that sits far from the rest. It might be a human error or a faulty sensor, but it could also be a rare, true event.

One extreme value skews your averages and KPIs, but it also reveals what is happening under the surface.

I use visual checks first. Scatter plots and Box plots show me what does not belong before I touch any formulas.
If the data is normal, I use a Z-score.
If it is skewed, I switch to IQR.

Finding an anomaly is the start, and you have to decide: is it a mistake to fix or an insight to highlight?

If you are building a model, remove it. 
If you are presenting to stakeholders, keep it and explain it.
Context is everything.

You are three subqueries deep, and your JOIN logic looks like a spider web.

You forgot what question you were trying to answer?

This is a common problem in data analysis.

Window functions can solve this. They let you perform complex calculations and keep every original row of data visible.

Unlike a GROUP BY, which collapses rows into one summary, window functions keep every row while adding calculated context columns.

You can show a salesperson’s performance alongside their team average with one line.

Window functions scan your data once, and it is not an exaggeration to say these have cut my query time by 30-40%.

Stop using nested subqueries.

Use a clean window function with clear PARTITION BY logic: https://lnkd.in/dpPXwF3y

taplio

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