Power BI Sales Dashboard
Interactive sales dashboard built in Power BI, visualizing KPIs and trends for business insights.
Turning messy data into clear decisions
I transform raw, chaotic data into actionable insights that drive business growth.
From building dynamic KPI dashboards to uncovering trends with SQL, I deliver clarity
and impact through Tableau, Power BI, and Excel.
Let’s turn
your data into a compelling story that inspires action!
SQL (MySQL)
Tableau
Power BI
Excel
KPI Dashboards
Featured Projects
Assignments Tableau Dashboard (Superstore)
Tableau dashboard showcasing assignment analytics and interactive data exploration.
Open in Tableau PublicKing County Real Estate Dashboard
Interactive Tableau dashboard analyzing King County real estate data for trends and insights.
Open in Tableau PublicSkills
SQL (MySQL)
Tableau
Power BI
Excel (Pivot, PowerQuery)
Data Cleaning
Visualization
A/B Testing
Basic Statistics
Experience
Data Analyst Intern — AI Variant
- Built dashboard prototypes and performed ad‑hoc analyses to guide decisions.
- Collaborated with cross‑functional teams on data cleaning and visualization.
Intern — SBI Mutual Fund
- Supported data collection and investor outreach during a short internship.
Education
M.A., Economics — Christ University, Bangalore (2019–2021)
B.B.A. — G.D. Rungta College of Science & Technology, Bhilai (2015–2018)
Visharad — Indira Kala Sangeet Vishwavidyalaya, Khairagarh
Certifications
Data Analytics Certification Program — ExcelR (Issued Jun 2025)
Contact
Email: mridu.gurudwan@gmail.com
LinkedIn: mridu-krishna-gurudwan
Blogs
The Power of Data Cleaning: Why It’s the Foundation of Great Analytics
“Garbage in, garbage out.” Clean data drives accurate insights, trusted decisions, and faster analysis…
SQL Window Functions: A Practical Cheatsheet for Analysts
Ranking, running totals, moving averages—learn the window patterns I use to turn raw tables into insight…
Designing KPI Dashboards Leaders Actually Use (Power BI & Tableau)
A simple framework to pick the right KPIs, declutter visuals, and ship dashboards that drive action…
The Power of Data Cleaning: Why It’s the Foundation of Great Analytics
Posted by Mridu Krishna Gurudwan
When it comes to data analysis, the phrase “garbage in, garbage out” couldn’t be more true. No matter how advanced your analytics tools or how sophisticated your models, if your data is messy, your insights will be unreliable. That’s why data cleaning is the unsung hero of every successful data project.
What is Data Cleaning?
Data cleaning (or data cleansing) is the process of identifying and correcting (or removing) errors and inconsistencies in data to improve its quality. This step is essential before any meaningful analysis, visualization, or machine learning can take place.
Why it matters
- Accuracy: Reflect reality, not noise or typos.
- Speed: Clean, typed data is faster to analyze and model.
- Trust: Stakeholders believe what they can validate.
- Decisions: Less rework, more action.
Common tasks
- Handle missing values (drop or impute with mean/median/logic).
- Remove duplicates and standardize formats (dates, casing, categories).
- Detect outliers (IQR/Z-score) and decide to cap, transform, or exclude.
- Fix data types (text → date/number/boolean) and validate ranges.
Tools I use
- Excel: Filters, conditional formatting,
TRIM,CLEAN, Remove Duplicates. - SQL: CTE pipelines to de-dupe, standardize, and validate at source.
- Python (Pandas):
.dropna(),.fillna(),.duplicated(),.astype(). - Power BI/Tableau Prep: Transform before viz for consistency.
Remember: Clean data isn’t a chore—it’s a competitive advantage.
Want to see it in action? Check out my Power BI and Tableau dashboards.
SQL Window Functions: A Practical Cheatsheet for Analysts
Posted by Mridu Krishna Gurudwan
Window functions let you compute metrics across related rows without collapsing them. Here are the patterns I use most in analytics work.
Setup
-- Example sales table
-- sales(order_id, order_date, customer_id, region, amount)
1) Running totals
SELECT
order_date,
amount,
SUM(amount) OVER (ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_sales
FROM sales;
2) Rank / Dense Rank (Top-N)
SELECT
customer_id,
SUM(amount) AS total_spend,
DENSE_RANK() OVER (ORDER BY SUM(amount) DESC) AS spend_rank
FROM sales
GROUP BY customer_id;
3) Partitioned metrics (per region)
SELECT
region,
order_date,
amount,
SUM(amount) OVER (PARTITION BY region ORDER BY order_date) AS running_region_sales
FROM sales;
4) Moving averages (trend smoothing)
SELECT
order_date,
amount,
AVG(amount) OVER (ORDER BY order_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d
FROM daily_sales;
5) Previous / Next row (period-over-period)
WITH daily AS (
SELECT order_date, SUM(amount) AS sales
FROM sales
GROUP BY order_date
)
SELECT
order_date,
sales,
LAG(sales, 1) OVER (ORDER BY order_date) AS prev_sales,
ROUND(100.0 * (sales - LAG(sales,1) OVER (ORDER BY order_date))
/ NULLIF(LAG(sales,1) OVER (ORDER BY order_date),0), 2) AS growth_pct
FROM daily;
Tips
- Always define a stable ORDER BY (e.g., date + id).
- Use
PARTITION BYto compute per-segment metrics without extra joins. - Guard percent changes with
NULLIF(x,0)to avoid division by zero. - Start with CTEs to keep logic readable and testable.
Designing KPI Dashboards Leaders Actually Use (Power BI & Tableau)
Posted by Mridu Krishna Gurudwan
The best dashboards answer three questions fast: Where are we? Why? What next? Here’s my simple approach to build dashboards that drive action.
1) Choose meaningful KPIs
- Map KPIs to outcomes (e.g., revenue, retention, cycle time).
- Limit to 5–7 top KPIs; put the rest in drill-downs.
- Define clear formulas, owners, and refresh cadence.
2) Layout that guides the eye
- Top: KPI scorecards with trend and vs target.
- Middle: Diagnostic visuals (segments, funnels, cohorts).
- Bottom: Detail tables for investigation.
3) Visual best practices
- Use consistent scales, minimal gridlines, and plain titles.
- Prefer bars/lines over gauges and novelty charts.
- Color encodes meaning (green/on-track, amber/warning, red/off-track).
4) Interactivity with purpose
- Filters for audience-relevant slices (region, segment, product).
- Drill-through to explanations, not just raw tables.
- Bookmark common views for exec reviews.
5) Rollout & adoption
- Ship a one-page “how to read this dashboard”.
- Instrument usage; prune what’s not used.
- Automate refresh and alert on threshold changes.
I apply this framework in my Power BI and Tableau projects to make sure the dashboards don’t just look good—they change behavior.