Hi, I'm Pelumi.
As a data analyst, I specialize in collecting, analyzing, and transforming complex datasets into meaningful insights that drive smart business decisions. With a strong foundation in statistics and data visualization, I help stakeholders across various industries uncover trends, optimize processes, and make data-driven choices with confidence.

My journey into data began in Geo-informatics Engineering, where I discovered a passion for pattern recognition and relational mathematics. This sparked my interest in big data and the power of analytics. Beyond crunching numbers, I enjoy translating insights into compelling narratives, ensuring that data doesn’t just inform, it inspires action.

I thrive in structured, collaborative environments where ideas turn into impactful solutions. Detail-oriented and highly organized, I believe that great results come from great minds working together toward achieving meaningful goals.

My goal is to help business/people make right data-driven decisions.

Analytic Skills
•Excel Power User: Formulas, pivot tables, lookup functions and dashboard design
• SQL Expert: EDA, Window functions, Joins, CTEs and Subqueries
• Data Visualization & Storytelling: Dashboard design, automation and insight communication
• Python: Pandas, NumPy, Matplotlib and Seaborn
• R: Dyplr, ggplot, tidyr and lubridate

Technical Tools
• Database and Report Querying: Postgres, Google Big Query, Azure Data Studio, SQL Server
• Data Analysis : SQL, Python, R, Excel
• Data Visualization : Tableau, Microsoft PowerBI
• GIS: ArcMap, Envi, SAGA GIS

 Competencies
•Data Mining
•Data Analysis & Visualization
•Data Warehousing
•Critical Thinking
•Trend and Pattern Recognition
•Problem Solving
•Spatial Data Analysis
•Digital Mapping

I'm open to work and to connect

Email: shittuoluwadamilarepelumi@gmail.com

Let's Connect!

Hi #datafam🤗🤗!!

I am participating in FP20 Analytics Challenge 35

𝐆𝐥𝐨𝐛𝐚𝐥 𝐁𝟐𝐁 𝐒𝐚𝐥𝐞𝐬 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 & 𝐃𝐞𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
Understanding the health of a global B2B sales pipeline is key to making data-driven decisions in enterprise sales
A sales pipeline captures every opportunity, from the very first engagement to the moment a deal is won or lost. But not all deals progress equally: some move quickly, others stall, and some carry higher risk due to lack of activity or engagement.
By analyzing pipeline stages, deal age, engagement frequency, company size, and regional distribution, we can identify:
 
• Which deals are high-risk and need attention
• Which SDRs or regions are performing exceptionally
• Where the pipeline can be optimized for higher win rates

Visualizing these insights allows sales leaders to prioritize follow-ups, allocate resources effectively, and ultimately increase revenue.

In essence, a well-monitored sales pipeline isn’t just a list of deals—it’s a strategic tool that turns data into decisions.
Analytics + visualization = smarter sales decisions.

Inspired by Jhonathan Alexander Gonzales Piza Sales dashboard theme
Federico Pastor Enterprise DNA ZoomCharts

#FP20Analytics #FP20SalesPipeline #dataanalytics #datavizchallenge #powerbi #enterprisedata

Oluwadamilare Shittu

Business Intelligence Analyst | Retail & eCommerce | Unlocking Insights with Impactful Tableau Dashboards | 2xVOTD | SQL | Excel

Tech Lead at Hiberus | Dataviz Developer | Community Leader | Inforiver Ambassador | 2020 Maven Analytics "Hall of Fame" Award Recipient

Enterprise DNA

ZoomCharts

Data Analyst | R | Power BI | SQL | Python| Tableau |Excel| Turning Data Into Decisions

Hey #datafam🤗🤗

Submitting my entry for the Onyx Data January-February #dataDNA 
Pharmacy Sales & Profitability Analytics Challenge.

The aim of this challenge was to analyze a dataset representing an European pharmacy chain distributor operating across multiple European countries and  build a Power BI report that helps stakeholders understand:
 • How sales and profitability vary across countries, regions, and individual pharmacies
 • How different product categories and brands perform in different locations
 • How regional performance contributes to overall business results.

📋 𝐈𝐧𝐬𝐢𝐠𝐡𝐭⁣s
€𝟖.𝟔𝟑𝐌 in revenue generated across 𝟏𝟐𝟎 pharmacies in 𝟖 countries, delivering a strong 𝟐𝟖% profit margin. Performance shows steady 𝐦𝐨𝐧𝐭𝐡-𝐨𝐯𝐞𝐫-𝐦𝐨𝐧𝐭𝐡 growth, driven by high-volume 𝐮𝐫𝐛𝐚𝐧 𝐩𝐡𝐚𝐫𝐦𝐚𝐜𝐢𝐞𝐬 and leading 𝐩𝐫𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 products.

Onyx Data, ZoomCharts, Smart Frames UI, Data Career Jumpstart

#datadna #powerbi #onyxdata #datavizchallenge #dataanalytics #executivedashboard #salesdashboard #pharmanalytics

Onyx Data

Smart Frames UI

Data Career Jumpstart

Sales Dashboard

Thank you Federico Pastor and the FP20 Analytics Challenges Group for the awesome challenges as always..

FP20 Analytics Challenges Group

Here’s a SQL trick many  overlook—but it can save hours and simplify complex queries: using 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 with 𝐑𝐎𝐋𝐋𝐔𝐏 for hierarchical summaries. 

𝐑𝐎𝐋𝐋𝐔𝐏 lets you calculate subtotals and grand totals in a single query, perfect for reporting across multiple dimensions without writing multiple aggregations.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Summarize sales by region and product category, including totals:

✅ Single query → subtotals & grand total
✅ Reduces multiple aggregations or unions
✅ Great for dashboards, reports, and executive summaries

Data is only as valuable as the model behind it.

𝐃𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 is the practice of structuring and organizing data to ensure it can answer business questions accurately and efficiently. A strong model isn’t just about tables and relationships—it’s about creating a framework where insights are reliable and actionable.

Why it matters:

𝐅𝐚𝐬𝐭𝐞𝐫 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Clean, structured data reduces the time analysts spend preparing datasets.

𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: Well-defined relationships prevent errors in reports and dashboards.

𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: As data grows, a solid model supports more complex queries without slowing down performance.

𝐁𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Insights derived from structured data allow leadership to act confidently, whether it’s forecasting sales, optimizing inventory, or identifying trends.

Example:
In a sales scenario, a dimensional model in Power BI might separate facts (sales transactions) from dimensions (customers, products, time). This allows the team to:

 • Track trends by product category or region
 • Identify top-performing customers
 • Forecast demand without manual spreadsheet wrangling

Ultimately, data modeling transforms raw data into a strategic asset—saving time, reducing errors, and enabling smarter, data-driven decisions.

#data #datamodelling

You build a dashboard, run a query, or refresh a report… and then you notice it: numbers that don’t make sense.

Before panicking, a good analyst asks: “𝘞𝘩𝘦𝘳𝘦 𝘤𝘰𝘶𝘭𝘥 𝘵𝘩𝘪𝘴 𝘥𝘪𝘴𝘤𝘳𝘦𝘱𝘢𝘯𝘤𝘺 𝘤𝘰𝘮𝘦 𝘧𝘳𝘰𝘮?”

𝐂𝐨𝐦𝐦𝐨𝐧 𝐜𝐚𝐮𝐬𝐞𝐬 𝐨𝐟 𝐮𝐧𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐫𝐞𝐬𝐮𝐥𝐭𝐬:

 • Joins gone wrong: Duplicate rows or missing matches
 • Incorrect aggregations: Summing instead of averaging, or vice versa
 • Missing or duplicate data: Nulls, blanks, or repeated entries
 • Unit mismatches: Dollars vs thousands, percentages vs decimals
 • Filters not applied consistently: Some reports include or exclude data unintentionally

𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐝𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐬𝐭𝐞𝐩𝐬:

 • Compare row counts before and after transformations
 • Spot-check totals or sample rows
 • Check joins and relationships carefully
 • Validate assumptions (timeframes, units, categories)

 Debugging isn’t just fixing mistakes.
It’s about understanding your data, validating assumptions, and building trust in your outputs.

Not all metrics are created equal. Are your KPIs truly telling you what you need to know?

𝐊𝐞𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬 (KPIs) are more than just numbers—they are the metrics that directly measure progress toward business objectives. Choosing the right KPIs is essential for informed decision-making.

A few important points to consider:

𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐨𝐯𝐞𝐫 𝐯𝐨𝐥𝐮𝐦𝐞: Tracking every available metric can create noise. Focus on indicators that truly impact business goals.

𝐀𝐯𝐨𝐢𝐝 𝐯𝐚𝐧𝐢𝐭𝐲 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: High-level numbers like page views or social likes may look impressive, but they don’t always translate to actionable insights.

𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: KPIs should reflect what drives success. For example, in retail, tracking actual sales is more meaningful than simply measuring foot traffic.

𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Every KPI should guide a decision or action. If it doesn’t, it’s just data clutter.

The right KPIs transform raw data into strategic insight, helping teams focus on what matters most and drive measurable outcomes.

Some concepts exist independently of tools.

One of them is the idea of looking back —
accessing a previous value without losing the current row.

Both SQL and Python handle this in their own way.
In SQL: 𝐋𝐀𝐆()

𝐋𝐀𝐆() is a window function that lets you access a value from a previous row while keeping the current row intact.

It’s especially useful when you need:

• Period-over-period comparisons
• Change detection
• Trend analysis without aggregations

The key idea: context across rows, not collapsed results.

In Python (𝐩𝐚𝐧𝐝𝐚𝐬): .𝐬𝐡𝐢𝐟𝐭()
.𝐬𝐡𝐢𝐟𝐭() serves a similar purpose.
It repositions data up or down, allowing you to compare a row with what came before (or after) it.

It comes in handy when:
 • Working with time series
 • Calculating deltas
 • Validating trends row by row

A common analytical problem looks like this:
𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘵𝘰𝘥𝘢𝘺’𝘴 𝘷𝘢𝘭𝘶𝘦 𝘤𝘰𝘮𝘱𝘢𝘳𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 𝘰𝘯𝘦?

#sql #python

Ever updated a table or column and found dashboards or reports breaking unexpectedly?

That’s why 𝐝𝐚𝐭𝐚 𝐥𝐢𝐧𝐞𝐚𝐠𝐞 𝐚𝐧𝐝 𝐢𝐦𝐩𝐚𝐜𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 are essential.

𝐃𝐚𝐭𝐚 𝐥𝐢𝐧𝐞𝐚𝐠𝐞: Maps where your data comes from, how it flows, and where it ends up.

𝐈𝐦𝐩𝐚𝐜𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Shows which reports or processes will be affected before you make changes.

𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:

📌Prevents accidental breakages
📌Makes collaboration safer across teams
📌Saves hours troubleshooting downstream issues

𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐬𝐭𝐞𝐩𝐬:

 • Document table → column → report dependencies
 • Visualize flows using diagrams or BI tools
 • Flag high-risk fields before modifying them
 • Always check: “Who depends on this?”

Even small changes can have wide effects.
Knowing your data’s lineage ensures your work is safe, trusted, and impactful.

In analytics, the concept often matters more than the tool.

Once you understand the pattern, SQL, Python, or a BI tool are just different ways to implement it.

Examples of common patterns:

𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐚𝐯𝐞𝐫𝐚𝐠𝐞𝐬 / 𝐦𝐨𝐯𝐢𝐧𝐠 𝐭𝐫𝐞𝐧𝐝𝐬:

𝐒𝐐𝐋: AVG(value) OVER(ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)

𝐏𝐲𝐭𝐡𝐨𝐧 (𝐩𝐚𝐧𝐝𝐚𝐬): df['value'].rolling(7).mean()

𝐏𝐨𝐰𝐞𝐫 𝐁𝐈: AVERAGEX(LASTN(7, Table), Table[Value])

𝐑𝐨𝐰-𝐭𝐨-𝐫𝐨𝐰 𝐜𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧𝐬:

𝐒𝐐𝐋: LAG()

𝐏𝐲𝐭𝐡𝐨𝐧: .shift()

𝐁𝐈 𝐓𝐨𝐨𝐥𝐬: Calculated columns with previous value functions

𝐑𝐚𝐧𝐤𝐢𝐧𝐠 / 𝐩𝐞𝐫𝐜𝐞𝐧𝐭𝐢𝐥𝐞𝐬:

𝐒𝐐𝐋: RANK() OVER(ORDER BY value DESC)

𝐏𝐲𝐭𝐡𝐨𝐧: df['value'].rank(method='min', ascending=False)

𝐏𝐨𝐰𝐞𝐫 𝐁𝐈: RANKX(Table, Table[Value], , DESC)

𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:

 • Recognizing patterns lets you switch tools confidently
 • Saves time — you stop memorizing syntax and start thinking conceptually
 • Makes collaboration across teams using different tools easier

💡Focus on the problem first, the tool second. Once you see the pattern, syntax becomes trivial.

Here’s a SQL trick that most isnt quite common—but it can save you multiple joins and a lot of headache: 𝐂𝐑𝐎𝐒𝐒 𝐀𝐏𝐏𝐋𝐘. ⚡

𝐂𝐑𝐎𝐒𝐒 𝐀𝐏𝐏𝐋𝐘 lets you run a subquery for each row in your main table, giving you powerful ways to transform or expand data on the fly.

Example: Get the top 2 products by revenue for each customer:

 ✅ No messy joins
 ✅ One query, clear results
 ✅ Works beautifully for ranking, filtering, or extracting related rows per group

Other uses:
Breaking JSON/XML arrays into rows
Getting the latest or top N related records without subqueries
Dynamic calculations per row

A dashboard is only as useful as its ability to keep up with the data.

Static reports might look polished, but the moment data changes, they become outdated. That’s why dynamic reporting patterns matter.

What makes a report dynamic?
✅𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Let users change filters (dates, regions, categories) without editing the query.
✅𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐫𝐞𝐟𝐫𝐞𝐬𝐡: Only new or changed data loads, keeping dashboards fast.
✅𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞𝐝 𝐟𝐢𝐞𝐥𝐝𝐬 & 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬: Automate common computations so numbers always reflect the latest data.

Why it matters:

•Reduces manual updates
•Ensures stakeholders always see current insights
•Saves hours of repetitive work

𝐀𝐜𝐫𝐨𝐬𝐬 𝐭𝐨𝐨𝐥𝐬:

𝐒𝐐𝐋 Use parameterized queries or views for dynamic filters
𝐏𝐨𝐰𝐞𝐫 𝐁𝐈: Leverage slicers, calculated measures, and incremental refresh
𝐏𝐲𝐭𝐡𝐨𝐧 Automate reports using scripts that pull and process fresh data regularly

💡Think of dashboards as living tools, not static snapshots. The goal is to make insights accessible, up-to-date, and actionable.

Even perfect data can tell the wrong story if bias creeps in.

𝐃𝐚𝐭𝐚 𝐞𝐭𝐡𝐢𝐜𝐬 isn’t just a buzzword—it’s a critical part of analytics. Decisions based on flawed, biased, or incomplete data can lead to unintended consequences, from skewed business strategies to unfair outcomes in hiring or lending.

A few points to consider:

𝐃𝐚𝐭𝐚 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: If your dataset isn’t representative, your insights won’t be either.

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐛𝐢𝐚𝐬: Models can unintentionally reinforce existing biases if trained on biased data.

𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲: Clear documentation of assumptions, sources, and methods helps stakeholders trust your analysis.

Ethical analytics ensures that insights are not just accurate but also fair and responsible, ultimately supporting decisions that benefit both business and society.

 How do you identify and mitigate bias in your datasets or models?

#dataethics

Numbers alone don’t persuade—stories do. 📊✨

𝐃𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 is about turning raw datasets into insights that influence decisions. It’s not just charts and tables; it’s framing data in a way that answers the “so what?” question for your audience.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
A retail company noticed flat sales over a quarter. Raw data alone didn’t explain why. By analyzing:

 ✅ Customer segments
 ✅ Purchase frequency
 ✅ Product categories

The team discovered that repeat customers were buying less in a specific product category, while new customers were driving most purchases. Visualizing this trend with a clear narrative helped leadership decide to run targeted loyalty campaigns, increasing repeat sales by 15% the next quarter.

𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Data becomes actionable when it’s structured, visualized, and framed as a story.

#datastorytelling

𝐃𝐚𝐭𝐚 𝐢𝐧 𝐚𝐜𝐭𝐢𝐨𝐧: Turning a business challenge into measurable results. 📊✨⁣
⁣
𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: A mid-sized e-commerce company noticed a decline in repeat purchases, but the reason wasn’t obvious from raw sales data.⁣
⁣
𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡:⁣
⁣
 • Segmented customers by purchase frequency and average order value⁣
 • Analyzed product categories with declining repeat purchases⁣
 • Cross-referenced with marketing campaign engagement⁣
⁣
𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Repeat customers were reducing purchases primarily in one product category, while new customers drove most of the growth.⁣
⁣
𝐀𝐜𝐭𝐢𝐨𝐧: Targeted loyalty campaigns for repeat customers in the underperforming category and optimized recommendations for new customers.⁣
⁣
𝐑𝐞𝐬𝐮𝐥𝐭: Within one quarter:⁣
⁣
 ✅ Repeat purchases increased by 15%⁣
 ✅ Customer retention improved by 8%⁣
 ✅ Marketing ROI improved by 12%⁣
⁣
𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Analytics isn’t just about reporting numbers—it’s about uncovering patterns, generating insights, and driving actionable business decisions.

Not all metrics are created equal—some can mislead decision-makers if interpreted incorrectly. ⚠️⁣
⁣
𝐂𝐨𝐦𝐦𝐨𝐧 𝐩𝐢𝐭𝐟𝐚𝐥𝐥𝐬:⁣
⁣
𝐕𝐚𝐧𝐢𝐭𝐲 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: High page views or social likes may look impressive, but they don’t indicate real engagement or conversions.⁣
⁣
𝐀𝐯𝐞𝐫𝐚𝐠𝐞𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐨𝐧𝐭𝐞𝐱𝐭: Average order value can hide large disparities—median or segmentation often tells a more accurate story.⁣
⁣
𝐘𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐜𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐬𝐞𝐚𝐬𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭: Sales might look flat or declining when seasonal trends are ignored.⁣
⁣
Better approach:⁣
⁣
 ✅ Align metrics with business objectives⁣
 ✅ Segment data to uncover hidden patterns⁣
 ✅ Use context-aware calculations (like YoY growth adjusted for seasonality)⁣
⁣
𝐄𝐱𝐚𝐦𝐩𝐥𝐞:⁣
Instead of reporting a simple average order value, segment by customer type:⁣
⁣
𝐍𝐞𝐰 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬: $50⁣
⁣
𝐑𝐞𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬: $120⁣
This highlights where revenue growth is truly coming from and guides targeted actions.⁣
⁣
𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Metrics only matter when they accurately reflect business performance and support informed decisions.

Ever had that moment when you reach for your favorite snack… and it’s on sale? 🍫⁣
Or when your streaming app suggests exactly the show you didn’t know you wanted to binge? 📺⁣
⁣
It feels like magic, but it’s really just… data. Quiet, invisible, shaping tiny decisions we barely notice every day.⁣
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𝐓𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭:⁣
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Grocery stores arrange aisles to nudge what we pick 🛒⁣
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Playlists that seem to read your mind 🎵⁣
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Traffic apps predicting the fastest route based on everyone else’s choices 🚗⁣
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It’s a little eerie, a little fascinating—and a reminder that data isn’t just numbers. It’s storytelling, influence… and sometimes, a secret companion in our daily lives.⁣
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Here’s my question: when did you first notice that data was quietly shaping your world? 

#data

Think analysts just make charts? Think again. 📊

There are a lot of myths floating around about what “analytics work” really looks like. Here are a few that I hear all the time:

𝐌𝐲𝐭𝐡: Analysts only make pretty charts.
𝐑𝐞𝐚𝐥𝐢𝐭𝐲: Charts are just the tip of the iceberg. We tell stories with data that influence real business decisions.

𝐌𝐲𝐭𝐡: You need a PhD to work in analytics.
𝐑𝐞𝐚𝐥𝐢𝐭𝐲: Curiosity, problem-solving, and asking the right questions go much further than a title.

𝐌𝐲𝐭𝐡: Analytics is all about numbers.
𝐑𝐞𝐚𝐥𝐢𝐭𝐲: Context, communication, and understanding people are just as critical as crunching data.

Analytics isn’t a job—it’s a way of seeing the world differently. 🌍

What’s the biggest analytics myth you’ve ever heard? Or have you had one of these “aha” moments where reality completely surprised you?

#data

The analytics landscape is evolving rapidly, and staying ahead requires exploring emerging tools and techniques that amplify decision-making.

One area gaining significant traction: 𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬. These tools combine advanced algorithms with automation to help analysts extract actionable insights from raw data—faster and more accurately than traditional methods.

How AI-assisted analytics adds value:

𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Generates trends, patterns, and anomalies automatically, reducing manual exploration.

𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Enables forecasting outcomes, such as sales trends, customer churn, or inventory needs.

𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Automates repetitive tasks, freeing analysts to focus on strategic interpretation and business recommendations.

𝐃𝐞𝐦𝐨𝐜𝐫𝐚𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐝𝐚𝐭𝐚: Allows non-technical stakeholders to interact with dashboards and gain insights with minimal training.

𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
A retail company using AI-assisted dashboards identified a drop in repeat customer purchases in specific regions. The tool suggested scenarios for corrective actions—allowing the team to test and implement targeted loyalty campaigns without building complex models manually. The result: a measurable increase in repeat purchases by 15% within one quarter.

Emerging analytics tools aren’t replacing analysts—they’re enhancing their impact, enabling faster, smarter, and more precise data-driven decisions.

taplio

Oluwadamilare Shittu's Linkedin Analytics