PJ

Day 50/120✅ Long time no posts…..but better late than never right? Laptop got spoil which put my learning on hold for sometime. Here’s what I’ve done so far: I started a guided project with @Rita_tyna in Power BI.

Day 72/120: Overview page of the Supplier Performance Dashboard. Worked through DAX, time intelligence, parameters, charts, and dashboard design. Challenging, but one of the most practical projects so far. #DataAnalyticsLockedIn

I honestly agree with you What do you think about this I just posted it on my LinkedIn

My Twitter interactions circle but I call you all family. One cold malt for each of you 🍺 Say hi if you can see yourself here

Prepping for my internship involves me practicing mid tier SQL questions Ngl they felt more high tier than mid tier to me 😹💀

Charts in excel is all about story telling, visualizing your data for better understanding

This stage helped me move from just building measures to actually seeing how they shape performance insights on a dashboard. @Rita_tyna #DataAnalyticsLockedIn #120DaysOfDataWithTina

I also learned how the VAR function helps store calculations and makes DAX measures cleaner and easier to read. Then I added everything into KPI cards so the changes in performance could be seen instantly on the dashboard.

Day 50/120 – Guided project I continued building my retail dashboard in Power BI and started connecting more meaning to the numbers I've been working with.

Good morning folks. It’s going to be a great week.

I find the first 3 chapters relevant for Beginners in Data. It expands your view of how you should see data at first glance.

Learning Power Bi without Structure? I got you covered PowerBI-Learning-Roadmap/ │ ├── 01_Foundation_of_BI/ │ ├── What_is_Business_Intelligence │ ├── Data_vs_Information_vs_Insights │ ├── KPI_and_Metrics │ ├── Reporting_vs_Analytics │ ├── OLTP_vs_OLAP │ └── Data_Driven_Decision_Making │ ├── 02_PowerBI_Introduction/ │ ├── PowerBI_Desktop │ ├── PowerBI_Service │ ├── PowerBI_Mobile │ ├── PowerBI_Gateway │ ├── Licensing_Concepts │ └── PowerBI_Ecosystem │ ├── 03_Data_Sources_and_Connections/ │ ├── Excel │ ├── CSV_and_Text │ ├── SQL_Server │ ├── APIs │ ├── SharePoint │ ├── Azure_Sources │ ├── Folder_Connections │ └── Web_Data │ ├── 04_Power_Query_ETL/ │ ├── Query_Editor │ ├── Data_Cleaning │ ├── Remove_Duplicates │ ├── Merge_Queries │ ├── Append_Queries │ ├── Pivot_and_Unpivot │ ├── Conditional_Columns │ ├── Custom_Columns │ ├── Parameters │ ├── Data_Types │ ├── Error_Handling │ └── M_Language_Basics │ ├── 05_Data_Modeling/ │ ├── Tables_and_Relationships │ ├── Primary_and_Foreign_Keys │ ├── Cardinality │ ├── Filter_Direction │ ├── Star_Schema │ ├── Snowflake_Schema │ ├── Fact_and_Dimension_Tables │ ├── Data_Granularity │ ├── Bridge_Tables │ ├── Calendar_Table │ └── Model_Optimization │ ├── 06_DAX_Fundamentals/ │ ├── Calculated_Columns │ ├── Measures │ ├── Aggregation_Functions │ ├── IF_and_SWITCH │ ├── Variables │ ├── Filter_Context │ ├── Row_Context │ ├── CALCULATE_Function │ ├── RELATED_and_LOOKUPVALUE │ └── Time_Intelligence_Basics │ ├── 07_Intermediate_DAX/ │ ├── Advanced_CALCULATE │ ├── ALL_and_ALLEXCEPT │ ├── FILTER_Function │ ├── Iterators_SUMX_AVERAGEX │ ├── Ranking_Functions │ ├── Dynamic_Measures │ ├── Running_Totals │ ├── Year_to_Date │ ├── Month_to_Date │ ├── Previous_Period_Analysis │ └── KPI_Calculations │ ├── 08_Data_Visualization/ │ ├── Charts_and_Graphs │ ├── Tables_and_Matrix │ ├── Cards_and_KPIs │ ├── Maps │ ├── Slicers │ ├── Drillthrough │ ├── Tooltips │ ├── Conditional_Formatting │ ├── Bookmarks │ ├── Buttons_and_Navigation │ └── Dashboard_Design_Principles │ ├── 09_Report_Design_and_UX/ │ ├── Storytelling_with_Data │ ├── Color_Theory │ ├── Layout_Design │ ├── Executive_Dashboard_Design │ ├── Mobile_Layout │ ├── User_Experience │ ├── Accessibility │ └── Performance_Friendly_Design │ ├── 10_Advanced_Modeling_and_Performance/ │ ├── Query_Performance │ ├── DAX_Optimization │ ├── Incremental_Refresh │ ├── Aggregation_Tables │ ├── Composite_Models │ ├── DirectQuery │ ├── Import_Mode │ ├── Hybrid_Model │ └── Performance_Analyzer │ ├── 11_PowerBI_Service_and_Deployment/ │ ├── Publishing_Reports │ ├── Workspaces │ ├── Apps │ ├── Dashboards │ ├── Scheduled_Refresh │ ├── Data_Gateway │ ├── Sharing_and_Permissions │ ├── Row_Level_Security │ ├── Deployment_Pipelines │ └── Governance │ ├── 12_Real_Time_and_AI_Features/ │ ├── Streaming_Datasets │ ├── Real_Time_Dashboards │ ├── AI_Visuals │ ├── Forecasting │ ├── Key_Influencers │ ├── Decomposition_Tree │ ├── Python_in_PowerBI │ ├── R_in_PowerBI │ └── Copilot_Features │ ├── 13_Fabric_and_Modern_Data_Platform/ │ ├── Microsoft_Fabric_Overview │ ├── Lakehouse │ ├── Warehouse │ ├── Dataflows_Gen2 │ ├── Notebooks │ ├── Pipelines │ ├── OneLake │ ├── Real_Time_Intelligence │ └── Fabric_with_PowerBI

If you don’t like this word “WHY” please leave Data Related Jobs.

What I told you about using AI as a data professional is so true. AI is not taking your job. AI is making some people finally get accolades they may never have gotten before 😂 Why? Because AI is only as good as the context, structure, and business understanding you feed into it. Two people can use the same AI tool and get completely different outcomes. One person dumps random data and gets generic answers. The other understands: - data modeling - business logic - SQL - metrics - process flow - data quality - stakeholder expectations …then presents the data properly. That person gets sharp insights, executive-level storytelling, and precise answers from AI. So learning the fundamentals is not a waste of time. It is the foundation for using AI efficiently. The professionals who understand the basics will use AI like a power tool. The ones skipping the basics will keep wondering why the AI output feels “off.”

While I was away, I was using data to solve a very critical problem around port terminal congestion. AI is instrumental, yes. But the real breakthrough came from digging into the peripheral data around the terminal gate operations. That led to the development of a Gate Intelligence System that gives the traffic team early warning signals of congestion based on the classification of trucks approaching the gate. The system helps them decide when to activate: - Dedicated lanes - Mixed flow operations - Fast-track processing for truck categories that naturally process faster Think of it like the supermarket fast lane: A customer with one item should not spend 30 minutes behind someone doing bulk shopping. That’s data thinking. Not everything is AI. Sometimes the real value is understanding operations deeply enough to know what data matters, how to model it, and when to act on it. Data is power.

@RealOEPrecious You're welcome. 🥰

If you’re serious about data, data modeling is non-negotiable. Why? Businesses naturally keep acquiring new systems to run their operations. Some systems integrate directly. Others never truly connect technically, but are still connected through business logic. The only way to wire these worlds together properly is through strong data modeling and a deep understanding of data grains. Without that foundation, reporting becomes inconsistent, metrics start conflicting, and trust in the data slowly breaks down. A good data model is what turns scattered systems into a single business story.

@RealOEPrecious Congratulations, Precious.