Businesses interact with data periodically as part of their day-to-day operations. Raw data is often messy and needs polishing to extract value and ultimately create insights on the actions needed to be performed for improvement.
One powerful tool is Power Query in PowerBI helps a great deal in transforming data for decision making.
As a data analyst, working with raw data can be broadly classified into three stages:
- Data Cleaning
- Data Analysis
- Data Visualization
Data Cleaning
Here you have to state the problem statement that is specific, measurable and achievable. The problem statement will be answered in the different KPI’s to be shown in the dashboard. Thereafter, perform a preliminary analysis on the data like:
-Asigning consistent correct data types.
-Removing duplicates.
-Fixing misspellings/typos.
-Checking for biases.
Data Analysis
This is the core stage of the process and must be executed carefully.
After understanding the problem statement, adjust and format the data using DAX (Data Analysis and Expression). DAX is a very critical part in creating measures or columns representing calculation like aggregates, filters, time and logic.
The choice of data modelling is also vital for performance and accurate reporting. It is advisable to adopt star schema as it is organized and allows easy creation of large data volumes.
Data Modeling
Best Practices for Creating Models
- Always connect dimension tables to fact tables, not the other way around.
- Use one-to-one relationships only where necessary.
- Keep filter direction single unless there is a strong reason otherwise.
- Avoid unnecessary inactive relationships.
- Always validate relationships by testing visuals and slicers.
Once the data achieves high integrity, joins can be introduced to combine or filter data from multiple tables based on relationships (matching columns).
Essentially, joins enrich data and checks quality. Based on the required outcome one can either choose inner join, left join, right join, left-anti join, right-anti join etc.
Data Visualization
After completing the first two stages, a dashboard is created to visualize key KPIs. A dashboard is a well-organized visual representation of data designed for presentation and sharing.
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, tables and maps.
Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Here is where non-technical people understand the KPI’s e.g. trend over time, relationship between different factors. This is possible by filtering through slicers on a given metric.
Once visual data is analyzed, informed decisions can be made to improve business performance and focus on specific areas—backed by data rather than guesswork.
Simply no flying blind in decision-making.
Without taking action, results are bound to fail. This is often the tipping point that differentiates good organizations from great ones.
Principles of a Good Dashboard
A well-designed dashboard should emphasize:
- Simplicity
- Clarity
- Contextualization
It should be clean, well-organized, and not overdone.
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