Modernization of business transactions and record keeping has seen more collection and use of data. This has in turn grown the need to use that data to understand past trends and use that knowledge to improve or make decisions. This has seen the increased demand of Business Analysts or Data Analysts.
So how exactly does an analyst go about data to get to the decision part?
Data Sources
When data is collected from the field, it may not be organized or clean. The first thing an analyst does is to understand the data presented to them and what it means. Say if a school collects data, or a hospital collects patient data or a farmer collects data etc. The data in these scenarios is specific to a certain field.
Data Tools
There are many tools that can be used to organize, clean and generate reports. Power BI is an example that is mostly used for this purpose. There are more business intelligence tools such as Apache Superset, Tableau etc that can also be used.
Analysis Steps
1. Cleaning Data
The first thing an analyst does it to clean and organize the data depending on the nature of the data sourced. Data cleaning involves removing duplicate values, filling missing information with relevant data, establish and assigning data types.
Power BI gives an analyst the option to pick data from various sources e.g spreadsheet, database, online services etc. The data is then loaded onto the platform. Cleaning of data is performed when it’s transformed into power query for application of the above steps.
2. Organizing Data
Most data is organized in tables or schemas. Power BI has a modelling page that is used to organize the data and create relationships to related fields. The data about students may be in form of registration information table, fee payment table, hostel information table, examination payment information table etc.
For analysis to be done the tables need to be related with common fields (primary and foreign keys) so that one can bring all the data about a student from different tables to analyse it. This organization and crating relationships between table is what is referred to as data modelling.
3. Analysing Data
Next, an analyst performs calculations on respective data which includes totals, averages, sorting data etc. This is done to derive key metrics such as total fees paid by students, total registrations, average age of the students enrolled in certain courses etc.
In Power BI these calculations are carried out by a series of Data Analysis Expressions (DAX language). These functions and expressions gives an analyst the platform to manipulate the data and get key metrics.
4. Data Presentation
After data is analysed, the analyst needs to visualize it for presentation to decision makers. This is done by creating different charts and combining data points that can be drawn insights from.
Power BI has different charts that can represent the related data and data points. There are cards that represent key metrics. Column charts for related data such as Relationship between student age and hostel booking rates, fee payment as related to the examination date etc.
When one plots the various charts, the key performance indicators are combined into one visual dashboard. A dashboard is a combination of the key metrics on one page that a decision maker can have a look and see trends, relationships and performance analysis. After which they can make decisions based on that.
From raw data collected, an analyst walks through data cleaning, transforming, data modelling, data manipulation using DAX, and data visualization for drawing insights.
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