Introduction.
In the real world data is not always presented to an analyst being clean or clear for decision making. Power Bi helps analysts turn this raw, unclear data into easily understandable information by using dashboards making it easy for business executive to make quick decisions. In this article we are going to learn how this happens.
Messy Data
As we have seen analysts do not always get data that is clean data, so present it as it is will give inaccurate results that will negatively impact decision making, among the issues they encounter are;
Missing Values
Duplicate Values
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inconsistent Formats
Data Preparation
For data to be cleaned and make sense analysts use Power query in Power Bi to transorm data.Once you have get your data from the data source,you do not load the data instead you click transorm data here you will remove duplicates, fix inconsistent formatting for example currencies, converting data types ensuring fields calculate correctly, splitting or merging columns to organize information consistently, removing null values that disrupt aggregation and reporting.
Each of this actions is recorded in the applied steps pane, making the process reproducable and tranparent.The Data model.
Once cleaning is done, to make it easier working with calculations analyst design a data model, the most recommended data model to use is the star schema, in this model data is organised into two main parts; fact tables and dimension tables, facttables store quantitative,measurable business events such as sales or transactions while dimensions tables stores information that gives context to the data in fact tables.
Using Dax for Data Transformation.
Dax(Data analysis Expressions),is the formula language that, creates calculations,measures and relationships after the data has been loaded, Dax defines how data behaves in analysis with calculated measures and tables that helps transform raw data into meaningful insights.
In Power Bi, Aggregation is used to convert raw data into meaningful summaries such as Average, Sum, Mean, Median e.t.c this forms the basis for all reporting and dashboards.
In hindsight Dax shapes data into answers that reflects on the business priorities.Making Insights visible: Dashboards and Reports
After cleaning, modeling and calculating your data as ana analyst you create Dashboards.Dashboards visualizes the data communicating key insights and supporting decision making, the most success dashboards are able to start conversations, prompt actions and influence decision making.
Analysts are able to create dashboards using charts, line graphs, slicers
A well built dashboard highlights key performance indicators, it focuses on decison not data exploration and long term trends,each dashboard created by analyst should have a clear purpose. Below is an example of a dashboard.
Conclusion.
Inconclusion Power Bi turns raw data into a shared understanding of a business,begining from unclean data, through modelling followed by dax where business logic is defined, and it becomes visible in dashboards where business decision are made.
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