Tools: Translating Messy Data, DAX, and Dashboards into Actions Using Power BI

Establishing where data should be used ## Modelling ## Transforming Data ## Defining data ## Organizing data ## Analysis ## Visualization and dashboarding Messy data often refers to data that is disorganized, inconsistent, or inaccurate, and tends to make analysis quite difficult. However, if you think about it, the information from that data often has the power to drive insights, provide solutions, or guide business processes in an organization. Intriguing right? Let me walk you through a step-by-step guide on transforming data into actionable insights using Power BI, and by the end of it, Power BI will be one of your best business intelligence tools. To start off we shall assume we have hypothetical data for a shoe business. In addition to all the data from the different departments has been given This is referred to as establishing a domain where data is needed by obtaining the data specific to the domain you want to extract insights from. Data may come in different forms, such as structured data, semi-structured, and unstructured data Structured data is data organized in a rather specific format, that is, tables with rows and columns. Semi-structured data is data that has self-defining structure and does not confirm to formal structured data eg. HTML,jSON Unstructured data is data without structure ie; videos, audios, and images. For our shoe business, we shall ensure that we collect all our structured data from the sales department to perform an analysis for the sales of the different shoes in the department. After identifying data that is specific to our domain, the next step is to organize the different data obtained in a manner that supports dashboarding and getting insights. Modelling involves the following: The process involves cleaning data. This can be done through formatting to remove duplicates, removing blanks and errors from your data, and finally trimming using the trimming functions to remove unnecessary space. For our make-shift data, missing text data shall be filled in with pseudo blanks and using the trim(specific text), all unnecessary spaces in the text data shall be removed. In Power bi we have different data types that are Text, decimal, whole number, date and time, binary, time, and boolean. It is important to ensure that the different data in the table is defined by its data type within each table. This is done through changing the data type option in Power Query. The process involves right-clicking on a selected column, then selecting the change type option, and finally selecting the correct data type for the data in the column. For the sales shoe data set, we shall ensure that the name of the shoe and the shoe id are in the text data type. Order date is in the date data type, and the shoe price or any discount given is given in the numeric and decimal values, respectively. This refers to the use of schemas to create a data model. For one to establish a good schema, relationships within the data have to be established between the fact tables and the dimensional tables, and connections made to ensure proper efficiency and performance. For our shoe data, we shall establish a star schema with the fact table containing the Sales ID, Order ID, Location ID, shoe price, discount amoun,t and shipping costs. Analysis is more or less an interpretation of data to answer questions that are relevant to a given business or organization. To carry out analysis, we use DAX expressions. DAX is the formal language used in Power Bi that allows us to carry out calculations. Some functions in DAX include Naturally, human beings tend to be inclined to visuals as they are easier to understand, thus making data visualization an important part of business intelligence. Data visualization entails the representation of information in charts and graphs or maps in order to highlight trends and patterns. The visualizations are all assembled at a centralized point to create a dashboard that provides an overall view of the business. They tell a story of the business and make it easy for even the not technical people to follow along. For example, ** For our hypothetical data, we can come up with charts such as the line graph to show trends of shoe sales over the year, a column chart to highlight the top 1o shoes with the highest revenue, or a map showing the amount of revenue generated for sales in different locations across the country** After creating the dashboard, questions like what happened and why it happened are answered. From this, we move to what to do next, which are the insights and actions necessary to make any changes or improvements required. For example, from our shoe data, the question would be like what measures should be taken to improve revenue base don the data. In conclusion, we were able to move our messy data to give us insights that prompt improvements within the business by cleaning, modelling, analysing, and visualizing data. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Transforming and cleaning of data - Defining data - Organizing data Power Query is the tool used to transform, clean, and define data. - Aggregate functions, e.g., the sum(shoe price), average(discount amount), are used to get the KPI measures for different data. - Logical functions like the if() are used to filter out data to get specific metrics necessary to answer stakeholder information.