How can you easily analyze and visualize data?

Analyzing and visualizing data can be made easier with the help of various tools and techniques. The key is to have a structured approach, practice, and continuously refine your skills to become more proficient at analyzing and visualizing data. Gather the relevant data from reliable sources. Ensure that your data is clean and free from errors, missing values, and duplicates. If necessary, preprocess your data by scaling, normalizing, or encoding categorical variables. This step depends on the specific requirements of your analysis. Conduct statistical tests to test hypotheses or make inferences. Common tests include t-tests, ANOVA, correlation analysis, and regression analysis.

Data analysis and Visualisation are the essential skills for any data professional. I totally agree with this point "Choose the right tool". Clients or customers never think about the tools which are used by companies or service providers. They only think about ease to use services and which service has the best user experience features. When I was working for the clients (One of biggest financial firms in Europe) to create reports and dashboards then we had options to select resources like python developer, backend and frontend developer, and Tableau developer. So keeping cost and time utilisation and optimisation in our mind, we decided to proceed with the Tableau developer. We delivered more than 30 dashboards.

Project Managers or Delivery managers should understand the scope of the project before deciding the tool. Clients do not bother what tools you gonna use and what cleaning you are performing at the back end. They are more focused the business solutions whatever tool might be. I would say Power BI has more versatile features in accommodating most of the requirements for Data Visualization.

Power BI is a good tool for data visualisation, we can use it for databases from SQL Server, Oracle and even for Excel tables Power BI provides many visualisation templates like : Bar and column charts, Doughnut charts, KPI charts, Gauge charts and also maps

Power BI : Power BI offers a mobile app that allows you to access your reports and dashboards on smartphones and tablets, ensuring that you have critical business insights at your fingertips. Post August 2023 updates you can see the layout left bottom of the canvas.

Discussion with the client is highly important to understand the requirement. This will also ease your process and cease the duplication of efforts like what charts you would be interested in. Any KPIs to be highlighted, Any trend we would like to explore and so on.

I second that cleaning is an important part of data visialization prosess if you to receive correct and relevant data for the dashboards and reporting. Never use data aggregated from the primary sources that has not been cleaned and organized beforehand.

Using PowerQuery in PowerBI you can achieve a lot of these topics. ? Import data from numerous data sources ? Combine various data sources ? Apply some common data transformations ? Do data profiling of the data ? When applying transformations with PowerQuery each step is saved- if you need to modify or remove this step in future you will be able to do this You can also handle the below if you have cleared the requirements with your stakeholders: ? Handle NULLS or BLANKS ? Remove duplicates ? Append or merge data ? Remove historical data from analysis

As part of the data preparation, it is important to first figure out what columns do you actually want to keep from the raw data. The first step as part of data preparation should be to remove all those columns which have no use or role in answering the questions that you formulated in the previous step.

Prior to doing visualizations and descriptive statistics, there is one major thing to do when exploring your data. Understanding the structure of the data, this step helps to understand what variable needs which type of analysis or type of chart? This is also essential to understand the anatomy of data and how variables and observations are related among them.

n summary, exploring your data is a crucial step in the data analysis process. Whether you're using traditional data analysis methods or tools like Power BI, thorough exploration helps you gain insights, identify patterns, and make informed decisions based on your data. It's a foundational practice in data-driven decision-making.

All Data Analysts or Developers (irrespective of the BI tool used) should have some fundamental knowledge of Statistics and Mathematics. By having this knowledge you can "interrogate" your data accordingly. Once you have the required insights from your data, you will be able to create visually compelling reports for your end users to interact with.

Data profiling is a very important steps. You have multiple options to do this step as part of the exploration process. One way to expedite this process is to anonymize your data (just remove the columns that have sensitive or private information or replace these with dummy values) and then use the power of AI to help you get more insights and detailed understanding by using tools like chatGPT and some others.

This is the important step for stakeholders where they will take business decisions based on your Visuals. Once you submit your dashboards to stakeholders it's also your job to teach and train them the interface and features exploration. This will avoid complaints from them as well as very much clear on enhancement. As a developer one should add this step as critical. Efforts will be voided without the end user is not having clarity of visuals to move forward.

When implementing data visualization tools in a company, it's important not only to show, but teach and support client employees afterwards. We use Tableau and Power BI, and haven't heard any complaints from our clients.

Data storytelling is at the heart of this step. Make sure you are "telling a story" instead of "just dumping visuals". The end goal of any visualization to enable decision makers to make decisions and a well narrated story makes this process very easy and useful.

When using a BI tool for Data visualization, you have too follow the basic guidelines: ? Use the right visualization for the right data i.e.: use a line chart for time series analysis ? Reduce the usage of Pie charts- if you have to use it limit it to 5 or 6 categories ? Usage of color must be considered due to Accessibility issues- avoid the red/ green combination where possible ? Your visualizations should answer the questions (KPI) your stakeholders wanted initialy ? The most important visual is placed in the top left corner. ? Reduce clutter on your report ? Correct use of White space

I think we also need to consider who we're analyzing this data for. What do the stakeholders care about the most? This will help us figure out how to set up the data. Also, whether our stakeholders know a lot about data analysis will affect how we present the data visually to tell the story clearly.

As part of the defining the question phase, it is important to have a requirement gathering framework that maps all your questions in an easily to understand format. This can include a simple Excel based sheet where you can collect all the necessary information relevant to each question. This step is important as this framework helps you in all the subsequent steps related to ETL, data modeling and even creating data visualizations for your reports and dashboards.