10 Ways to Visualize your data

You’ve been avidly collecting data. You’ve figured out how to process it all and set up your formulas… but how do you transform those into powerful KPI dashboards and genuinely valuable data visualizations that bring your insights to life?

There’s an array of data visualization types, and which you choose for your data depends on what measurement you are trying to emphasize and what information you are trying to reveal. If you want to know when you should use a column chart versus a line chart – and yes, there’s a big difference – then this is the guide for you.

Indicators

What is Indicator?

An indicator data visualization is a vivid way to present changes that you’re tracking in your data. Typically, this uses something like a gauge or a ticker to show which direction the numbers are heading in.

What does it visualize?

This allows you to display one or two numeric values. You can also add additional titles and a color-coded indicator icon, such as a green “up” arrow or a red “down” arrow to represent the value, and changes in this value, in the clearest way possible.

What does it measure?

Indicators are clear, simple ways to demonstrate how your organization is doing on a particular metric, and whether you’re heading in the right direction.

What Sources of Data Does It Use?

You can feed in just about any form of numerical data source, so long as you can continually refresh
these numbers, so that the movement of the ticker / gauge / color coding is accurate.

Example:

Above you can see a “gauge” indicator showing how revenue figures are progressing towards the target, and a “numeric” value indicator showing the annual increase to average admission cost

Line Chart

What is Line Chart?

Line charts plot data points on a graph and then join them up with a single line that zigzags from each point to the next.

What does it visualize?

These are super simple and very popular, because they give you an immediate idea of how a trend emerged over time. You can see when peaks and troughs hit, whether the overall values are going up or down, and when there’s a sharp spike or drop in numbers.

What does it measure?

There are many different business cases that work well with line charts. Pretty much anything that compares data, or shows changes, over time is well suited to this type of visualization.

Again, it’s all about visualizing a trend. You can also compare changes over the same period of time for more than one group or category very easily, by adding a “break by” category.

What Sources of Data Does It Use?

Again, anything that gives solid, discrete numbers, organized by time. So, you could use sales figures
from your CRM, pull in tables of data showing total numbers of new sign-ups, record showing income
per month. Info from SQL databases is particularly easy to translate into line charts.

Example

This line chart shows sales revenue over the past year. For more granular detail, could then add a “break by” category to analyze expenditures of different business units, also over the past year.

Column Charts

What is Column Chart?

A column chart graphically represents data by displaying vertical bars next to each other, lined up on the horizontal axis.

Each bar represents a different category, and the height of the bar correlates with numbers on the values axis, on the left hand side.

What does it visualize?

Column charts give you an immediate way to compare values for related data sets side by side, highlighting trends in a swift, visual way.

They can include multiple values on both the X and Y axis, as well as a breakdown by categories displayed on the Y axis.

What does it measure?

Like a line chart, column charts are often used to show trends over time, for example sales figures from month to month or year to year.

However, they’re also useful for comparing different things side by side, e.g. how well two different products are selling in the same month.

What Sources of Data Does It Use?

Column charts are straightforward visualizations and can draw on data from just about any data source,
so long as it’s consistent and presented numerically.

Example

This column chart shows total page views and sessions spent on a website by online visitors on consecutive months

If you want to emphasize overlapping trends over time, you can also combine column charts with line charts, as in this chart that compares total revenue with units sold, month by month.

Bar Chart

What is Bar Chart?

A bar chart is essentially a column chart on its side: values are presented on the horizontal axis and the categories are on vertical axis, on the left.

What does it visualize?

Bar charts are more commonly used to compare different values, items and categories of data. From a purely practical perspective, they’re also used over column charts when the names of the categories are too long to comfortably read on their side! They are not usually used to show trends over time.

What does it measure?

Like column charts, bar charts are frequently used to compare the total number of items within a category, for example total sales or the number of respondents that selected a particular answer.

However, they’re also handy for visualizing sub-categories using color coding.

What Sources of Data Does It Use?

Data used to compile bar charts could come from Google Analytics, your CRM, sales figures or any other
kind of database that stores data numerically.

Example

The bar chart above represents the spread of customers per age group, but it also gives a quick, visual representation of which products each type of customer is most likely to buy, too.

Pie Charts

What is Pie Chart?

Pie charts show values as a “slice” of a whole circle (the whole pie). Numerical Values are translated into a percentage of 360 degrees, represented by the arc length, and each slice is color coded accordingly

What does it visualize?

Pie charts show what percentage of the whole is made up of each category. That means they deal with total numbers, and trends in overall responses, rather than changes over time.

That means it’s a good idea to use a pie chart when displaying proportional data and/or percentages. Remember that the point is to represents the size relationship between the parts and the entire entity, so these parts need to add up to a meaningful whole

What does it measure?

It makes sense to use a pie chart when you want to get a rapid, overall idea of the spread of data – for example, market share or responses to a survey – rather than when you’re concerned about the precise figures they represent.

What Sources of Data Does It Use?

Survey and questionnaire responses, data from social media sources or Google analytics, total sales
figures and so on will all work. Keep it fairly simple though – if you have more than 6 categories, your
pie chart won’t give you much information at a glance, especially if there’s no clear “winning” answer.

Example

In the example above, you can tell in a millisecond which marketing channels bring in the most leads, thanks to the pie chart structure.

Area Chart

What is Area Chart?

An area chart is similar to a line chart in that it plots figures graphically using lines to join each point – but it’s more dynamic and visual, giving an idea of comparative mass.

The area under the jagged points formed by the line is filled in with color, so that it looks kind of like a mountain range.

What does it visualize?

Area charts are used to demonstrate a time-series relationship. Unlike line charts, though, because they also represent volume in a highly visual way.

The information is shown along two axes and each “area” is depicted using different color or shade to make it easier to interpret.

What does it measure?

Area charts are great for showing absolute or relative (“stacked”) values – as in, showing trends as you do in a line chart, but comparing a few different trends at once.

They’re particularly effective if there’s a broad disparity between some of these trends, as it makes the comparison starker, too.

What Sources of Data Does It Use?

Any data that works for line charts should work for area charts, too: SQL data tables, sales figures from
your CRM, financial data and so on – but you must be able to organize the information by day / month /
year, etc. to demonstrate change over time.

Example

Using an area chart, you can easily compare sales figures for different products by quarter, and track trends in total sales volume over time.

Pivot Table

What is Pivot Table?

A pivot table brings together, simplifies and summarizes information stored in other tables and spreadsheets, stripping this down to the most pertinent insights.

They are also used to create unweighted cross tabulations fast.

What does it visualize?

Pivot tables are one of the most simple and useful ways to visualize data. That’s because they allow you to quickly summarize and analyze large amounts of data, and to use additional features such as color formatting and data bars to enhance the visual aspects

What does it measure?

Pivot tables are more about simplifying tables than changing it into a graphical representation. That means they are helpful for displaying data with several subcategories in easily digestible ways.

What Sources of Data Does It Use?

Existing databases, tables and spreadsheets, including Excel. A good example is a company’s
asset management.

Scatter Plot

What is Scatter Plot?

Scatter charts are a more unusual way to visualize data than the examples above. These are mathematical diagrams or plots that rely on Cartesian co-ordinates.

If you’re using one color in the graph, this means you can display two values for two variables relating to a data set, but you can also use two colors to incorporate an additional variable.

What does it visualize?

In this type of graph, the circles on the chart represent the categories being compared (demonstrated by circle color), and the numeric volume of the data (indicated by the circle size).

What does it measure?

Scatter charts are great in scenarios where you want to display both distribution and the relationship between two variables.

What Sources of Data Does It Use?

CRM, sales and lead data that comes with granular information on buyers – age, gender, location and
so on – are particularly useful for this kind of graph.

Scatter Map / Area Map

What is Scatter map?

A scatter map allows viewers to visualize geographical data across a region by displaying this as data points on a map.

What does it visualize?

Scatter maps / area maps work a little like scatter graphs, in that the size and color of the circle illustrates quantities and types of data.

However, it goes a step further by also showing where this activity is concentrated, geographically speaking.

What does it measure?

You can incorporate up to two sets of numeric data, using circle color and size to represent the value of your data on the map.

What Sources of Data Does It Use?

The more precise information you can enter about geographic location, the better. For example, entering
the country and city, or latitude and longitude information, alongside the data you want to map will help
you create a very accurate scatter or area map.

Example

Above is an example scatter map that gives a breakdown of the number of website visitors a company has by location. The larger the circle, the higher the number of visitors from that city on the map.

Tree-map

What is Treemap?

A treemap is a multi-dimensional widget that displays hierarchical data in the format of clustered rectangles, which are all nested inside each other.

What does it visualize?

Data that comes under the same broad heading is grouped by color, and within each section, the size of the rectangles relate to the data volume or share.

What does it measure?

These types of chart can be used in all kinds of different scenarios where you want to incorporate more granular insights than other visualizations will allow.

For example, you might want to use it instead of a column chart, to give a sense of trends in the popularity of a certain product, but also include and compare many categories and sub-categories.

What Sources of Data Does It Use?

You can bring in data from CRMs, Google Analytics and AdWords, social media, spreadsheets, etc. Bear
in mind, though, that like a pie chart, you’re looking at the percentage make-up of each category more than changes over time.

Example

In the example above, you gain an overview of how different marketing campaigns breakdown by region.

So this were the 10 important visualizations you should be knowing. From the next articles we will study each of them in detail.

Happy Learning ! 🙌🚀🚀

Ever wondered about the 7 Pillars of Power BI!

In the previous blog we did the introduction to Power BI and discussed architecture design behind Power BI. In this article we are going to discuss about some of the most important things which are required to start your Power BI journey effectively.

There are 7 steps that you have to remember while working with Power BI. I will call this 7 steps to follow as “The 7 Pillars of Power BI”.

So here is the diagram representing the 7 Pillars of Power BI.

The 7 pillars are as follows:

  1. Extract
  2. Transform
  3. Modeling
  4. Calculations
  5. Visual
  6. Distribution
  7. Automation

Let’s discuss each of them now

Extract: This is the step where we get the information from the data set performed in Power BI Desktop. You can talk to any data source with ha highly simplified Power BI interface. Power BI can connect to any data source to bring meaningful insights to the end-user. It is simple to import any custom file into Power BI. Connecting data from multiple data sources can be achieved by anyone new to Power BI.

Transform: It is the second pillar where we can clean and treat the data. This is performed in Power Query Editor which is the part of Power BI Desktop Environment. After Data Loading, it should undergo pre-processing according to the requirements. This process is called Data shaping or Data Transformation. It involved various steps like renaming tables and columns, changing the data type, modifying rows and columns, appending, merging, etc.

Modeling: Third Pillar where we can create relationships between the data tables and is done using Power BI Desktop. Here we enhance the data to get more accurate insights and analytics. This is achieved by creating relationships and hierarchies between various data tables for better analysis.

Calculations: This is the Fourth Pillar where we can create various measures using DAX language analysis. DAX is also known as “Data Analysis Expressions”. It is achieved by creating several measures and calculated columns. Even M Language is used for various purposes, especially for Data and Time operations.

Visuals: This is the Fifth Pillar where we can create the storytelling and present the information and insights through various visualizations using Power BI. Visualizations is the heart of Power BI. We can play with a variety of visualizations, right from in-built visualizations to custom visualizations. Power BI is the Pechora of visual tools and custom visuals. Business users acquire good analytical insights without writing a single line of code.

Distributions: This is the Sixth Pillar where we share the reports created to end-users, stakeholders, and customers through Power BI cloud platforms. To achieve this distribution Power BI service where you can make changes in the report and share reports with anyone.

Automation: This is the Seventh Pillar where we update the dataset automatically, this is being performed on Power BI cloud platforms. Automating the data and further refreshing the data takes place in this automation process of Power BI, this is achieved using Power BI Service.

So here are the most important 7 pillars or 7 steps you have to keep in mind while performing any Power BI project.

From the next blog, we will start the Power BI Installation and Setup followed by the main concepts of Power BI.

Happy Learning!! 🙌🙌🚀

All About Power BI Architecture Design

In the previous article we had a detailed introduction to what actually Power BI is and how it is used. In this section we are going to discuss a new topic and an important one to kick-start the Power BI journey. Here, we will discuss Power BI Architecture, its components and the Power BI Service architecture. So let’s start.

Power BI Architecture

Power BI architecture consist of 4 major sections that starts right from Data Sourcing to the creation of reports and dashboards. If we observe various technologies and processes are working together to get the desired outcome with correct accuracy. This is the reason Power BI is among the market leader when it is about Reporting and Dash boarding tools.

Power BI Architecture

Sourcing of Data: Power BI can extract data from various data connectors. It can be servers, Excel Sheets, CSV files, other databases and many more. You can even extract live data or a streaming data in Power BI. The extracted data is directly imported in Power BI within few seconds and is compressed up to 1 GB. After sourcing of data you can perform Data Transformation operations.

Transforming the data: As we know the Golden Rule of Data Analytics that before analyzing or visualizing the data we have to clean the data to get the accurate insights. So in this step Data Cleaning and Pre-processing takes place. After transforming data, the data is loaded into data warehouse and further analysis takes place.

Creating Reports or Visualizations: After data transformation process, different data reports and data visualizations are made based on the business requirements. A particular report has various visualizations of the data with different filters, graphs, charts, diagrams, etc.

Creating Dashboards: Planning and arranging all elements of Power BI report makes a Power BI Dashboard. Dashboards are created after publishing the reports in Power BI service.

Components of Power BI Architecture

Various components included in Power BI Architecture are as follows:

1) Power Query: This component provided by Power BI is used to access, search and transform data from various data sources.
2) Power Pivot: It provides tools to model data from internal memory data source for analytics.
3) Power View: These components have various tools to represent data through various visuals which are used for visual analysis.
4) Power Map: It has abilities to represent spatial data in form of maps. The important advantage of Power BI is that we can use maps in different customized ways.
5) Power BI Desktop: Power BI Desktop is the heart of entire Power BI platform. Its development tool for Power View, Power Query, and Power Pivot. You can import various data sources and perform visualization tasks.
6) Power Q&A: Using the Power Q&A option, you can search for your data and find insights by entering queries in natural language format. It can understand your questions asked and answers it with relevant insights in form of various visualizations.
7) Power BI Service: The Power BI Service helps in sharing the workbooks and data views with other users. Even data re-freshing can take place after regular intervals.
8) Power BI Mobile Apps: Business stakeholders can view and interact with the reports and dashboards published on a cloud service through mobile using Power BI Mobile Apps.

Working of Power BI Architecture

The Power BI architecture is mainly divided into two parts:

  1. On-cloud
  2. On-premises

The below diagram is also called as Power BI Data Flow diagram that may help you to clearly understand the flow of data from On-premises to On-cloud server applications.

Power BI Gateway Diagram

On-premises

All the reports published in Power BI Report Server are distributed to the end users only. Power Publisher enables to publish Power BI reports to Power BI Report Server. Report Server and Publisher tools by Power BI helps to create datasets, paginated reports, etc.

On-cloud

In this Data flow diagram, Power BI gateway acts as a bridge in transferring data from on-premises data sources to on-cloud servers. The clouds consist of various stuffs such as datasets, reports, dashboards, embedded, etc.

Power BI Service Architecture

It is mainly based on two clusters they are mainly:

  1. The Front-end Cluster
  2. The Back-end Cluster

The Front-end Cluster

The front-end cluster behaves as a medium between the clients and the on-cloud servers. After the initial connection and authentication the client can interact with various datasets available.

The Back-end Cluster

The back-end cluster manages datasets, visualizations, data connections, reports, and other services in Power BI. These Components are mainly responsible for authorizing, routing, authentication and load balancing.

Here, we have completed the architecture part behind the Power BI and in the next article we will study about some of the “7 Important Rules” that we need to remember to become pro in Power BI.

I hope you liked and understood the write-up. Meet you all soon in the next blog. Stay tuned and Happy Learning !! 🚀🚀👋

Getting an overview of Power BI

Introduction

Hello guys, here we are with the super series of Power BI articles and the following blog is the first part of the series. The complete series will be divided into various sub-parts where we will discuss the important features and other tutorials related to Power BI.

The blogs are going to be in quite detailed manner which will be enough for you guys to learn Power BI and become a pro in it.

All right the time has come for us to officially meet Power BI by a quick summary. Here Power BI is a standalone Microsoft business intelligence product which includes both desktop and web based applications for loading modeling and visualizing data.

There’s a ton of additional info if you’d like to learn more at powerbi.microsoft.com

Now I want to show you something called the Gartner Magic Quadrant and Gartner’s and market intelligence
research company. They produce these quadrants a few times a year. And what we’re looking at here is the Magic Quadrant for analytics and business intelligence platforms specifically updated February 2021 and the idea is that you have completeness of vision on the x axis and the ability to execute on the Y axis.

And when you break down the players into the four quadrants you end up with niche players challengers, leaders and visionaries and where you want to be is right here in this top right corner where the leaders live.

And that’s exactly where we find Microsoft with power BI leading the charge among some very popular and very powerful other platforms like Tableau, IBM, Qlik, etc.

So really exciting time to be learning power BI because I think it’s only going to get more powerful and more popular from here onwards.

There are hell lots of features in Power BI which make them different from other reporting platforms. Power BI is user-friendly tool which offers awesome drag and drop features and self-service capabilities which make it easy to use and learn.

There are three main components of Power BI platform:
1) Power BI Desktop (A desktop application)
2) Power BI Service (SaaS i.e. Software as a Service)
3) Power BI Mobile (For iOS and Android devices)

One of the plus features of Power Bi is that we can deploy Power BI on both on-premise and on-cloud platforms.

Why Power BI?

As we read what exactly Power BI is, now let’s understand why should we use Power BI?

Power BI is a huge platform where several kinds of services comes under it.
1) One of the important service under Power BI is Power BI Services which is a cloud based service which is used to view and share dashboard with end users or various stakeholders.
2) Power BI Desktop is the heart of Power BI platform which is a reporting interface where all the query editing and reporting part takes place.
3) Also one another useful service is Power BI Embedded that uses Azure cloud platform, and we can use for Data analysis and various ETL process.

Features of Power BI

So what are some of the key benefits that make this such a game changing product.

1)  Connect, transform and analyze millions of rows of data 

You can connect transform and analyze millions even hundreds of millions of rows of data and you can access that data from virtually anywhere whether it’s a database flat files on your desktop cloud services folders of files etc. There’s a huge huge connector library that allows you to access a ton of information and then not only that but you can create fully automated and repeatable ETL procedures to shape and transform and load the data from those different sources.

2) Build relational models to blend data from multiple sources.

We can actually build relational models inside of power BI. to blend the data from each of those multiple sources. And this is a concept that’s getting more and more important in the analytics world by creating relationships between all of those sources were able to analyze holistic performance across our entire data model. And that’s a critical skill set for anyone working in data or analytics or business intelligence it’s that ability to blend information tie sources together and paint that comprehensive view of performance.

3) Define complex calculations using Data Analysis Expressions (DAX)

We can define complex calculations using data analysis expressions or that DAX formula language. So we’ll be doing this to enhance our data sets and enables some really interesting advanced analytics techniques using those powerful and portable expressions.

4) Visualize data with interactive reports & dashboards

Most important one we can visualize or data with interactive reports and dashboards and what we’ll be doing throughout the course is actually building our own custom business intelligence tools using power be best in class visualization and dashboard features.

5) Power BI is the industry leader among BI platforms

And then last but not least, fact is power BI is the industry leader among other Business Intelligence platforms. It’s intuitive it’s powerful and most importantly it’s absolutely free to get started with power BI desktop.

Power BI vs MS Excel

Now last but not least just want to make a quick comparison between power BI and Excel because there is quite a bit of overlap here especially between Power BI and Excel.

So let’s think of this like Venn diagram where you’ve got power Excel tools on the left you’ve got power BI tools on the right. And this area of overlap in the middle with features that both platforms share. So here’s kind of what it looks like.

In summary you’ve got these Excel specific tools on the left like pivot tables, pivot charts, power map, power view and cube functions and then shifting over to the right side you’ve got the report, dashboard, views and power behind that don’t exist in Excel.

Got those custom visualization tools that we’ve been talking about as well as the publishing and collaboration options available through power vs service.

Coming to the intersection part. These two tools are actually built on the exact same engine. Power BI takes the same data shaping modeling and analytics capabilities and then adds these incredible new reporting and visualization and publishing tools on top of them.

So even though they’re called different things in different places you know that data loading tools
will be called either power query or get and transform and excel the data modeling tools will be called Power pivot.

The fact is it’s all the same thing. And the best news of all is that transitioning is incredibly easy. These two platforms play really really nicely together.

Power BI Components

Till now we all know why Power BI is so powerful and why it is used by so many organizations. So now let’s see what are various Power BI components which are used widely are.

1) Power Query: This component provided by Power BI is used to access, search and transform data from various data sources.
2) Power Pivot: It provides tools to model data from internal memory data source for analytics.
3) Power View: These components have various tools to represent data through various visuals which are used for visual analysis.
4) Power Map: It has abilities to represent spatial data in form of maps. The important advantage of Power BI is that we can use maps in different customized ways.
5) Power BI Desktop: Power BI Desktop is the heart of entire Power BI platform. Its development tool for Power View, Power Query, and Power Pivot. You can import various data sources and perform visualization tasks.
6) Power Q&A: Using the Power Q&A option, you can search for your data and find insights by entering queries in natural language format. It can understand your questions asked and answers it with relevant insights in form of various visualizations.
7) Power BI Service: The Power BI Service helps in sharing the workbooks and data views with other users. Even data refreshing can take place after regular intervals.
8) Power BI Mobile Apps: Business stakeholders can view and interact with the reports and dashboards published on a cloud service through mobile using Power BI Mobile Apps.

So lets start exploring Power BI with its architecture in more detail in the coming articles.🔥🔥🔥

Stay tunes!! Happy Learning!! 🙌🙌