Data Science and Data Analysis have become an integral part of Business Intelligence. And to study the productivity of your business, you need to understand the uniqueness of each data point.

But before that, you need to have a better understanding of Data Science and Data Analytics.

Introduction to Periscope Analytics (Sisense)

  • Periscope Analytics/Sisense simplifies complex data and makes Business Analytics easier. Backed by the power of unique In-Chip and Single Stack Technologies, Periscope Analytics helps deliver unrivalled performance, agility, and value by eliminating the significant cost required for Business Analytics tools.
  • Instead, it provides a single, complete tool to analyze and visualize large datasets without IT resources. Having more than one thousand customers in 50 different countries, including global brands, Periscope Analytics has bagged many awards and recognitions.

Understanding the Need of Periscope Analytics (Sisense)

Blending data in a consumable fashion is a bit risky for organizations. As a Data Engineer, your job revolves around providing complex data from multiple sources or performing Advanced Analysis to achieve specific goals.

To achieve the goals, you need some quick way to store such massive amounts of data and employ relevant Data for Analysis. This is why you need a platform that can make the job easy for you. With the powerful tools offered by Sisense, managing data has become convenient for organizations.

Seeing the complexity of data, organizations these days need a more advanced approach to Data Analysis while ensuring the rapid development of their business. This includes proper planning of the data capabilities, right from building a Data Pipeline to self-service BI tools and more. Sisense helps exactly with this by providing powerful tools for simplifying complex data, building data products, and delivering insights for both the inside and outside of the organizations.

Key Benefits of the Periscope Analytics (Sisense) Platform

Although we have discussed how Periscope Analytics helps businesses, still let’s talk about its benefits in this section:

  • With Sisense, the process of decision-making is enhanced that further helps in increasing profit and improving efficiency.
  • The Predictive Analysis feature here helps businesses to plan better for the future that previously was impossible to achieve.
  • It helps organizations make informed decisions.
  • There is little room for guesswork because of past experiences.
  • The clear datasets support accurate decision-making.

1) AI-Driven Analytics Features of Periscope Analytics (Sisense)

Sisense employs an AI Algorithm that runs in the background and scans the entire dashboard associated with a particular data model. This Algorithm studies the pattern and behavior of all dashboard users. 

With time, the Algorithm picks up more input from user activity and shows more accurate results targeted as per the user’s needs.  

2) Periscope Analytics’ (Sisense) Embedded Analytics Support 

Sisense’s Embedded Analytics is an end-to-end solution that is integrated within your application. It lets your customer easily prepare, analyze and visualize complex data. 

Sisense’s Embedded Analytics lets you accomplish the following goals: 

  • It helps you integrate Sisense with your current interface & branding.
  • Managing, reporting, and visualizing data becomes easier.
  • Sisense’s Embedded Analytics also helps you integrate SSO and Active Directory.
  • It takes care of your Data Security as well.

Sisense’s Embedded Analytics is mainly for product managers and developers.

Visualizations Supported by Periscope Analytics (Sisense) for Analytics

Sisense supports either static or interactive data visualization. In static visualization, users can have a single view of what’s in front of them while interactive visualization enables users to view different forms of the same datasets selecting any particular dataset.

Have a look at the various types of visualisations supported by Sisense:

  • Indicators: This is a simple Gauge Indicator that shows whether you are moving in the right direction or not.
  • Line Charts: These are great for showing the relationships between data points. Mostly, they are used to show any change or trend over a certain period. 
  • Bar Charts: Bar Charts are meant to compare the quantities of different categories or items.
  • Pie and Donut Charts: These are used to check how different parts of a whole compare to each other.
  • Area Charts: These are used to have a deeper insight into resource and financial planning and allocation.
  • Pivot Tables: Here you can compare the entries with the exact figures. These are mostly used to do a comparison of the components of the same category.
  • Scatter Charts/Maps: When you have numerous data points, you can use Scatter Charts to correlate them.
  • Bubble Charts: These are the same as Scatter Charts, the only difference is that these have bubbles in place of markers or dots to correlate the data.
  • Heat Maps: Heat Maps are useful when there is only one data point having a wide value. With Heat Maps, the opportunities can be easily pinpointed.
  • Tree Maps: Here, colour-coded rectangles are put inside or next to each other. These can be used to compare the value between and within categories.
  • Polar Charts: These are also known as Summary Resource Grade Charts. They act just like a Pie Chart.
  • Funnel charts: These are used to show the decreasing value of the sales funnel when customers move through it. Here, conversion rates can be easily viewed at each step. So, you have an idea where you are losing people. 

Building Dashboards in Periscope Analytics (Sisense)

Sisense helps you create a dashboard in two ways- the first is through the Sisense Analytics page and the second is through Sisense Rest API. 

The difference between both methods is that the Analytics page provides you with an interface where you can put different widgets in the Dashboard.

1) Creating Dashboards through Analytics Page

Step 1: On the Analytics page, click on “+” above the Dashboard list or Right-click on the folder menu and select “New Dashboard”.

Periscope Analytics - Creating Dashboards through Analytics Page Step 1
Source: Self

Step 2: A window will appear. In the displayed window, click on the Data Set on which you want to work.

Periscope Analytics - Creating Dashboards through Analytics Page Step 2
Source: Self

Step 3: Enter a title to create a name for the Dashboard. 

Step 4: This name appears at the top of the Dashboard list.

Step 5: Finally, click on “Create”. You will then be automatically guided through the process of creating your first Widget in the Widget Wizard.

2) Creating Dashboards through Periscope Analytics (Sisense) Rest API

Step 1: In Sisense, click on “Admin” at the top. After that click on “Rest API”.

Step 2: Next, select Version 1.0 on the top right of the screen. 

Step 3: Click on “Dashboards” and then on POST/Dashboards.

Step 4: Define values for different calls as per the requirement.

Step 5: Lastly, click on “POST”. The Dashboard gets added to the Dashboard list on the Analytics Page. 

Introduction to Data Science

Data Science focuses on finding answers to questions that one does not know even exists. It takes into consideration large sets of raw and Unstructured Data.

The experts use different techniques like Predictive Analysis, Statistics, Machine Learning, and more to parse massive datasets for solving complex business problems.

Introduction to Data Analysis

Data Analytics, on the other hand, is the Statistical Analysis of existing datasets. The main focus here is to find solutions to business problems for better decision-making. The result, thus obtained, can be immediately sent for improvement. Data Analytics focuses on some specific regions having specific goals.

Difference between Data Science and Data Analysis

Periscope Analytics - Data Science vs Data Analytics
Image Source

Often, people think Data Science and Data Analysis fundamentally represent and involve the same process.  However, this is not true. There is a slight difference between Data Science and Data Analysis, but both are said to be the different sides of the same coin.

Data Science is a broad concept that deals with massive sets of data, while Data Analysis is a part of Data Science.

How these two leverage datasets, is what makes them different. Data Science deals with the construction and designing of new processes, including Prototypes, Algorithms, and Custom Analysis. Businesses use Data Analysis to create a better strategy by examining a large set of data. Thus, it is more specific and concentrated than Data Science.


  • Sisense is beneficial in simplifying end-to-end Data and Analytics, thereby reducing time to build advanced data models.
  • It also offers an Automated Analysis of visualizing data of any size. Periscope Analytics/Sisense is a leading AI-driven Analytics platform that uses Business Intelligence to infuse data from anywhere & this is where Hevo saves the day.
Bhavik Soni
Freelance Technical Content Writer, Hevo Data

Bhavik is an expert in freelance writing within the data industry, seamlessly producing informative and engaging content focused on data science by leveraging his problem-solving skills.

No-code Data Pipeline For Sisense