In Gartner’s Top 10 Data and Analytics Trends for 2021, trend No. 9 is “dashboards will be replaced with automated, conversational, mobile, and dynamically generated insights customized to a user’s needs and delivered to their point of consumption.” The Sisense Fusion platform embodies this idea, empowering every user to embrace analytics via actionable intelligence delivered directly to them where they spend their time.
As an analyst, developer, or even marketing or product leader, it’s more crucial now than ever to put personalized intelligence at the fingertips of the right stakeholders at the right place and time. Doing so empowers people to see past the noise of the typical dashboard and home in on key insights they really care about. The Sisense Q1 2021 release is focused on bringing customized analytics to each person.
Rapid, code-free customization with Sisense Themes
Simplify the way you deliver a fully personalized analytics look and feel to each of your customers and end users using new Sisense Themes . With our new code-free customization enhancement, you can rapidly create and group UI customizations at a granular level through a point-and-click experience into a Theme. Assign your Themes to groups individually (manageable via REST APIs too) or dynamically update themes in embedded dashboards and widgets through iFrames, Embed SDK, or Sisense.JS for a custom, integrated embedded analytics solution.
Advanced data transformation with Custom Code
Does your tech stack include both AWS and Sisense? If so, it’s your lucky day. With our latest release, you can leverage Python code from Jupyter Notebooks to connect to services such as Amazon SageMaker, Amazon Comprehend, and Amazon Translate to transform and augment data inside your ElastiCube models. Within Sisense, it’s easy to connect to these third-party sources and pick which sections of code you’d like to use to perform data cleansing, advanced transformations, or machine learning on your existing dataset.
Take Amazon reviews, for example: Say you’d like to do sentiment analysis on your text data to understand who felt positively, negatively, or neutral about your brand. By adding powerful snippets of Python code to your Jupyter Notebooks, you’re empowered to analyze text data in new ways to discover each reviewer’s sentiment toward your brand and ultimately extend your analysis with machine learning.
We’re further strengthening our relationship with AWS and proud to see new enhancements come to life that boost the ways you can use the Sisense platform together with the AWS environment. Dive deeper into Custom Code here .
Enhanced live model connection parameters
With enhanced live model connection parameters, you can now leverage one live data model for multiple customers who use the same schema structure in your data warehouse. This new functionality eliminates the need to manage many live models so you can consolidate them and easily make updates all in one place.
Define parameters within Sisense to connect to your live model, and say goodbye to the heavy lifting of replicating and managing multiple data models for your customers who use the same schema structure.
Prepare for the power of personalized analytics in 2021!
Don’t forget to upgrade your Sisense deployment to the most recent version that includes new features, fixes, and other enhancements. Are you ready to start this year off strong and infuse analytics with us?
This is just the beginning of an analytics-infusion-filled year with Sisense, and we’ve only scratched the surface of what’s new here in this post. Innovation is the name of the game, and we want you to join us on the journey to bring analytics to both your teams and customers. Learn more in our release notes !
