In our latest Sisense release, we came out with the first iteration of Notebooks, a new way to perform ad hoc analysis on disparate datasets, develop powerful charts that tell your data’s story, and provide users with a single platform for both in-depth analysis and BI that preserves data security and integrity. Read on to learn more about Notebooks and get answers to the most common questions people ask, straight from our Director of Product Management, Pat Bhatt.

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Matt Madden (MM): Pat, can you give a short introduction to what Notebooks is and why it’s being integrated into Sisense Fusion Analytics?

Pat Bhatt (PB): The Notebooks functionality empowers data analysts with the tools they need to conduct advanced analysis using SQL, Python, and R. With Notebooks, users can query data from any data source, visualize results in custom charts, or even take analytics further using procedural code before visualization. In addition to visualization, outputs can be materialized or serialized to any destination, including cloud data warehouses.

Notebooks helps boost data analyst productivity with integrated workflows, source control, advanced security, and much more.

MM: What customer pain points are we trying to solve with Notebooks?

PB: One huge pain point Notebooks solves is that self-service tools do not answer all questions for the data analyst; particularly, semantic models can limit the type of questions business users can ask. Notebooks allows analysts to orchestrate information with custom queries using windowing functions, common table expressions, and other advanced tools to glean more complex insights from datasets with high accuracy and precision.  

All the analysis and insights a company needs may not be achievable through SQL alone. For example, to perform predictive analysis or time-series analysis, procedural code is required. By allowing the SQL results to be processed with Python and R, analysts are empowered with significantly greater analytical capabilities.

Notebooks also makes it very easy to process and reuse data. For instance, users can create materialized views wherein data is acquired, cleansed, curated, and enhanced, and later becomes part of another analytics workflow where it’s repeatedly used. This saves users tons of time in the long run versus performing these steps over and over again every time.

Analysts also struggle with disconnected source control and version management tools. Notebooks solves this with Git (or other version control systems) integration.

Additionally, data experts know that there are hundreds of different chart types, including Sankeys, box plots, and others that are popular in the market. Notebooks supports custom JavaScript chart types as well as Python and R chart types available through Matplotlib, SeaHorn, GGPlot, and many others.

Notebooks also makes collaboration with fellow analysts easy by allowing in-app sharing: Charts developed via Notebooks can be saved to dashboards or embedded into webpages like any other Sisense output. Notebooks also makes it easy for analysts to access cloud resources through libraries such as Boto/AWS, GCP, and others.  

MM: What’s unique about what Notebooks brings to the market?
PB: The market is full of tools that offer fragmented workflows and manual procedures, compromising productivity, accuracy, and security. Notebooks offers an integrated workflow, so that the analyst goes from model design to advanced analysis to visualization to source control, all in one location, and with the highest degree of data security. 

MM: What are some of the key features in Notebooks?
PB: Key features include SQL to charts, SQL to Python/R to charts, materialization, version control for charts and source code, advanced library management allowing custom Python packages such as Boto, SeaHorn, scikit, and TensorFlow, among others, to be imported. The Sisense platform additionally offers a rich set of features including caching, modeling, SSO, APIs, security and control, and plug-ins. We are starting with SQL Notebooks in Q2 and should be ready for GA in Q3 of 2021.

MM: What is Sisense Fusion Analytics, and how does Notebooks fit into Sisense Fusion Analytics?

PB: Sisense Fusion Analytics offers self-service capabilities as well as tools for data analysts, so any type of intelligence may be gleaned from business data. Notebooks fills a huge gap with self-service tools by allowing for integrated custom analysis. 

MM: How does Notebooks allow teams to better work together?

PB: Teams may work on specialized activities such as modeling or advanced analysis with queries, all in a single environment. Furthermore, source and access control allow teams to rest assured that productivity and collaboration remain high while keeping each analyst’s work separate. 

MM: Is it important to have one single platform for all of these aspects involving analytics? Why?
PB: In a very fast-paced and dynamic business environment, delays in data processing or the acquisition of insights make the difference between a win and loss. Fusion Analytics delivers an integrated, end-to-end decision-making platform to help not just succeed, but thrive in this decision economy. Fusion Analytics offers speed, flexibility, and the ability to acquire deep insights quickly and easily for all stakeholders in the enterprise. 

>>>Reveal deeper intelligence from your data with Python and R

Learn how

Matt Madden, Sisense’s Senior Director, Go to Market, has over 20 years of experience in the data and analytics market. He’s held roles in sales and marketing, all with the goal of helping organizations make better decisions with their data.

Pat Bhatt is Director of Product Management, Cloud Analytics, at Sisense. He has over 20 years of experience in product management and innovation in the tech space, having led product management at Model N, SkyNovus, Intuit, and Silicon Valley Bank.

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