Datalore
Collaborative data science platform for teams
Constellation Accelerated Data Science R&D and Reporting by 50% With Datalore
Searching for ways to deliver better insights faster
To scale data science operations and provide valuable insights to both clients and internal stakeholders, Constellation needed a way to streamline collaboration and enhance agility. Issues like slow report generation in traditional business intelligence tools (Power BI and Looker) and disjointed workflows were limiting their ability to produce timely, customized insights. At the same time, they were looking for a solution that could seamlessly integrate with AWS, their cloud provider of choice.
“We were faced with a question: how can we have Constellation’s cross-functional teams work together seamlessly? Before Datalore, each engineer would operate independently – in local Jupyter notebooks. This process required collaborators to download and share these documents after each iteration, resulting in patchwork cloud services. This stifled speed, restricted scalability, and hindered our ability to manage permissions.”
The solution? A collaborative and intuitive data science platform that could connect data, customer success, and product management teams to speed up insight delivery.
“Tableau and Power BI, while user-friendly, present significant challenges when it comes to distributing work between multiple engineers. Even though Looker offers better integration with Git, it still falls short when it comes to facilitating and balancing collaboration, flexibility, functionality, and visual appeal. These tools are not designed to support a mid-size team trying to maximize innovation and collaboration.”
Adopting Datalore as a centralized data science hub
Datalore transformed how the Constellation team collaborates, consolidating previously scattered analytical workflows into a single tool.
Here’s what a typical Constellation workflow looks like with Datalore:
A data engineer pulls information from the Snowflake database into a Datalore notebook. Then, they use SQL to link the data they need to Datalore before switching to Python code and layering in data from various third-party APIs. Once the data engineer has consolidated and structured this data, they send the Datalore notebook link to a data scientist who then jumps into the notebook and begins deriving insights, creating visualizations, and modeling.
“The initial data scaffolding is a key part of our data engineering process, closely aligned with our product team’s understanding of customer needs. The ease with which Datalore allows us to integrate our data warehouse, Snowflake, into the larger system plays an essential role in this. It simplifies and centralizes the way we work with data.”
Once the data scientist has completed a first iteration, they loop in the product management and customer success teams for a business-focused evaluation. Following the initial review, the team decides whether they want to refine the iteration, go through another iteration, or transform the notebook into an interactive Datalore report for end users.
“Datalore serves as the glue between our remote teams across the Middle East and North and South America.”
“Datalore acts as a portal to a broader ecosystem. It provides foundational tools that help us move faster. This contrasts with the many other platforms that act as closed systems, limited only to the scope their creators envisioned.”
Constellation also finds value in Datalore’s Report builder, which gives them the ability to easily turn a Jupyter notebook into a user-friendly and interactive data story. Because Report builder lets Constellation hide code cells, organize Markdown cells, position charts, and add interactive controls to the report canvas, they can take full control over the data narrative and leverage open-source technologies and frameworks previously unavailable to them.
“Interactive reports in Datalore let us quickly deploy prototypes and seamlessly convey complex data stories. This dramatically demystifies data reports for our non-technical team members, offering a level of clarity and understanding not possible through previous reporting methods.”
“Datalore’s agility is crucial for client reporting, since our client needs vary widely by industry and objective, extending beyond standard templated reports.”
Results
Initiatives that used to take them months to complete within traditional BI tools, Constellation can now accomplish within weeks thanks to Datalore. The collaborative data science platform by JetBrains provides a noticeable boost in operational efficiencies across the board for Constellation.
“We’ve managed to reduce our development time substantially, often by 50% or more.”
By centralizing their data science workspace in Datalore, Constellation broke down silos and eradicated redundant workflows. The platform’s collaborative capabilities connected remote teams and enabled faster iterations with shorter feedback loops.
“Datalore’s real value lies in its ability to connect all of our teams, creating a shared workspace that simplifies communication and collaboration across the board.”
Looking ahead, Constellation intends to further embed Datalore into their processes by expanding the platform’s report scheduling capabilities. They also plan to use Datalore as a way to elevate internal training and keep pace with the company’s rapid growth.
“We plan to utilize Datalore as a training resource within our company. It will help us create structured modules that progressively introduce less experienced employees to our systems”
“Datalore has given me the opportunity to collaborate directly with more experienced data scientists and engineers on my team. It’s made it easy for me to grow and develop in my role.”
About Constellation Software
Constellation is an end-to-end enterprise AI-powered data analytics and content engine. Their SaaS platform, complemented by data-driven insights, empowers customers to execute marketing research and campaign development with enhanced efficiency and compliance, streamlining the creation and distribution of content within regulated industries, such as healthcare, fintech, automotive, and more.
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