


A company that identified and addressed this need is Plotly—a prominent data science and AI company that takes data science out of the lab and into the business environment. When Plotly began in 2013, Python was taking over the world, and the demand for Python-driven analytics was accelerating across industries. Identifying this need, Plotly has set itself on a mission to provide the Python community with an enterprise-grade data visualization platform.
Today, Plotly is widely recognized in the market for Dash Enterprise, a novel platform that enables the conversion of Python scripts into production-grade apps in a completely self-service way. It radically simplifies the application development and deployment process by allowing data scientists and domain experts to build and deploy data apps, visualizations, and interactive analytics with Python in an enterprise environment.
As a category-defining technology, Dash Enterprise excels in bridging the gap between the low-code, business intelligence (BI), and data science markets. It provides Python users with control over their data to deliver customer-facing dashboards, reports, and web-based applications that display data in real time. “Data scientists, engineers, and analysts use Python and Dash Enterprise as a low-code development environment to build applications in a few hours without full-stack development resources,” says Jim McIntosh, the Executive Chairperson at Plotly.
Plotly’s Dash Enterprise allows for easy deployment, scaling, and authentication, enabling clients to embed web-based applications to any browser for easy access and data consumption by anyone with appropriate enterprise credentials. Companies can deploy this highly flexible platform on the cloud, on-premises, or in air-gapped network environments.
“Dash Enterprise is currently used by over 20 Fortune 500 companies, across many different industries including banking, pharmaceuticals, energy, utilities, healthcare, and government,” says McIntosh. The company recently worked with a leading North American public utility with over 6,000 employees managing its power network operations. The client couldn't keep up with their customer requests, including downed wires and power outages.
Dash Enterprise allowed the client to explore and quantify data visually. They identified that over 90% of the department's work orders were consistently backlogged. Based on this data, they built a web-based application that allowed managers to gain data views by technician, location, number of orders, and job type. Several technicians, managers, and engineers regularly used the application to deploy the right person to the right jobs and ensure that no one was over or underworked.
Within one year, Dash Enterprise enabled the public utility company to reduce its overdue rate of work orders from 90% to 3%, reducing customer complaints by 10 times and saving several hundreds of thousands of dollars each year.
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Data scientists, engineers, and analysts use Python and Dash Enterprise as a low-code development environment to build applications in a few hours without full-stack development resources.
Going forward, Plotly will continue to enhance its capabilities to address enterprises’ Python analytics and visualization needs of the future. The innovation labs at Plotly are busy working on facilitating Dash Enterprise's integration with clients' existing platforms such as Databricks, Snowflake, Google BigQuery, DataRobot, and Domino Data Lab.
Company
Plotly
Headquarters
Montreal, QC
Management
Chris Parmer, Co-Founder & Chief Product Officer
Description
Plotly is widely recognized in the market for Dash Enterprise, a novel platform that enables the conversion of Python scripts into production-grade apps in a completely self-service way. It radically simplifies the application development and deployment process by allowing data scientists and domain experts to build and deploy data apps, visualizations, and interactive analytics with Python in an enterprise environment. As a category-defining technology, Dash Enterprise excels in bridging the gap between the low-code, business intelligence (BI), and data science markets. It provides Python users with control over their data to deliver customer-facing dashboards, reports, and web-based applications that display data in real time.
