What is a Data Workspace?

Hoshang Mehta
October 4, 2023

Data Workspaces are a type of tool that helps people do complex data work, like analyzing data and creating artifacts, while also allowing them to collaborate with others and easily share their results. They are different from traditional BI tools because they are more flexible and don't try to enable self-serve data access across an entire organization.

What is a Data Workspace?

Data Workspaces are powerful, versatile tools for data analysis. They share many of the same characteristics as traditional BI tools, but with a greater focus on flexibility and collaboration. This new approach to data analysis moves away from the drag and drop querying and dashboarding paradigms that are typical in BI and allows users with a technical background to connect multiple data sources, query and analyze them in a highly flexible environment, and then publish the results in the form of curated applications or data documents that can be consumed by end-users. All of this can be accomplished in one tool. This is an exciting development in the world of data analysis, providing users with the ability to create sophisticated data environments that can be shared, analyzed, and manipulated with ease.

It is very important to remember that there are many data tools available. This can be helpful, but it can also lead to inefficient workflows. For example, have you ever had to take a screenshot of a chart from an analytics tool and copy it into a document or presentation? Or have you ever had to run a query in Snowflake and download it as a .csv file to use in a local Python environment or tool?

A Data Workspace is a tool that enables a streamlined analytics workflow from exploration to publishing results. It facilitates collaboration and iteration throughout the entire process.

A complete Data Workspace are built around these three things:

  1. Knowledge Gathering: A Data Workspace has a way to organize and browse published work - a convenient and accurate way to access the knowledge that your team needs. This isn't just a random assortment of projects or insights, but rather a curated, organized and intentional library that has been created with a specific purpose in mind and is constantly updated. This eliminates the “scratchpad” effect and ensures that knowledge is easy to access and always up-to-date. In addition, it allows the team to quickly and efficiently find the information that they need without having to search through a multitude of documents. This effective organization of knowledge can give your team an invaluable advantage, as it ensures that trusted, accurate information is always available.
  2. Interface Agnostic- Data Workspaces offer a highly flexible analytics experience; specifically, they provide an extremely flexible interface for analytical work and data exploration. This flexibility is best captured in the form of a notebook experience, which is perfect for linear, exploratory, story-telling data work. To enhance this flexibility, many tools support multiple programming languages, enable data interpolation and rich text integration, or can connect to a wide variety of data sources. This type of flexible analytics experience enables complex analysis without any limits, as it is not just a drag and drop BI tool. Data Workspaces offer a great deal of control and flexibility to their users, allowing for a truly personalized data exploration experience.
  3. Collaborative- Embraces collaboration, sharing, and publishing: A Data Workspace should provide users with the ability to take the outputs of an analytical project, condense and reshape them into a published artifact that can be easily shared with non-technical end-users. This artifact can be presented in a variety of forms, such as traditional dashboards, stories, documents, tools, and more. Unlike PDF exports or slide decks, these artifacts are live and interactive, allowing users to gain an end-to-end Data Workspace experience. Furthermore, the artifact can be updated and adjusted in real-time based on the user's needs, providing a much more dynamic experience than a static PDF or slide deck.

Why use a Data Workspace?

Data Workspaces are experiential layers built on top of your data that lets even non-technical users collaborate on and consume the value of analysis. This combination of features has enabled data teams to unlock a level of data exploration and processing that is unprecedented in the industry.

Data Workspaces don’t necessarily enable analyses that were 100% impossible before their existence; complex and flexible analysis has always been possible to do. However, the introduction of Data Workspaces has vastly simplified and accelerated the process of deriving meaningful insights from data. While data teams previously had to spend countless hours developing and fine-tuning complicated queries, they can now leverage the power of Data Workspaces to quickly generate actionable results. Data Workspaces just take something that used to be a process involving long hours of manual work and instead turn it into a streamlined, efficient process.

Data Workspaces are different from BI tools

Business Intelligence (BI) tools are really useful for companies, helping them to look at data and create dashboards. But they may not be enough for users who need more detail and power. Data Workspaces are designed for this, providing tools for deeper research, complex models and other things that BI tools can't do. Data Workspaces are also useful for trying out new projects and data, as it is less time-consuming than other methods. So, Data Workspaces are a great choice for teams who need to quickly explore new data or try out new projects.

Airbook- A Modern Data Workspace

At Airbook, we believe that both- consumers and operators of data should never have to fully leave one system and start over in another.

Airbook is a modern workspace aimed at bringing revenue and data teams on one page from Day 0. One page that enables-

  1. to have the power and flexibility to answer data & business questions together by letting anyone connect to various data stores and query them through drag-and-drops (Revenue teams) and/or code (data teams).
  2. for everyone to trust the process by letting everyone see the business logic/code and the author’s commentary.
  3. a way to collaborate on, present, gain approval and/or consensus and share these insights with a wider audience to validate decisions.
  4. a way to track the process by letting them store and retrieve the data, conversations around data and the context around decisions made on one single page

As we’ve been building Airbook we’ve been interviewing a number of operations analysts to see how our solution change the way data is used amongst teams. Here’s what we’ve found-

Airbook makes information consumable by everyone, in one single place. Thereby, trust issues start to improve since they are viewable (and interpretable) by anyone so no one dismisses the numbers on the basis of not knowing where they came from. Moreover, airbooks are full of author’s commentary to help guide readers on how to interpret the numbers and any considerations to take.

Airbook empowers analysts to act as true advisors of data rather than being ‘report factories

Airbook is one home built for data and revenue teams to come together and drive the last mile of analytics together!


What is a Data Workspace?
How do Data Workspaces differ from traditional BI tools?
What are the main components that make up a complete Data Workspace?
Why should teams consider using Data Workspaces instead of traditional analysis methods?
In what ways are Data Workspaces beneficial over BI tools for deeper research?
What is Airbook, and how does it fit into the Data Workspace narrative?
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