Bringing Business Analytics To End Users With GoodData - Episode 138
The majority of analytics platforms are focused on use internal to an organization by business stakeholders. As the availability of data increases and overall literacy in how to interpret it and take action improves there is a growing need to bring business intelligence use cases to a broader audience. GoodData is a platform focused on simplifying the work of bringing data to employees and end users. In this episode Sheila Jung and Philip Farr discuss how the GoodData platform is being used, how it is architected to provide scalable and performant analytics, and how it integrates into customer’s data platforms. This was an interesting conversation about a different approach to business intelligence and the importance of expanded access to data.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- GoodData is revolutionizing the way in which companies provide analytics to their customers and partners. Start now with GoodData Free that makes our self-service analytics platform available to you at no cost. Register today at dataengineeringpodcast.com/gooddata
- Your host is Tobias Macey and today I’m interviewing Sheila Jung and Philip Farr about how GoodData is building a platform that lets you share your analytics outside the boundaries of your organization
- How did you get involved in the area of data management?
- Can you start by describing what you are building at GoodData and some of its origin story?
- The business intelligence market has been around for decades now and there are dozens of options with different areas of focus. What are the factors that might motivate me to choose GoodData over the other contenders in the space?
- What are the use cases and industries that you focus on supporting with GoodData?
- How has the market of business intelligence tools evolved in recent years?
- What are the contributing trends in technology and business use cases that are driving that change?
- What are some of the ways that your customers are embedding analytics into their own products?
- What are the differences in processing and serving capabilities between an internally used business intelligence tool, and one that is used for embedding into externally used systems?
- What unique challenges are posed by the embedded analytics use case?
- How do you approach topics such as security, access control, and latency in a multitenant analytics platform?
- What guidelines have you found to be most useful when addressing the concerns of accuracy and interpretability of the data being presented?
- How is the GoodData platform architected?
- What are the complexities that you have had to design around in order to provide performant access to your customers’ data sources in an interactive use case?
- What are the off-the-shelf components that you have been able to integrate into the platform, and what are the driving factors for solutions that have been built specifically for the GoodData use case?
- What is the process for your users to integrate GoodData into their existing data platform?
- What is the workflow for someone building a data product in GoodData?
- How does GoodData manage the lifecycle of the data that your customers are presenting to their end users?
- How does GoodData integrate into the customer development lifecycle?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with GoodData?
- Can you give an overview of the MAQL (Multi-Dimension Analytical Query Language) dialect that you use in GoodData and contrast it with SQL?
- What are the benefits and additional functionality that MAQL provides?
- When is GoodData the wrong choice?
- What is on the roadmap for the future of GoodData?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- GDPR == General Data Protection Regulation
- IoT == Internet of Things
- Multi-Dimension Analytical Query Language