Making effective use of data requires proper context around the information that is being used. As the size and complexity of your organization increases the difficulty of ensuring that everyone has the necessary knowledge about how to get their work done scales exponentially. Wikis and intranets are a common way to attempt to solve this problem, but they are frequently ineffective. Rehgan Avon co-founded AlignAI to help address this challenge through a more purposeful platform designed to collect and distribute the knowledge of how and why data is used in a business. In this episode she shares the strategic and tactical elements of how to make more effective use of the technical and organizational resources that are available to you for getting work done with data.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Rehgan Avon about her work at AlignAI to help organizations standardize their technical and procedural approaches to working with data
- How did you get involved in the area of data management?
- Can you describe what AlignAI is and the story behind it?
- What are the core problems that you are focused on addressing?
- What are the tactical ways that you are working to solve those problems?
- What are some of the common and avoidable ways that analytics/AI projects go wrong?
- What are some of the ways that organizational scale and complexity impacts their ability to execute on data and AI projects?
- What are the ways that incomplete/unevenly distributed knowledge manifests in project design and execution?
- Can you describe the design and implementation of the AlignAI platform?
- How have the goals and implementation of the product changed since you first started working on it?
- What is the workflow at the individual and organizational level for businesses that are using AlignAI?
- One of the perennial challenges with knowledge sharing in an organization is managing incentives to engage with the available material. What are some of the ways that you are working to integrate the creation and distribution of institutional knowledge into employees' day-to-day work?
- What are the most interesting, innovative, or unexpected ways that you have seen AlignAI used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on AlignAI?
- When is AlignAI the wrong choice?
- What do you have planned for the future of AlignAI?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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