Increase Your Odds Of Success For Analytics And AI Through More Effective Knowledge Management With AlignAI
Data Engineering Podcast
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Is There a New Way to Redesign a Waterfall Design Approach?
There's kind of a three phase approach one is okay what capability are we focused on. Then the seconds getting the workflow configured so they take the appropriate templates from that list under that capability. And then the next part after that is configuring all of the examples and showing how it can be used in real-world situations. So for data ops we have a bunch of different what we call modules that they can grab whichever modules are applicable to them that they're currently doing.
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Summary
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.
Announcements
- 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
Interview
- Introduction
- 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?
Contact Info
- @RehganAvon on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
- AlignAI
- Sharepoint
- Confluence
- GitHub
- Canva
- Instructional Design
- Notion
- Coda
- Waterfall Design
- dbt
- Alteryx
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- MonteCarlo:  Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit [dataengineeringpodcast.com/montecarlo](https://www.dataengineeringpodcast.com/montecarlo) to learn more.
- Atlan:  Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means? Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to [dataengineeringpodcast.com/atlan](https://www.dataengineeringpodcast.com/atlan) and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.
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