Data Engineering Podcast Increase Your Odds Of Success For Analytics And AI Through More Effective Knowledge Management With AlignAI
Dec 29, 2022
59:21
1 chevron_right 2 chevron_right 3 chevron_right 4 chevron_right 5 chevron_right 6 chevron_right 7 chevron_right 8 chevron_right 9 chevron_right 10 chevron_right 11 chevron_right 12 chevron_right 13 chevron_right 14 chevron_right 15 chevron_right 16 chevron_right 17 chevron_right 18 chevron_right 19 chevron_right
Introduction
00:00 • 2min
Alain AI - How Did You Get Your Start?
02:01 • 3min
Align AI - The Number One Reason That Companies Can't Move Fast
04:39 • 2min
Align AI Platform and the Tactical Elements of the Workflow?
06:32 • 4min
Align AI
10:09 • 5min
The Importance of Standards in Machine Learning and Analytics Development
15:04 • 3min
Do You Have a Problem With Knowledge Capture?
18:24 • 4min
Is There a Paradigm Shift in Data Management?
22:51 • 2min
Are There Any Commonalities in the Failure Modes?
24:33 • 4min
Is There a New Way to Redesign a Waterfall Design Approach?
28:51 • 4min
The Learning Elements That Make a Process Efficient Workflow
32:47 • 2min
Is There a Difference Between a Poll Approach and a Push Approach?
34:55 • 5min
Data Engineers Analyze Data With Monte Carlo
39:31 • 5min
Is There a Need for Community Within the Organization?
44:05 • 3min
Integrations in Data Workflows
46:40 • 3min
What Are Some of the Most Interesting or Unexpected or Challengesome Lessons You've Learned?
49:21 • 2min
Align AI
51:00 • 2min
The Future of Align AI - What's Next?
52:41 • 2min
What's the Biggest Gap in Data Management?
54:17 • 5min
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
- When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don't forget to thank them for their continued support of this show!
- Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
- 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 to learn more.
- 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.
- Linode:  Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to: [dataengineeringpodcast.com/linode](https://www.dataengineeringpodcast.com/linode) today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!