Data Engineering Podcast
Defining A Strategy For Your Data Products
Episode guests
Podcast summary created with Snipd AI
Quick takeaways
- Rudestack Profiles automates joint calculations and computations for creating comprehensive customer profiles, enabling businesses to quickly generate enriched data.
- A well-defined data product strategy helps optimize costs, improve efficiency, and enable scalability by streamlining the production process from inception to deployment.
- Building interfaces that limit inputs and provide relevant output, implementing observability and monitoring infrastructure, and questioning established norms improve the accuracy and reliability of data products.
Deep dives
Developing Complete Customer Profiles
Rudestack Profiles simplifies the process of creating comprehensive customer profiles by automating the joint calculations and computations needed. This allows businesses to quickly generate enriched data that can be utilized by multiple teams. The product focuses on optimizing customer experiences in retail, life sciences, and financial services industries by leveraging data engineering and data platforms. By using Rudestack Profiles, businesses can personalize content, improve marketing strategies, and optimize call center wait times.
Defining Data Product Strategy
A data product is a service that addresses the needs of a specific customer segment by providing a set of features. This can include data assets such as dashboards or models. A data product strategy involves formulating a plan to industrialize the production of these products, streamlining the process from inception to deployment. By focusing on capabilities and patterns, teams can reduce duplication of effort and simplify the production of new data products. A well-defined strategy helps optimize costs, improve efficiency, and enable scalability.
Delivering Effective Data Experiences
To ensure effective data experiences for end-users, teams need to consider the specific metrics they are optimizing for, such as the number of clicks or response time. It's important to build interfaces that limit the number of inputs and provide relevant output based on the desired questions. Implementing observability and monitoring infrastructure can help identify anomalies and patterns, allowing teams to proactively address issues. Additionally, documenting assumptions and questioning established norms can help uncover unexpected challenges and improve the accuracy and reliability of the data product.
The Importance of Industry Verticals in Data Product Strategies
The speaker highlights the significance of considering industry verticals when developing data product strategies. They explain how the nature of the industry and the relevance of data in decision-making processes determine the number and roles of the team involved. Different verticals, such as retail, require extensive rules and iterations to ensure accurate data, while others may only need one developer overseeing the entire process. The speaker emphasizes the need to align data products with the specific needs and characteristics of the industry.
Artifacts as Key Elements in Data Product Strategies
The speaker discusses the importance of artifacts in the formulation and implementation of data product strategies. They suggest conducting stakeholder interviews to understand the decision-making processes and use cases that the analytics will support. By translating these use cases into a portfolio of capabilities, programs, and projects, organizations can create data products that deliver value. The speaker emphasizes the need to consider metrics, governance, and data sets throughout the strategy development process. The focus is on building interfaces and channels that cater to different consumer needs and optimize the overall user experience.
Summary
The primary application of data has moved beyond analytics. With the broader audience comes the need to present data in a more approachable format. This has led to the broad adoption of data products being the delivery mechanism for information. In this episode Ranjith Raghunath shares his thoughts on how to build a strategy for the development, delivery, and evolution of data products.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
- As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES.
- This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold
- Your host is Tobias Macey and today I'm interviewing Ranjith Raghunath about tactical elements of a data product strategy
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what is encompassed by the idea of a data product strategy?
- Which roles in an organization need to be involved in the planning and implementation of that strategy?
- order of operations:
- strategy -> platform design -> implementation/adoption
- platform implementation -> product strategy -> interface development
- managing grain of data in products
- team organization to support product development/deployment
- customer communications - what questions to ask? requirements gathering, helping to understand "the art of the possible"
- What are the most interesting, innovative, or unexpected ways that you have seen organizations approach data product strategies?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on defining and implementing data product strategies?
- When is a data product strategy overkill?
- What are some additional resources that you recommend for listeners to direct their thinking and learning about data product strategy?
Contact Info
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.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Neo4J:  NODES 2023 is a free online conference focused on graph-driven innovations with content for all skill levels. Its 24 hours are packed with 90 interactive technical sessions from top developers and data scientists across the world covering a broad range of topics and use cases. The event tracks: - Intelligent Applications: APIs, Libraries, and Frameworks – Tools and best practices for creating graph-powered applications and APIs with any software stack and programming language, including Java, Python, and JavaScript - Machine Learning and AI – How graph technology provides context for your data and enhances the accuracy of your AI and ML projects (e.g.: graph neural networks, responsible AI) - Visualization: Tools, Techniques, and Best Practices – Techniques and tools for exploring hidden and unknown patterns in your data and presenting complex relationships (knowledge graphs, ethical data practices, and data representation) Don’t miss your chance to hear about the latest graph-powered implementations and best practices for free on October 26 at NODES 2023. Go to [Neo4j.com/NODES](https://Neo4j.com/NODES) today to see the full agenda and register!
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
- Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!
- Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today!
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