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[DSC Europe 23] Ivan Dundovic - How To Treat Your Data As A Product.pptx

In today's digital age, data has become a valuable asset that can drive innovation, improve decision-making, and enhance business performance. However, many organizations still struggle to fully leverage the potential of their data. This presentation aims to shed light on the concept of treating data as a product and how it can transform the way we handle and derive value from data. We highlight the importance of data as a strategic asset and discuss key principles such as data governance, quality management, data contract and data monetization.

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Ivan Dundović
How To
Treat Your Data
as a Product
„The world’s most valuable resource
is no longer oil, but data”
- The Economist
„Information is the oil of the 21st
century, and the analytics is the
combustion engine”
- Peter Sondergaard, ex
Gartner executive vice president
„Data is like sunshine, we keep using
it, and it keeps regenerating”
- Ruth Porat, CFO of Google
Value extracted
out of data
• Since the early 2000’s enormous
amount of data have been
generated and collected
• Did companies get the value they
wanted out of that data?
Traditional data management approach
Data Warehouse
Analytics and reporting
Operational systems
and other data sources
This is the point where organizations
usually start thinking about data management
Tools are fine
We need a different approach

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[DSC Europe 23] Ivan Dundovic - How To Treat Your Data As A Product.pptx

  • 1. Ivan Dundović How To Treat Your Data as a Product
  • 2. „The world’s most valuable resource is no longer oil, but data” - The Economist „Information is the oil of the 21st century, and the analytics is the combustion engine” - Peter Sondergaard, ex Gartner executive vice president „Data is like sunshine, we keep using it, and it keeps regenerating” - Ruth Porat, CFO of Google
  • 3. Value extracted out of data • Since the early 2000’s enormous amount of data have been generated and collected • Did companies get the value they wanted out of that data?
  • 4. Traditional data management approach Data Warehouse Analytics and reporting Operational systems and other data sources This is the point where organizations usually start thinking about data management
  • 6. We need a different approach
  • 7. • Data is definitely a valuable asset, but it is only as valuable as the business value it generates • Cool data stacks and pipelines are not worth enough if they don’t deliver value to the business • The focus of every business should be the customer • Product management guides every step of a product’s lifecycle by focusing on the product and its customers first • It is a well-established practice in application development • Should it be applied in data management as well? What is missing?
  • 8. • The term was introduced by Zhamak Dehghani in 2019 • Central data teams often become a bottleneck • Organizations have been using domain-driven design, product teams and decentralized microservice architecture, but still relly on the overloaded central data team to get the necessary data-driven insights • Zhamak proposes to get out of this by shifting the responsibility for data from the central data team to the domain teams Data mesh
  • 9. Data mesh principles • Domain ownership • Data As A Product • Self-Serve Data Platform • Federated Computational Governance
  • 11. Data as a product • Projects a product thinking philosophy onto analytical data • There are consumers for the data beyond the domain • A data product is owned by a domain team • The domain team is responsible for satisfying the needs of consumers from other domains by providing high-quality data
  • 13. Baseline usability characteristics of a data product • Discoverable • Understandable • Addresable • Secure • Interoperable • Trustworthy • Natively accessible • Valuable on its own
  • 14. Data Contract • Data products serve high-quality data to the consumers in their or any other domain • To simplify interoperability and addressability, increase data quality, and decrease time to market, each data product offers a data contract to its consumers • In essence, a data contract is a formal agreement between the data product provider and the data product consumer to use a data product • Implementations of a data contract can vary, it can be fully automated with cool features, but it’s best to start small (maybe a simple yaml file) and build on it • There is an Open Data Contract Standard based on PayPal’s data contract template implementation, it is maintained by a non-profit organization called AIDA User Group https://github.com/bitol-io/open-data-contract-standard
  • 18. Data Mesh introduces a couple of new roles Domain data product owner Domain Data product developer Self-Serve Data Platform Product Owner
  • 19. How to start • Move data management closer to applications that produce data • Ownership of data should be under the teams that produce and manage data, not only central data teams • Use tools and workshops like Event Storming Shift left and distribute ownership to domain teams • Standardise interfaces and documenting mechanisms • Embrace self-service and invest in it • Start with a simple implementation of data contract and evolve it as you go Make data discoverable and well-documented • Think of your data as a product that needs to satisfy your customer needs • Introduce Data Product roles • Give your customers a quality product that can be immediately used to produce value Customer-first approach • Mindset and organizational change will take the most energy • Technology will support you along the way Data stack will come naturally

Editor's Notes

  1. At CROZ, we help our clients by providing consulting and implementation services for various data solutions. And from most of our clients we’ve been hearing how they don’t have trust in their data. For some data they are not even sure where it is stored, where it’s coming from and where it’s going to. Those kind of questions are especially important when GDPR or other regulatives needs to be applied. For streaming applications, situation is not looking better, metadata is badly documented and it’s quite common to have applications failling because changes in the data are not properly propagated. If you recognise yourself in these questions, you’re in the right place
  2. These are usability characteristics and are oriented around the experience of the consumers. Technical characteristics are covered in the chapter 9. Listed characteristics are an addition to what we have known as FAIR data in the past – finability, accessibility, interoperability and reusability
  3. First problem that data governance can help us with is data lineage. With data lineage we get a clear understanding of where data resides, and it’s entire flow from source to the final target system. Now, imagine the scenario where you have thousands of microservices that are consuming and producing some data, if that entire process is not properly documented, visualized and can be searched, it would be very hard to answer the questions like „what are all applications that are consuming from some Kafka topic called e.g. transactions„?” Let’s see how that question can be answered with one of the most popular open source data governance tools „Apache Atlas”