In classic agile/scrum fashion, now is the time to group, prioritize, and select the first set of stories to tackle. All the stakeholders should be involved in this process. Grouping can be done using traditional techniques like card sorting and affinity exercises. Sizing, business impact, and the team available can also play a role in which stories get done first.
Make sure to keep the analysis concrete, not hypothetical. Pick stories that are closely tied to jobs that the data consumers need to get done so that clear, measurable value is delivered in the end. Additionally, time box these deliverables and set a date to measure the results. This will help you reign in the temptation to boil the ocean on your first iteration.
6. Gather your sources in a data catalog
It’s time for data producers (typically DBAs or data engineers) to gather up raw data sources to answer the questions posed in the first set of analytics stories. As producers curate sources by story in your data catalog, consumers can evaluate and ask questions about those sources. The initial questions and findings are critical to capture france whatsapp number data in real-time and can’t disappear into the ether of chat or email. A great data catalog makes this curation, profiling, and question process fluid and eases the overall workflow. This is the step where it becomes clear why you should build a catalog and warehouse at the same time.
7. Build & Document your Data Assets
As data sources get refined into the architectural style you’ve selected, data consumers should be working with the data in real-time and evaluating how good the models are at answering the metrics questions posed. Data stewards build data dictionaries and business glossaries right next to the data being used. Since you’ve curated the sources by analytics story, the appropriate data assets are now discoverable by purpose. By making the data catalog the fulcrum around which the collaboration happens for your new data platform, all this knowledge capture happens in real-time. This minimizes the chore of having to go back and scrape data dictionaries from Google Sheets or write boring documentation. By incorporating your data catalog AS YOU BUILD THE ASSETS, you’re ensuring their reuse and minimizing your knowledge debt.
8. Peer review the analysis
At the end of your first data sprint, it’s time to peer review the work. The process is far more efficient with an enterprise data catalog in place. Your data catalog acts as a consumer and SME friendly environment to ask questions and understand results and prevents the kind of data brawls that happen when people show up to decision meetings with different results and definitions. Everyone can see who’s contributed to the work and other questions that have been asked. Work can be quickly and efficiently validated and extended. Your data work is all in one place: the data catalog.
9. Publish the Results
Congratulations! You’ve got your first set of data models in your shiny new awesome data management platform. Everything is well-curated by analytic stories, peer-reviewed, and documented in your cloud data catalog. You’ve done something good for the business and made it reusable at the same time. Best of all, your team did it without having a massive post-hoc documentation effort because the work was done in the data catalog from the beginning.
10. Refine and Expand
By working in an agile way with your data platform and data catalog at the same time, your assets will be well documented and organized by the time they’re published. With the next sprint coming up, you can now expand or refine the assets that are already published. A jumping-off point where assets are well documented and organized around use cases makes the next sprints easier and easier. You can then expand the program to include more lines of business or working groups. This expansion drives adoption, data literacy, and the data-driven culture we all aspire to.
If you’ve already started down the path of building out a new data warehouse or data lake, you can still adopt agile data governance practices and chip away at any knowledge debt you have (it’s never too late!). Adopting a data catalog that allows you to work iteratively on this will be the key to not feeling like you have an ocean to boil. If you’re interested in learning more or if you’re already working in this way, we’d love to hear from you. Please contact me at world.