A conversation between 2 Chief Data Officers

Two Chief Data Officers meet during a cocktail party. They start to talk.

[Chief Data Officer 1]
Last week, since our regular controller was out of office, our CEO asked me to report on last month’s revenue. After searching for half a day, I found four results:
- a Power BI report indicating an ‘Invoice Volume’ of €3.5 million, without additional documentation
- a second Power BI report indicating ‘Net Sales’ of €3.7 million, with some documentation on the report in an Excel stored in Microsoft Teams
- an embedded analytics report in our ERP system indicating ‘Total Net Sales’ of €3.5 million, with a technical explanation available via the initial ERP blueprint documentation
- an Excel sheet forwarded to me by a colleague, assuring me that the included ‘Net Sales’ of €3.2 million was correct

Feeling rather unsettled, I had to report these various numbers to our CEO, confessing I was not sure which one of these were taking into account intercompany sales, credit notes, transportation costs or service fees.

[Chief Data Officer 2]
Having multiple reports available isn’t necessarily bad, it’s a sign your organization is actively working with data to generate insights! You might want to consider adding a metadata catalog.

[Chief Data Officer 1]
How would this help me?

[Chief Data Officer 2]
A good metadata catalog consists of a glossary, data dictionary and data catalog combined in one. With intuitive access for all data users across the organization, this enables anyone to easily find and understand data insights, wherever they are coming from.

[Chief Data Officer 1]
Alright I’m triggered, tell me more.

[Chief Data Officer 2]
While supporting a data and analytics landscape, it can be hard to keep track of all existing reports, queries and data sources throughout the organization. That’s why it’s no luxury to have a data catalog that automatically detects and catalogues the full range of your data assets, no matter where they are located.

This way, you have all your data assets in one place, which is great! To make sure your users easily understand the content of your catalog, you can maintain a structured set of KPI and data definitions in a transparent data dictionary.

Finally, colleagues across functional departments and geographical regions often speak their own language. Customer, GDPR, SKU, YTD… You’ve probably heard it all. To make sure everyone speaks the same language and interprets specific terms correctly, a glossary can help. Any term in the glossary can then be referred to when documenting your assets or data definitions.

[Chief Data Officer 1]
Not bad! That does sound like a lot of work though…

[Chief Data Officer 2]
Yes and no. Growing in data maturity is a continuous journey. However, deploying a cloud-native data catalog and leveraging its out-of-the-box connectors can be done in a few days’ time.

Enriching your catalog via harmonized KPIs, data definitions and glossary terms can be done iteratively over time. I always recommend to be ambitious, but not afraid to start small. Starting with 1 domain, your 20% most popular reports or a key business unit could be the best way to experiment, learn and help boost user adoption.

For areas where you can’t yet provide dedicated support, you can choose to trust your users’ expertise and give them a centralized way to contribute and share their data knowledge.

[Chief Data Officer 1]
This indeed seems like a good approach to avoid ambiguity across our reporting and to be able to work with data more efficiently. I would love to have that realized!

Interested to hear how a metadata solution can help your organization gain more value from its data? Reach out via pieter.delaere@dscribedata.com.