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Table of Contents
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the following is a revised edition.
This piece is drafted with feedback from our peers in the data community on how we can enhance the end-to-end guide on practical Data Products. Below is a summary of the requests we’ll be addressing in this brief article:
1. An easy way to convey the value of the Data Product Lifecycle to stakeholders who aren't invested in detailed materials or don't have enough time for the same.
2. A condensed view to revise the stages of the Data Product Lifecycle at a glance.
3. A direct mapping of how every stage impacts different data personas (currently addressed only implicitly).
We don’t want to restrict the scope of this article to only data leaders and influential executives. As startup folks, we are confident in how individual contributors or ICs, such as Data Engineers, DevOps experts, or even the surprising intern, could influence the organisation.
So even if you’re an IC, it’s of utmost importance to understand the core impact of data strategies on business that perhaps trickle down to the execution wing top-to-bottom.
Addressing: “An easy way to convey the value of the lifecycle to stakeholders who aren't invested in detailed materials or don't have enough time for the same.”
In other words, conveying the impact of Data Products on your executive’s favourite KPIs.
As is commonly understood, the Data Product approach spans a wide range of data personas, but practically, this tends to be both a blessing and a curse. The curse comes as a mindset shift to apply product thinking to data, which is a challenging and psychological transition for users across the org.
But once that barrier is crossed, it uplifts the entire organisation's ecosystem and every user's effort into a proactive engine - or very close to the state of a data-driven ideal.
A Data Product is in itself a hub of activated high-quality and well-governed data, which consequently cuts across vertical and horizontal cross-sections of a data-driven organisation, impacting both business leaders (CMO, CRO, CFO, CSO) as well as technical leaders (CIO, CDO, CTO) in terms of KPI performance, and of course, the day-to-day experience of the execution wing, aka data developers/engineers which we have covered in the lifecycle materials.
To convey the value across the spectrum of different stakeholders impacted, we have identified certain key drivers for two personas across both categories, Business & Tech, as compact models for understanding and conveying the impact of data products to such stakeholders.
➡️ Data Products enable the possibility of a transparent metric tree
➡️ Metric trees are a web of associated metrics cutting across the entire vertical of the data’s journey
➡️ How does it help CMOs & associated decision-makers:
Zooming into some identified categories on a CMO’s plate:
Note that some of these pain points, such as consistently securing executive support, consistently showing impact on revenue-related metrics, and even creating adaptive short-term & long-term strategies, are true for a wide band of business executives across both business & technical fragments of the organisation.
We are using the CDO persona as a compact model to illustrate the impact of data products on a tech executive’s function.
Impact of Data Products for CDOs at a glance.
➡️ Data Products enable the possibility of a transparent metric tree
➡️ Metric trees are a web of associated metrics cutting across the entire vertical of the data’s journey
➡️ How does it help CDOs & associated decision-makers:
Note how both the diagrams have different pain points for business & tech execs, resolved through a standardised approach that exposes metrics with high transparency across the vertical cross-section of data’s journey through infra, code, and data applications.
Zooming into some identified categories on a CDO’s plate:
The Data-as-a-Product Design.
We’ll share a quickly consumable overview of the different stages of the data product lifecycle soon. But if you want to do a deep dive, you’ll find all the materials here: End-to-End Guide to Building Data Products.
Addressing:
