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Facilitated by The Modern Data Company in collaboration with the Modern Data 101 Community
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TABLE OF CONTENT
TL;DR
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.
Part 1
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.”
Acquiring Stakeholder Support for Implementing A Data Product Strategy
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.
Business Stakeholders: Likes of CMO, CRO, CSO, & CFO
An overview of some of these roles
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.
Impact of Data Products for CMOs 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 CMOs & associated decision-makers:
- Ability to detect tracks which are fuelling primary business metrics/KPIs
- Ability to detect tracks which are pulling down the primary KPIs
- Informed calls on empowering or shutting down tracks across business initiatives and technical tracks without the need to be technically savvy or skilled.
- Complete transparency and detailed lineage behind all metrics that are pushing the key metrics such as Revenue, Conversion Rate, or #MQLs forward.
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.
Tech Stakeholders: Likes of CDO, CIO, & CTO
An overview of some of these roles
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:
- Ability to detect tracks fuelling primary metrics around data infra & initiatives
- Ability to detect tracks which are pulling down the primary KPIs
- Informed calls on empowering or shutting down tracks across technical tracks without the need to know the ins and outs of every ongoing initiative.
- Complete transparency and detailed lineage behind all metrics that are pushing the key metrics such as revenue from digitally enhanced products, time to ROI for new digital initiatives, TAT of data-to-insights, and increase in data usage by different segments in the org.
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:
What’s Enabling Your Metric Tree?
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.
Part 2
Addressing:
- Need for a condensed view to revise the stages at a glance
- Direct mapping of stakeholder involvement across the different stages.
Co-Author Connect
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Author Connect 🖋️
Bhava Kompala
Head of Customer Success and Growth
Results-oriented, growth driven senior executive with a demonstrated history of 23+ years of experience in the services industry and analytics.
Animesh Kumar
Cofounder & CTO at The Modern Data Company
Results-oriented, growth driven senior executive with a demonstrated history of 23+ years of experience in the services industry and analytics.
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