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Discover the value of data products in the context of both tech & business. Also explore the impact of data products on different stakeholders & tech execs, highlighting the transparency & interconnected metrics within data products.
Data Products are more an approach than technology, much like Agile for Software. Agile went through its share of pushback from sticky processes and emerged as a must-have. Post-adoption, Agile became a game changer for software teams and products.
But an approach for software cannot necessarily be duplicated for data. We have seen that over most part of the last decade. Interestingly, the Data Product approach has changed that. It works for the core indefinite element of any data stack that is absent in the software stack: Data. And it needs to be actively enforced through a Data Product Platform that integrates with existing data stacks.
Agile tools, the likes of JIRA and Asana, enable Agile Software Development, but establishing a cyclic and accountable cycle for data development is slightly more challenging since the main ingredient, data, is subject to constant change.
DataOS solves this problem by enabling the Data Product Approach for data development. It establishes a repetitive and reliable cycle despite the dynamic nature of data. Similar to the benefits of Agile, its reliability comes from being testable and changeable at any point in the lifecycle.
The benefit of the data product approach lies in its anatomy itself. The secret is tightly coupling data with infrastructure, code, and metadata instead of loosely processing and projecting data for various applications.
When data freely floats across your infra, there’s hardly any scope for reproducible cycles for incremental evolution.
Just as diamonds cut diamonds and fire fights fire, an evolving or changing framework optimally handles ever-changing data.
Evolving lifecycles are the best bet to optimise the value of data that is intrinsically dynamic. Coupling data with infrastructure, code, and metadata allows it. The tight coupling fosters quick changes while maintaining high stability and interoperability with a plethora of tools, sources, and consumption endpoints that the data stack necessitates.
Every Data Product serves a specific purpose or drives a set of business metrics. The coupling between infrastructure and data layers ensures that all initiatives across technical and business verticals are transparently observable in the context of the target metrics, i.e. if they are fuelling or harming the business.
We explain this ability through a compact metric tree model for the office of CDO and CMO, where every low-level to high-level metric across tech and business layers is interconnected and traceable due to the anatomy of the data product (data + metadata + code + infra).
As a consequence, decision-makers can roll out informed decisions across solutions, operations, and development tracks without having to know the ins and outs of every vertical - only the factors that move the needle.
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.
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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.
1. Data Products enable the possibility of a transparent metric tree
2. Metric trees are a web of associated metrics cutting across the entire vertical of the data’s journey
3. How does it help CMOs & associated decision-makers:
Leveraging a Business Metric Tree (Data Product Output) for the Office of CMO
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Zooming into some identified categories on a CMO’s plate:
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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.
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We are using the CDO persona as a compact model to illustrate the impact of data products on a tech executive’s function.
1. Data Products enable the possibility of a transparent metric tree
2. Metric trees are a web of associated metrics cutting across the entire vertical of the data’s journey
3. How does it help CDOs & associated decision-makers:
Leveraging a Business Metric Tree (Data Product Output) for the Office of CDO:
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Note how both the diagrams have different pain points for business & tech execs, resolved through a standardized 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:
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The Data-as-a-Product Design.
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The Data Product Strategy | Becoming Metrics-First
