Balancing the Field: Data Consumer Responsibilities in Data Contracts
A football game where half the team is unaware they need to score would indeed be a spectacle of confusion and inefficiency. Yet, isn’t this precisely what happens in many data organisations?
Today, numerous data organisations are adopting data contracts as a means to enhance collaboration and drive efficiency. Drawing from my experience in advising companies on data strategy, I have noticed a recurring pattern: while these contracts often meticulously define the responsibilities of data providers — from ensuring data accuracy to maintaining stable schemas — they frequently overlook the role of the data consumer.
It’s as if once the data is delivered, the responsibility of the consumer in extracting value from that data is taken for granted or assumed to be automatic.
Data leaders and professionals who care about business value creation should put more focus on the data consumer responsibilities. If the consumer does not efficiently analyse, interpret, and apply the data within the context of their business, the data contract isn’t living up to its full potential. Therefore, a truly effective data contract should also detail the consumer’s obligations, ensuring that they are equipped and ready to leverage the data as envisaged.
As we delve deeper, we will examine how neglecting the consumer’s commitments can reduce the business impact of data contracts. And more importantly, we will explore practical measures to incorporate such commitments, fostering a business value-first approach within data programs.
Beyond Compliance — Envisioning a Broader Role for Data Consumers
In the relatively scant literature that addresses consumer commitments within data contracts, there’s a distinct emphasis on risk and compliance. The descriptions lean towards outlining preventive measures and ensuring lawful use, rather than fostering a proactive approach to value creation. Key points often include:
- Data Usage: Strict terms on permissible uses of the data to prevent misuse or misapplication.
- Data Security: The obligation for consumers to implement robust protections for the data once in their possession.
- Regulatory Compliance: The need to align data usage with the intricate web of privacy and sector-specific laws.
- Data Integrity: The responsibility to maintain the data’s original state, safeguarding against unauthorised alterations.
- Incident Management: Established procedures for breach notification and response, emphasising a reactive stance.
While these obligations are essential for protection and compliance, they often place data consumers in a passive role. Recently, I spoke with a Chief Data Officer at a utilities company who shared an enlightening example. They had just implemented self-service capabilities for users to access datasets directly on their data platform, Databricks. This initiative was a technical success, evidenced by a high number of accesses. However, the CDO expressed a common concern: despite the increased data access, there was no clear visibility into the downstream value generated. This scenario is a prime example of the disconnect that can occur when the focus is solely on providing access, not on how that access translates to business impact.
To truly leverage data as a strategic asset, we need to extend our vision beyond these restrictive frameworks. The next section will delve into how the narrative and structure of consumer commitments can be transformed to encourage a more dynamic and value-oriented use of data.
Respecting the Producers — Committing to Value Creation
While it is natural to be absorbed in the technical details of data programme execution due to their inherent complexity, we have identified that these tasks are but means to an end. The complexity and the focus on technical tasks have inadvertently obscured the ultimate goal – driving business value. But how do we resolve this tension between technicality and business value? By putting business value first.
But what does that actually mean? It means shifting from a data-first perspective to a business-first perspective. It’s about looking beyond the jargon, algorithms, and infrastructural achievements to answer one fundamental question: “What is the impact of our data initiatives on the business?” This calls for aligning each technical task to a corresponding business goal and continually evaluating its effectiveness in terms of business outcome generation.
The truth is, irrespective of the complexity of data pipelines or the number of data sources integrated, if a data programme does not contribute positively to your company’s bottom line or other key performance indicators (KPIs), it is falling short of its primary purpose. That’s why taking a business value first approach is not just a strategic decision, but an essential shift in how data programmes should be assessed and executed.
This shift in focus does not negate the importance of technical tasks. Rather, it ensures these tasks are directly aligned to the central goal: generating tangible business value. This approach ensures that each decision made within a data programme, no matter how technical, is oriented towards driving meaningful business outcomes. In other words, it places the business context at the heart of data and analytics operations.
Scoring Goals — From Data Products to Business Value
The core principle is simple: every data engagement within an enterprise should yield value that surpasses its associated costs. In this context, a data product with solid SLAs, guaranteed reliability, and consistent accessibility is akin to an excellent pass in football — it creates the opportunity, yet the conversion to a goal rests with the data consumers.
In the process of conducting Business Value Creation Assessments, an approach pioneered at Kindata, I’ve come to appreciate the advantage of approaching value generation as two distinct phases:
· Delivery of Business Use Cases: This involves the data consumer crafting strategic reports, AI/ML models, or other analytical tools that inform and empower business decisions.
· Effective Business Usage: The ultimate success metric lies in how these tools are used by the business in achieving both financial and non-financial objectives. This phase often reveals the level of business unit involvement in data programs, a crucial factor in deriving true business value.
Data consumers are at the centre of business value creation. As the ones directly tapping into data products, they are in a prime position to ‘score the goals’ — that is, to deliver business value. Recognising their pivotal role means also understanding the need for accountability in how they utilise these data resources.
Formalising data consumer responsibilities
To foster this culture of accountability and ensure that data consumers are not just participants but active value creators, your organisation can adopt a structured approach with formalised data consumer responsibilities:
1. Document Data Usage: Record every interaction with a data product, specifying its purpose. Clarity at this stage sets the foundation for accountability.
2. Measure Outcomes: Regularly measure how the data products influence business objectives. This includes not only the delivery of business use-cases but also the ongoing engagement with business sponsors to make sure the value is actively realised through time.
3. Feedback to Data Producers: Share success stories where data products have significantly contributed to business outcomes and identify opportunities for further improvements.
4. Release Unnecessary Commitments: As a data consumer, when you determine that a data product no longer serves your purpose, promptly inform the data producers to release them from their obligations. This crucial step ensures resources are reallocated efficiently and maintenance efforts are not wasted on unused data products.
By embodying these practices, data consumers become proactive agents of value creation, ensuring data products consistently align with and contribute to the business’s overarching objectives. Of course, understanding these commitments is only the starting point. They need to be operationalised through the right combination of culture, governance and tools.
Aligning the organisation
Data products and data contracts are great ways of organising data teams and adding the extra focus on data consumer responsibilities can make a significant difference.
Just as in football, where every team member must be aligned with the objective to score, in the realm of data, clarity of purpose is equally critical. We cannot hope to win if a substantial part of our team doesn’t understand that the endgame is to score goals — to generate business value.
So, as we refine our playbooks and sharpen our strategies, let’s not lose sight of the goalposts. After all, in the game of data-driven success, it’s the goals that count.
Originally published in Towards Data Science on Nov 16, 2023