A Socio-Technical Strategy to Empower Business Decisions

Note for reviewers: These opinions are my own and do not reflect those of my employer.

In this article I will enable your Enterprise Data Strategy by exploring the full lifecycle of the concept of data products, packaged, readily consumable data assets, from conception through to distribution.

There are 5 key points within the data product lifecycle:

1. Simplified data access for business users
2. Improved data discoverability and understanding
3. Increased data utilisation and adoption
4. Accelerated time to insight
5. Measurement and observability

Let’s get started with this journey, as with any good story there are 3 parts:

1. We will identify the problem statements
2. We will come up with a plan to resolve them.
3. Put the plan in motion with the right mindset, process and tooling.

Eliminate Data Silos

There is a silent crisis in todays Enterprise Landscape.

Data sits across different data silos, making data hard to find. Multiple versions of the same data and/or missing data create trust challenges.

The burden of data requests falls on Information Technology teams; which in turn slows down every department.

We cannot see the full picture, our most valuable data remains hidden, locked away in individual projects.

Data as a Product

Treating data as a product is the solution, this leads to, faster innovation, reducing time to value by eliminating redundant data efforts and enabling data teams to build on each other’s work. End-to-end accountability, establishing clear ownership for our data, ensuring its quality and reliability. Unlocking value from data, making our data assets discoverable and readable, turning them from a cost centre into a strategic asset.

In order to transition your enterprise from a locked in siloed data strategy to a value driven data product centric enterprise you will need to adjust your mindset. You need to start looking at data products through a socio-technical lens; socio-technical is a concept which bridges the gap between the social (people, culture, structure) and technology (tools, process, tech).

[embed]Sociotechnical Theory If we think about how we can apply sociotechnical theory in practice, we realise that organisational change or…psychsafety.com

Your journey toward a data-product-centric enterprise does not begin with a platform purchase; it begins with a conversation.

Identify a single silent crisis in your organisation today; one critical silo where data remains hidden and host a socio-technical workshop between the data owner and the consumer.

Draft your first data contract, not as a technical hurdle, but as a bridge. Start small, prove the tangible value and the cultural mindset shift will naturally follow.

If you apply a socio-technical lens to data products they should contain 4 things:

• Data
• Metadata
• Code/Logic
• Platform components

These 4 things orchestrate the lifecycle of the product, the bulleted items are the components of the data product lifecycle that will help you with your journey. In essence, they become the proof points which validate and demonstrate the value of your data governance and date management strategy to your business stakeholders.

Socio-Technical Data Strategy

You can start this process by investing in the people, changing the culture to align to business outcomes, improving the skills & literacy and doing it ethically. This is the big picture of every data/technology office, this is what keep us up at night, this is what we yearn to constantly improve. We are driven by the desire to do the right thing for our stakeholders. By doing so we will drive profitability and productivity and in turn, reduce risk and complexity.

You have heard many of the leaders in industry correctly state that there is no AI Strategy without a Data Strategy. I am taking this concept one step further, there is no Data Strategy unless it is first underpinned by a Social Strategy.

Without a robust socio-technical framework, an AI strategy is built on shifting sand. By making data products first-class citizens, you effectively eliminate the risk of shadow AI where models are fed by dark, ungoverned data silos. Treating data as a product ensures that your AI initiatives are built upon a foundation that is secure, discoverable, and natively accessible. It ensures that as you scale, your models remain as trustworthy as the data products that hydrate them.

Think of it as a modern interconnected organisational structure where an AI Strategy is the ultimate goal, built upon a solid Data Strategy, which in turn relies on an effective Social Strategy. This creates a hierarchy of strategies where the success of the top layer is fundamentally dependant on the layers beneath it.

A Social Strategy isn’t merely a commitment to better culture; it is the formalisation of the human feedback loop. In an enterprise, data doesn’t move itself; people move it. If your Operational Team perceives the act of providing data as a tax on their time rather than a contribution to a larger mission, even the most advanced technical strategy will eventually stagnate. We must shift the organisational incentive from data hoarding to data sharing, ensuring that the people who understand the data the most are the people that are empowered to advocate for its value.

As we delve into the world of data products lets us consider some of the characteristics of data products, a data product taxonomy if you will:

• Secure
• Discoverable
• Addressable
• Interoperable
• Cost-Effective
• Valuable on its own
• Understandable
• Natively Accessible
• Trustworthy
• Measurable

The above list is not exhaustive but it gives you insight into the some of the characteristics that data products have; therefore allowing you to choose a platform that supports those products.

When we consider data products we want to know:

• What does the data product contain?
• Who owns the data product?
• How do we hydrate the data product?
• How do we store the data product?
• How do we shape the data product?
• How do we tag the data product?
• How to we observe the data product?
• How do we secure the data product? i.e. who can access the data product?

This is where the concept of data contracts comes in, there is currently a lot of work happening in the community pertaining to data contracts.

Open Data Contract Standards: is a Community Effort; sponsored by the Linux Foundation. v3 is now divided into 11 sections, with more to come.

Typically you can construct these contracts in YAML and depending on your System Development Lifecycle (SDLC) you can choose to keep them outside the platform. Platforms, like Snowflake, are making efforts in the future to natively integrate these open data contract standards into their platforms.

Reflecting on my years in the telecommunications industry, the concept of data contracts feels like a homecoming. Back then, we relied on Interface Contracts to act as our primary source of truth; capturing critical information such as the schema, data quality rules, and SLAs. They were the gatekeepers that prevented the chaos of an upstream network update from inadvertently breaking a downstream billing or roaming system. Modern data contracts are simply the necessary evolution of this discipline, moving us away from integration by accident toward a future of integration by design.

Now let’s apply what we have learned in real life scenarios.

For simplicity, let’s assume there are 4 major data sources that are being ingested by your Standards / Ingestion Architecture Framework.

The first layer is critical, these are the master / principal domains, which define your business, who you are as a company.

The second layer is consumption focussed, the things you need to keep the business running e.g. Loyalty, Churn, Profitability etc.

The third layer, once you have built your data products, is how do you activate these data products and put them to work for analytical purposes.

In order to deploy data products via your own System Development Lifecycle it helps to follow a standard process.

The phases within this process can help you with how to approach data products in a structured manner. Thinking about the key components and strategy vehicles, the business objectives and ultimately delivery and activation. What is the demand in front of you? Begin with business outcomes in mind, prioritize and sequence your dependencies. Ask yourself, what are we solving for? What’s critical for our stakeholders?

Ideate, from “What if I had this product?” to “What will it solve and activate?”

You are seeking investment and buy-in, you need to sell the concept of your data product and you do not have to do everything yourself, it takes a village. Borrow from data mesh concepts and build teams that have the necessary components to deliver a data product from conception through to delivery. Ensure your teams have business representation and the requisite technical skills to bridge the gap between the social and the technology. Consider the data contract elements, think about all the ingredients you need to be successful, including the technical ecosystem which will accelerate your lifecycle and speed to market.

Remember we have to end our story with reflection and one of the most important aspects for the data office is measurement and adoption.

To truly measure the success of the data office and data products, we must move beyond vanity metrics and look toward “hero metrics” that resonate with the board.

• Impact

We should measure decision velocity, quantifying how much faster a business unit moved because the data product was ready and waiting.

• Quality

We should measure contract compliance, the percentage of products meeting their governed specifications.

• Enablement

We should measure the self-service ratio, tracking the reduction in manual requests hitting the IT team.

• Adoption

We should measure data product re-use, the number of distinct departments building new value on top of a single master domain.

Conclusion: The Socio-Technical Mandate

The silent crisis of data silos is not a failure of technology, but a failure of connection. As we have explored, the road to decision intelligence and a transformative AI strategy does not start with a new toolset; it starts with a mindset shift that recognises data as a living, social product. By bridging the gap between the people who understand the data and the technology that scales it, we turn hidden assets into strategic fuel.

We must move beyond managing projects and start nurturing a data product marketplace where trust, quality, and accountability are baked into every contract.

Remember, there is no AI strategy without a data strategy, and there is no data strategy unless it is first underpinned by a social strategy.

It is time to make data products the first-class citizens of your enterprise.

For an implementation strategy on how to build data data products on Snowflake, check out: https://medium.com/@datainplain/building-regulatory-grade-data-products-on-snowflake-for-fsi-938895e25e35