The data dividend

Data programmes and large scale transformations tend to fail. In fact, 70% of them never achieve their objectives. Data transformation is extremely complex to implement, and showing an early return on investment is imperative. In our experience, the primary roadblock to successful change has been the struggle to show business value and align with what customers (both internal and external) need, particularly before momentum and faith in the initiative begins to waiver.

Our data transformation approach centres on data products, defined as high-quality, trustworthy, and accessible sets of consumption-ready data. These products evolve continuously, are overseen by a single product owner, organised by business domains, managed by cross-functional teams, and evaluated based on the business value they deliver through various use cases. At AND Digital, we aim to demonstrate early business value to maintain momentum and investment, ensuring that agility becomes ingrained in the organisation’s culture and systems after the change is implemented.

Traditional development runs into significant obstacles as they don’t share the above characteristics or measures that drive value. Typically, a project approach is taken which is completely different in its implementation and result.

Project delivery approach

Project delivery is focused on a specific outcome where data is tightly coupled to its use and associated business domain, which leads to significant duplication, wasted effort and delay as data pipelines are recreated for each use case.

Project teams are transient and have a heavy reliance on handovers between different functional teams to progress a data project from idea through to business consumption, risking significant delays. Often, there is a heavy reliance on a central data team that creates bottlenecks.

Product delivery approach

By contrast, data product architecture takes a proactive approach to data management for reuse and continuous curation, by applying the principles and approaches of product thinking. The end result is that companies can quickly adapt while also reducing the time it takes to implement new use cases by as much as 90%.

With data products, all of the skills and capabilities needed to develop and deploy are housed within the product team, with an operating model that is designed for fast flow, quick demonstration of value, and continuous feedback from the business.

The product approach has a cumulative effect by increasing in capability and business value over time. As the product and team matures, they are able to deliver more use cases with less effort, as reuse and team dynamics create a virtuous cycle of productivity and results. 

Realising your data dividend

Based on our experience and lessons learnt, we have developed a best practice framework. The application of it relies on the intersection of strategy, engineering, and product/platform delivery expertise. If implemented correctly, this approach can realise early business value, while also building the foundation needed to deliver on long-term transformation, creating a mature and high-value product ecosystem.

We worked with a client to revolutionise their data solution through a comprehensive data platform design and implementation. Using the framework outlined above, we designed a best-in-class event-based data and integration platform, supported by a robust governance model to ensure its sustainability for the future. Our approach included building a ‘delivery engine’ composed of multiple agile teams of engineers and analysts, who developed data products and a cloud data platform, creating a marketplace of high-quality operational data. Strategic enablement was a key focus, with dedicated teams ensuring that the platform delivered tangible business benefits through functions like data governance, quality assurance, data cataloguing, and security management.

As defined earlier in the article, data products are designed to be comprehensive and adaptable. By creating data products that hold the relevant information for specific domains, we have enabled this organisation to confidently handle future use cases.  We also established a proper enablement structure that ensured data security and quality, fostering trust among consumers, and refined the operating model, addressing scalability challenges encountered in early iterations.

Our client now has a robust, scalable platform along with new ways of working to better help them adapt to the future. This is just one example of how our framework can make a real difference, and we’ll continue to explore the key components of our framework in future articles, including:

1. Use cases & value: Identifying use cases that deliver business value, prioritising early gains and proving your approach, and building a long-term but flexible roadmap.
2. Data platform build: Developing a robust and scalable self-service data architecture that supports data product build.
3. Data enablement: Empowering users with the necessary tools, training and resources to effectively use data products and derive actionable insights.
4. Building successful data products: Establishing a ‘ways of working’ framework that outlines roles, processes, and delivery governance to manage the lifecycle of a data product. Additionally, implementing continuous delivery to create an iterative development process for building high-quality, impactful data products.
   
   

Start transforming your data strategy

We partner with leading organisations to maximise the value from their data. We’ve empowered clients such as Aviva, Heineken and Nationwide to transform their business operations and customer experience through tailored data programmes. Don't wait to transform your data strategy. Contact us today to start your journey, or visit our data insights page.