Data Mesh for Media Data Management

If you’re dealing with large-scale data in some way, chances are you’ve come across Data Mesh already. I recently had an opportunity to experience how Data Mesh works and found it can be game-changing in media and marketing data management, so I thought it’s worthwhile to share my learnings with you in some detail.

First of all, what is Data Mesh? It’s an approach to manage large-scale, enterprise data. The central tenets of the Data Mesh approach is to decentralize data ownership and enable domain experts to have more and better access to specific types of data, which in turn fosters collaboration across teams through a commonly understood set of standards.

A Decentralised Data Revolution

Data Mesh is a radical departure from traditional, monolithic data architectures. Instead of relying on a centralized approach to collect, connect, store and utilise data, it distributes data ownership and accountability to domain experts within the organization. Each domain expert team becomes the custodian of data within their area of expertise, leading to better insights and more informed decision-making. For example, the media billing team can take care of all financial data, while the audience team can take care of all consumer data and event-level data. With this decentralized structure, data becomes more accessible, relevant, and actionable for those who know it best. It's a completely different data paradigm, bringing experts closer to the data and lifting the constant pressure centralized systems and teams are under.

Empowering Domain Experts

Let’s explore how this is helpful for us in media and marketing. A mesh architecture can unleash the expertise of domain specialists. SMEs who possess deep knowledge of audience segmentation, content analytics, effectiveness/measurement often sit in different teams. By empowering these experts with ownership of their respective data domains, we can not only allow them to make data-driven decisions autonomously, but also to manage the data in a way that generates maximum value for the overall organisation. They in turn become the champions of data excellence, driving innovation and success in their specific areas while collaborating with other domains to unlock further synergies.

Collaboration and Democratisation

One of the core tenets of Data Mesh is fostering collaboration and democratising data access. The approach to encourage cross-functional collaboration is by facilitating transparent access to data across the organization through concepts like self-serve analytics, data products and data catalogues. Each of these are interesting topics and we can delve into them later. With these facilities in place, media and marketing teams can explore, analyze, and derive insights directly from the data relevant to their goals. This democratisation of data access not only promotes a culture of data-driven decision-making but also unleashes the creativity and expertise of individuals at all levels, fuelling innovation and accelerating growth.

Orchestrating Omni-channel Excellence

Data Mesh also plays a vital role in driving omni-channel excellence in media and marketing. Due to increased collaboration, and availability of domain-specific self-serve analytics, it’s easier to combine data from more sources, which is a primary prerequisite for omni-channel orchestration. Insights and decisions from various domains, e.g. planning, strategy, audience exploration, activation, content management, and customer journey management can be combined to create a cohesive, personalised customer experience. From web to social media to email, Data Mesh enables organizations to deliver exceptional, tailored experiences that resonate with their audience.

Of course, to be able to do all the above require some ground rules. This is why Data Mesh is based on four very well-defined principles:

• Domain-Oriented Data Ownership
• Data as a Product
• Self-Serve Data Infrastructure
• Federated Computational Governance

I’ll try to navigate what each of these mean, and how I’ve recently applied them to media data use cases in the next episodes. Stay tuned!