Real-World Use Case: How Data Mesh Saved Christmas 🎅📈 (Data Mesh – Part 2)

After an introduction to Data Mesh in the first part, let’s now imagine that you work for “WAL Tech”, a company that sells Christmas products online. Each year, the company faces the challenge of managing a massive spike in data during the holiday season 🎄.

Let’s explore how Data Mesh changed the game and what ROI was achieved.

The Problem 😩

Before Data Mesh, WAL Tech had a centralized data team that managed everything: sales, marketing, supply chain, etc. Each department had to wait to get the data they needed. This delay hampered marketing campaigns and affected inventory decisions. Faced with these challenges, WAL Tech decided to seek a more efficient solution: the implementation of Data Mesh.

Implementing Data Mesh 🌐

WAL Tech decided to test Data Mesh. They decentralized data management:
• The Sales team 🛒 took charge of sales data.
• The Marketing team 🎯 managed advertising campaign data.
• The Supply Chain team 📦 handled stock information.

Each team prepared and maintained their own dataset, making them accessible to the entire company.

Duplication 🆚 Opportunity Cost

In Data Mesh, we will likely have duplicated data, technology, infrastructure, and even technical specialists. But, you measure the cost of duplication versus the lost opportunity costs resulting from bottlenecks and slow core processes.

I offer to you a real-life reference from a large company. In this pretty interesting case study (see link) provided by McKinsey, a large mining organization significantly reduced the time spent on data engineering activities and developed use cases seven times faster than before, increasing the stability and reusability of data after implementing Data Mesh.

While decentralization and Data Mesh may lead to some duplication, the opportunity cost associated with slower delivery could be significantly higher.
Moreover, Data Mesh will increase productivity, time to market, better alignment of departments with the company’s strategic objectives, and last but not least, the organization’s data driven culture will be enhanced.

In summary, the benefit in speed and agility provided by Data Mesh usually far outweighs the costs of duplication. But what happens when this concept is applied to specialized roles within the organization? Let’s see it with an example.

Example of “Duplication” of Specialized Roles 👥

Imagine WAL Tech has a small group of highly qualified data engineers in its centralized data team. These engineers are responsible for maintaining data infrastructure, ensuring data quality, and facilitating data access for the entire organization.

Duplication Scenario 🔁

With the implementation of Data Mesh, each business domain or team (Sales, Marketing, Supply Chain, etc.) would require their own data engineer or even a small team of specialists. This could be seen as a “duplication” of specialized roles within the organization, with the additional cost of hiring, training, and retaining these highly qualified professionals.

Opportunity Cost 💹

Consider the opportunity cost of not having specialized data engineers in each team (and let’s not even talk if you don’t have them in the centralized team right now 😉). In a centralized scenario, requests for data cleaning, complex analysis, or new data pipelines would have to go through the centralized team. This could result in long waiting times and delayed business decisions, which could be costly in terms of lost market opportunities.

Balance ⚖️

While hiring more data engineers may seem costly in terms of human and financial resources, the opportunity cost of not doing so could be much higher.
Having data engineers embedded in each team allows for faster decision-making, more specific analysis, and better data quality, more closely aligning data strategies with business objectives.

Now, let’s see how all these changes translate into tangible results for WAL Tech.

The Outcome 🎉

1. Agility: The marketing team was able to react quickly to trends, adjusting their campaigns in real-time.
2. Efficiency: The inventory was updated accurately, avoiding excess stock or a lack of popular products.
3. Collaboration: Teams could collaborate more easily since each had access to the data they needed when they needed it.
4. Ownership: Each business domain was responsible for the lifecycle of the data in their domain, including business people, data analysts, and data engineers, maximizing quality, availability, security, and value.
   

ROI (Return on Investment) 💰

1. Time Savings: There was a 40% reduction in waiting time to access data.
2. Increase in Sales: A 25% increase in sales due to more effective marketing campaigns.
3. Cost Reduction: A 15% saving in storage costs by optimizing inventory.
   Adding up these benefits, the ROI was exceptionally high. The project paid for itself in less than six months 🚀.
   

Final Summary🎬

In this article, we have explored how Data Mesh can be a game-changer for organizations facing challenges in data management. Using the case of “WAL Tech,” a company that sells Christmas products, we’ve shown that implementing Data Mesh can:

1. Increase Agility: Allowing teams to respond quickly to market trends.
2. Improve Efficiency: Optimizing inventory management and reducing waiting time for data access.
3. Foster Collaboration: By giving each business domain ownership of its own data.
4. Maximize ROI: With tangible results in time savings, an increase in sales, and cost reduction.
   

We also addressed concerns about data and role duplication, arguing that the benefits in speed and efficiency often outweigh the associated costs.
If you’re considering modernizing your data infrastructure, Data Mesh offers a decentralized approach that can closely align data strategies with business objectives. It’s more than a buzzword; it’s a revolution in data management.

I hope this article has opened your eyes and invites you to delve deeper.
In fact, in the next article, we will delve into whether it is necessary to have technical profiles on each of the teams and the difference between a Data Owner and the figure of a Data Product Owner that emerges in the context of Data Mesh. Don’t miss it!

If you liked this article, PLEASE GIVE IT A LIKE, COMMENT, SHARE, and MENTION a friend who might find this information useful!! THANK YOU ❤️

Excellence in Data & Analytics is a journey, and it’s better when done in good company 👫