What is data mesh?

Published Mar 17 2023 Last updated Sep 21 2023 4 minute read
data mesh
Contributors
  • Sean Dougherty
    Written by Sean Dougherty

    A copywriter at Funnel, Sean has more than 15 years of experience working in branding and advertising (both agency and client side). He's also a professional voice actor.

Data mesh: you may have heard about the concept lately. In the world of digital marketing, it’s almost as big of a buzzword as “efficiency” right now. 

Say no more. We can help you understand what data mesh is, how it affects marketing, and what its true business value is. 

 

Data mesh defined

At its core, data mesh is simply an approach or strategy to data management. The approach focuses on the decentralization of data, and considers data as a strategic asset — as opposed to just a bunch of metrics that are produced as an outcome of your actions. 

A brief history on the evolution of data analytics

Don’t worry, no textbooks are required for this history lesson. Just quickly think back over the past couple of decades. It wasn’t that long ago when data (and data analysis) became the thing successful businesses needed. 

Fast forward to today, the explosion of apes and purposes of data has meant greater and greater storage solutions. Thankfully, the cost of those storage solutions has decreased over time. This increase in data storage capacity and decrease in storage cost also spurred the rise in machine learning. 

However, this all tends to favor a centralized approach to data management. Find one storage provider, stuff all of your data in it, and let your powerful machine learning capabilities find juicy insights for your business to act on.

This centralized approach can have its advantages. For instance, a business intelligence team can combine data from areas like marketing or finance to gain a much greater perspective on the health of the business and where it should go next. 

The central problem of a centralized data approach

At the end of the day, more data means more complexity. If you’re working with vast volumes of data from every part of the business, you might need a team just to manage the storage and organization of it all. 

Now, while the centralized approach for a local streetcar company may not be such a big issue, just think about the data needs of a multinational apparel brand. The storage and maintenance needs of the sales team alone could be massive. 

Also read: What is The Modern Data Stack

Or perhaps, since we’re all marketers here, think of the marketing and advertising data. There are so many platforms, it’s hard to constantly collect everything in one place yourself. And the data seems to keep multiplying somehow. 

In fact, the global volume of data tends to double every three years. Doubling data! And as more teams create more data, they will want to work with that data more. However, a centralized data approach runs everything through one location and one team. That single team is then expected to serve the needs of the other parts of the business. 

Serving up bottlenecks

Let’s step back and think of a restaurant for a moment. In this example, it’s going to be a fancy restaurant. White table cloths. Little plates of tiny, Instagram-worthy food that seem more like art than cuisine. A multi-sensory experience. Maybe a star or two? You know, that kind of restaurant. 

If we peek into the kitchen, we’ll see a head chef leading his team to craft each of the exquisite plates. But wait! The chef won’t share her recipes with the brigade and must approve every dish before it is sent out to customers. It will quickly become impossible to cook and serve food for every guest in a timely manner. 

This is essentially what happens in a centralized data approach. A bottleneck is created.

How data mesh can help

In a decentralized approach, that same chef would share her recipes with the rest of the team and allow them to make the dishes themselves. The chef manages the team and makes sure each station is maintaining the highest of standards, tasting and examining food throughout the service. 

Enough with the restaurants, though. Let’s get into how this benefits your business. 

Empowering the experts

With a data mesh approach, the teams with domain-specific expertise maintain ownership of their data while also sharing it with relevant teams. That means the marketing team owns the marketing data, while also perhaps sharing it with a BI team for broader analysis with the rest of the organization’s data. 

This has a couple benefits. A marketing team will know what data is meaningful for their needs, so it makes sense for them to be able to work with it directly. That means more meaningful and faster analysis. 

Making data integral to your process

In a data mesh approach, data isn’t just a bunch of numbers to throw into a report about last month’s numbers. Instead, data analysis becomes a core part of the business’ operations. 

Data analysis becomes particularly important for marketing teams. It can inform on what tactics or creatives worked and guide you toward the best next steps. It becomes the past and the future — a sort of marketing circle of life. 

With new machine learning capacity, data can help inform on emerging consumer behaviors, which can shape ad creatives, product designs, price changes, and much more. 

Managing it all

There is a fine, gray line when it comes to managing data permissions and access across your organization in a data mesh approach. On one hand, allowing self-service among teams can free up bottlenecks. But you may need to make sure that those teams aren’t affecting the underlying data during their analysis — before it is shared with the rest of the organization. 

Depending on your own data stack, the volume of your data, and the complexity of your organization, you’ll need to find the best solution for your own specific needs. 

The trick is to balance the needs of all the items in your organization who can benefit from working with data. Even in the fancy restaurant, the chef is still overseeing her team to make sure that everything is prepared to specification. She is tasting the food before it goes out to make sure it’s perfect. 

Some organizations may need this “head data chef” to maintain some sense of stability and consistency. 

More agility and speed for a modern world

The great thing about a decentralized or “data mesh” approach is the speed and agility it can grant teams like marketing. By avoiding bottlenecks, you can generate custom reports more quickly, leading to more insights, causing your team to make better-informed and more accurate decisions. 

Plus, as more teams start working with data more frequently across the organization, you will all become naturally more data fluent. Over time, you will become more comfortable and skilled in your data analysis, making you a more data-driven and data-savvy organization on the whole. 

If you’re interested in learning more about data mesh, including some more real-world examples of how a decentralized approach can benefit marketing, be sure to check out our latest Funnel Tip. And be sure to subscribe to our YouTube channel where you’ll get all of the tips and tricks to make you a better data-driven marketer. 

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