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Data Mesh: A Path to Data Availability And Accessibility at Scale

In the digital world, where every action leaves a footprint, it’s no secret that data is abundant. A recent study reveals that 1.145 trillion MB of data is created daily. From a business perspective, consumer data is a mecca of information that offers a way to better engage with current and potential customer trends and demands. Effectively using this data is key to creating customer-centric experiences. However, conventional data management faces performance and flexibility issues. As a solution to this problem, a new approach to data architecture called data mesh is gaining momentum.

In this article, we’ll explain data mesh, compare it to monolithic data infrastructures, and dive into the benefits data mesh offers, especially to sales and marketing teams.

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What Is a Data Mesh?

Simply put, data mesh is a decentralized approach to data architecture where data is managed by domain-specific data consumers. Each domain, represented by a business domain or a team in an organization, operates its own data pipeline, including functions like data ingestion, transformation, and loading. At an organizational scale, data infrastructure equips each domain with data management solutions, governs data access, stores raw data, and outlines common data standards.

What is data mesh?

To further understand the true concept of data mesh, it’s necessary to know a few key definitions and their roles in the data architecture diagram. 

▪️ Domain ownership: The core data mesh principle says that data must break down or decentralize, and the responsibility for handling it is assigned to the business team closest to the core information. Thus, the company ensures it sustains constant change and scalability of data. Essentially, by decentralizing the domain and distributing it to the business teams, the organization has absolute access to the data when needed, without having to extract it from a data warehouse. 

▪️ Data as a product: This principle aims to solve the problem of low-quality data that only exacerbates with the growing number of data owners. Data mesh treats data provided by domains as a product and consumers of this data as customers. Since the goal of any product is to solve customers’ pain and make them happy, data must meet certain characteristics to be considered a product. Thus, data must be discoverable, trustworthy, secure, etc.

▪️ Self-service approach: This principle is simple: for business teams to autonomously own their respective data products, they must have access to an easy way of effectively managing the expected data pipeline. Creating a self-service platform with specialized tools and interfaces makes the data mesh process flow easier.

▪️ Federated computational governance: This is a governance model that supports domain independence and ensures interoperability through common data standards and harmonization rules.  

Ultimately, data mesh is successful because it dispenses data ownership to the organization’s domain-specific teams, who manage data as a product. A self-service data platform allows your company to have faster and more accurate access to valuable data.

Data mesh principles

The Challenges of Current Data Platforms

What led to the formation of the data mesh approach to data architecture? Organizations are all too familiar with the challenges surrounding current data platforms. These architectural issues primarily stem from using a conventional centralization strategy. Not only are these monolithic data platforms expensive, but they’re also inflexible and cumbersome. 

As a decentralization approach, the data mesh process flow eliminates many of the prominent issues highlighted below. 

1. Data Import

Centralization problem: The current centralization strategy requires companies to import data to a central data lake or data warehouse. Here, the information is queried for analytics, which is an expensive process. 

Decentralization solution: Since data mesh views each strand of data as a product with its own domain, time-to-insights and time-to-value is significantly reduced. This means operational teams can analyze data faster and easier.

2. Scalability Response

Centralization problem: Any changes or query methods in the current data pipeline don’t respond to scale. As the number of sources increases, the response time to this new information decreases. Consequently, business agility is negatively impacted and decreases the value of the data. 

Decentralization solution: As part of the data mesh process flow, data ownership is delegated to various domains made up of business users or designated teams. This allows business agility to take place at scale, resulting in a much narrower gap between the time the data event happens and its extraction for analysis.

3. Data Migration

Centralization problem: In many instances, data is prone to privacy guidelines that forbid data migration to different jurisdictions. This results in data residency, and extracting this data requires a time-consuming process that subsequently delays processing and analyzing. For the most part, these types of delays happen between countries. 

Decentralization solution: With data mesh, the domains are singularly responsible for the security and transfer of the various data products. A connectivity layer enables quick and easy access to both technical and non-technical users at any location. This eliminates residency regulations and expensive data transfers. 

Centralized vs. Decentralized Data Approach

View our side-by-side comparison of the basic differences between a centralized and decentralized approach.

Factors to Consider Centralized Data Approach Decentralized Data Approach
Type of platform Centralized technology used by data warehouses/lakes Self-service platform technologies used by individual business domains
Data control Lack of transparency and no contingency against data ownership loss Domain-oriented approach with improved transparency
Costs Requires investment in data warehouses No need to invest in data warehouses or cloud platforms
Time factor Users must import data to a data warehouse which can be time-consuming Reduces time-to-insights, allowing users to access & analyze data much quicker
Scalability Needs changes in the data pipeline that don’t respond at scale, slowing down the response Delegates data ownership to domains, enabling agility and scalability
Success measurement Success is measured by the volume of governed table Success is based on the network effect
Data migration Privacy guidelines can prohibit data migration Domains maintain responsibility for security, quality and transfer of data products

Benefits of Data Mesh

Opting into a data mesh can result in several notable benefits over outdated data platforms. The most exceptional being:

1. Flexibility 

Implementing a data mesh architecture means that organizations are not tied down to one single data platform. Since data mesh has a more distributed infrastructure, companies will have access to a host of different systems. 

2. Tougher Data Governance 

Data mesh creates decentralized data operations, which in turn simplifies the process of controlling data security at the source. Together with simplified compliance to strict global data governance guidelines, companies will benefit from the ease of data access as well as quality data delivery. 

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3. Increased Business Agility

By decentralizing data operations, data mesh eliminates any potential IT backlog, which in turn reduces storage and operating costs. With a more accessible data infrastructure, businesses can bargain on improved time-to-market as well as domain agility.

4. Improved Transparency

With conventional data platforms, data ownership is largely isolated, resulting in a massive lack of transparency. When this happens, business teams must deal with data control and ownership loss. With data mesh, ownership is distributed among multiple cross-functional domain teams. Business teams, IT experts and virtual teams can use a domain-oriented approach to benefit from data quality.

5. Faster and More Accurate Access and Delivery

Organizations can use SQL queries to access data from anywhere. By using a self-service model, users can expect faster and more accurate delivery. Data mesh allows data to be available exactly when your business expert needs it.

6. Maximizes Received Data

For companies who rely heavily on data extraction and conversion, data mesh is the perfect solution. Unlike the archaic centralized data platforms currently in use, a data mesh solution allows businesses to have access to their data without requiring a data warehouse. Faster access to data means organizations can create more effective marketing strategies.

7. Increased Data Security and Improved Platform Connectivity

On-site sensitive data can easily connect to secure cloud applications in the form of live streaming or by accessing real-time information that exists on devices. Data mesh eliminates users having to route anything through data warehouses or public networks. Doing this creates considerably less risk of a data breach. It also reduces data latency, which in turn improves overall performance in instances such as online gaming, financial trading and live streaming.

What Are the Benefits of Data Mesh for Marketing, Sales, and Revenue Teams?

An organization won’t invest in a new data platform if there isn’t a considerable benefit to their bottom line. Aside from the general advantages of data mesh, marketing teams also benefit from this type of instant data availability. A decentralized approach to data: 

  • Speeds up time-to-insight, allowing teams to alternate faster 
  • Allows designated business teams to operate in a more agile and independent way 
  • Enables teams to better executive decisions due to data availability
  • Enables the marketing department to create competitive data-driven strategies 
  • Allows marketers to keep up with market trends and changes
  • Helps sales teams access up-to-date information to personalize customer and product offers

How can data mesh benefit your team?

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How to Prepare for Data Mesh Adoption?

If you’re preparing for data mesh adoption, there are a few steps you can take to ensure a smooth transition period. 

  • Identify the data mesh solution that is right for your business: Based on your business data goals, you must find a data solution that can be implemented on your current data infrastructure. The business sponsor you select should have a traceable track record of effectively providing this product to similar organizations. 
  • Attain funding: As with any new application, investing in a data mesh platform requires funding. Here, you can opt for buy-in from your sponsor company to take up ownership of the development of the data product. They should share your common goal of empowering designated teams to create business value from the received data. Furthermore, this acquired data product should be easily accessed via a self-service data platform that features embedded governance. 
  • Create platform readiness: The next step involves ensuring that your current data platform has the technical competence to onboard the new data mesh solution. This also involves ensuring deployment readiness following CI/CD best practices.
  • Train relevant teams: Identify the teams that will be responsible for the key functions. Use communication, training and mentoring methods to convey the organization’s step in this direction. It’s also critical to highlight the benefits to the individual teams, as well as to the overall ROI. Further training should happen in the form of collaborative workshops and the creation of study guides and training manuals.

Final Thoughts

Moving toward a data mesh platform will easily clear the bottleneck delays created by conventional data platforms. By moving data ownership to domain-specific business teams, your organization will benefit from the true potential of readily available enterprise data. Eliminate the hindrances of a conventional data availability program and optimize business insights at scale by becoming more data-driven.

Recommended further read: 

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