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Data mesh is a distributed architecture for data management that enables organizations to move away from a centralized approach and instead treat data as a product. The data mesh approach involves breaking down data into smaller, self-contained domains or “meshes.” Each mesh has a dedicated team responsible for managing its data domain and lifecycle. This includes data ingestion, processing, quality, and distribution. These teams work autonomously, creating a federated data ecosystem that encourages collaboration and flexibility.

Improved collaboration and agility
Data mesh enables cross-functional teams to collaborate effectively, breaking down traditional barriers that have hindered productivity in the past. In manufacturing and consumer packaged goods (CPG), for example, teams can work collaboratively to gain insights into production and supply chain operations, reducing lead times and improving overall agility. Moreover, data mesh architecture enables teams to work more efficiently by reducing the time spent searching for data, enabling them to focus on analyzing the data and generating insights.

Better data quality and governance
With data mesh, each team is responsible for the data within their mesh, ensuring that the data is of high quality and well governed. This approach helps reduce data duplication, improve data accuracy, and ensure that data is being used ethically and in compliance with regulations. By promoting a culture of ownership and accountability, data mesh architecture can help organizations build trust in their data and make more informed decisions, in a more rapid time frame.

Increased innovation and experimentation
Data mesh can create an environment of innovation and experimentation by enabling teams to access data quickly and easily, without the need for centralized data management. This approach encourages teams to test new hypotheses and explore new opportunities without being constrained by a centralized data management model. Manufacturing, CPG, and other organizations can leverage data mesh to experiment with new products and processes, optimize supply chain operations, and drive innovation across the business.

Improved customer insights
Data mesh architecture can provide a better understanding of customer behavior and preferences, which is essential for many organizations. By breaking down data into smaller domains, teams can create a more complete view of customer data, enabling them to personalize marketing campaigns, optimize product portfolios, and develop new products that meet customer needs.

Reduced costs and increased efficiency
Data mesh can lead to reduced costs and increased efficiency by enabling teams to work more effectively with data. With autonomous teams managing data domains, the need for centralized IT teams and data management resources is reduced, lowering overall costs. Additionally, data mesh architecture can increase the speed of decision-making, reducing lead times and improving overall efficiency.

Finance
The finance industry is data-intensive, and adopting data mesh can help it handle its vast amounts of data more effectively and efficiently. With data mesh, finance firms can establish domain-specific teams that can create their own data products, making the data creation process more agile and responsive. Additionally, data mesh can improve data quality and governance, leading to better compliance with regulatory requirements.

Healthcare
The healthcare industry generates and uses a significant amount of data, including electronic health records, medical images, and patient-generated data. With data mesh, healthcare providers can improve data interoperability, leading to better patient outcomes. Moreover, a decentralized data ownership model can help healthcare organizations manage the privacy and security of patient data more effectively.

Retail
The retail industry is characterized by a vast amount of customer data generated through online and offline transactions. With data mesh, retail companies can create domain-specific teams that can work on specific data products, leading to better insights into customer behavior and preferences. This, in turn, can lead to more targeted marketing and increasingly effective sales strategies.

Food and beverage
The food and beverage industry relies heavily on supply chain management to ensure the quality and safety of products. By implementing a data mesh approach, different stakeholders within the supply chain, such as farmers, manufacturers, distributors, and retailers, can have ownership and control over their relevant data domains. This promotes transparency, real-time data sharing, and collaboration, leading to improved visibility and traceability throughout the supply chain. Food safety and quality are also critical concerns in the industry. With data mesh, each domain can define its own data-quality metrics, standards, and monitoring mechanisms. This enables data owners to proactively identify and address issues related to product quality, safety, and compliance. Additionally, real-time data sharing and analytics can help identify potential risks, such as foodborne illnesses or contamination, enabling rapid response and mitigation.

Efficient inventory management is crucial to avoid wastage and ensure freshness in the food industry. Data mesh enables individual domains, such as distribution centers or retail outlets, to have ownership and control over their inventory data. This facilitates real-time inventory monitoring, demand forecasting, and efficient stock replenishment, minimizing waste and optimizing stock levels based on customer demand patterns.

Manufacturing and CPG
The manufacturing and CPG industries have always been data-driven, relying on vast amounts of data to optimize production, streamline supply chains, and better understand their customers. However, the traditional centralized approach to data management has led to numerous challenges, including data silos, slow decision-making, and limited collaboration across teams. The concept of data mesh offers a new approach to address these challenges.

Transportation
Data mesh can have significant implications for the transportation industry, which is heavily reliant on data to optimize routes, track vehicles, and ensure safety. By adopting data mesh, transportation companies can create a more decentralized approach to data management, with each team responsible for the quality and accuracy of their own data. This can lead to more efficient use of data nearer to real time, as teams can work independently to improve their own data rather than relying on a centralized data team. Agility can be increased, empowering teams to respond quickly to environmental concerns, customer needs, or market shifts.

Agriculture
The agriculture industry is transforming rapidly and becoming increasingly reliant on data to optimize crop yields, track weather patterns, and manage supply chains. By establishing and adopting a data mesh approach, agriculture companies can create a more decentralized approach to data management, with each team responsible for the quality and accuracy of their own data. This leads to more efficient use of data, as teams can work independently to improve their own data, rather than relying on a centralized data team. As these teams innovate, new ways to include and leverage data are analyzed, incorporated, and distributed.

Construction
The construction industry is increasingly relying on data to optimize building designs, track construction progress, and manage supply chains. By adopting a data mesh approach, construction companies can take advantage of a decentralized approach to data management, with each individual team responsible for the quality and accuracy of their own data. Efficiency gains are quickly realized as teams work independently and increase the quality of their data and decisions. This improvement in collaboration is facilitated by better data sharing and teams interacting more efficiently, sharing data more easily and partnering to optimize building designs and construction processes.

Real estate
The real estate industry has always been heavily reliant on data to track property values, manage rental properties, and optimize marketing campaigns. Adopting a data mesh approach can lead to more widespread use of data among the various teams. As these teams work independently to improve their own data, additional and deeper insights are often created. Additionally, this often leads to increased innovation, as teams experiment with new approaches to data management and analysis.

Many industries and organizations are facing increasing pressure to become more data-driven and agile. As outline above, data mesh architecture offers a new approach to data management that can enable organizations to break down traditional silos and build a more collaborative and agile data ecosystem. By embracing data mesh and treating data as a product, each team is responsible for their own data, allowing these industries to achieve greater efficiency, agility, innovation, collaboration, and overall success. Ultimately, data mesh architecture can help organizations to become more data-driven and competitive in today’s fast-paced business environment.

However, it is important to carefully plan and implement a data mesh approach, as it requires significant changes to organizational structure, processes, and culture. With careful planning and execution, the benefits of a data mesh approach can be significant and long-lasting.

If your organization isn’t prepared to execute the entire process of moving to data mesh, a modular approach of implementing valuable and attainable steps offers sound foundational building blocks that will improve partnership and cooperation between IT and business units. Progress related to governance, data engineering, data quality, and documentation around data resources increases trust and partnership within the organization. Additionally, this closer partnership between IT and business units will help alleviate data silos, duplication of effort, and pain points with existing data stores or data lakes.

As IT partners with the business units to progressively move forward, there are a few things to keep in mind.

Business units have to be ready to own their data. Successful data mesh cannot be “pushed out” from IT. A data culture needs to be established in the business units, where they are equipped to own their data, improve agility, and take responsibility for data that they have not historically had to be responsible for.

Decentralized development can be more expensive than a traditional operating model of centralized data engineering. Attention needs to be paid to business units that may now need to hire and support their own technical teams. While this leads to positive outcomes around increased agility and reduced time to delivery, it can create challenges as it relates to certain roles like testing. In addition, while agility and flexibility are desired outcomes of adopting data mesh, this can come at the expense of simplicity. These are additional reasons for putting in the effort to build a strong partnership between IT and the business units, including clear communication and agreement in priorities.

In summary, data mesh is a great option for many organizations but not for all. After assessing your organization and industry, you may find that focusing on the fundamentals will lead to a future where your organization is more flexible and scalable, with data that is more accurate and contextually relevant to the business units that now own it.

If you’re interested in diving deeper on how to tell if data mesh is the right approach for your specific business, along with best practices for implementation at every stage of maturity, check out The Building Blocks of Success: Is Data Mesh Right for My Organization?