If your company is implementing a data-driven culture, data-based solutions, and Machine Learning, then you should consider implementing Data Mesh in your business. This concept is new in the world of technology and is considered as a trend that can help considerably with data analysis.
As this is a new concept, it could be a strategy that differentiates your business in the market, creating a competitive advantage that makes your company stand out. Read on to find out more!
What is Data Mesh and what is this new approach all about?
Data Mesh is a new data architecture model that operates in a decentralized way. First introduced by Zhamak Dehghani, director of technologies at Thoughworks North America, it aims to help companies deal with pain points associated with large-scale data.
This approach works in the opposite way to Data Lakes or Data Warehouses, which agglomerate all your information in a single repository. Data Mesh goes against the traditional view that Big Data needs to be centralized in order to have analytical potential.
Data Mesh arose from the need for companies to find new ways of dealing with problems and challenges linked to the large scale of data that needs to be analyzed today. This means that centralized data may not be enough to meet the company’s needs due to the sheer volume of agglomerated information.
Its premise is to enable scalable data analysis, as well as ensuring greater accessibility and availability of information. These two factors are crucial given that more and more applications are using Machine Learning and data-centric solutions.
What are the principles of Data Mesh?
To apply Data Mesh in practice, Dehghani has established four principles that need to be adopted to guarantee its functionality. Find out what they are below.
Making data available as a product
The domain’s data delivers products (datasets) and is accessible by other domains via APIs, codes used to connect solutions and platforms.
Federated data governance
This governance aims to enable interoperability between domains. This is done through policies, codes, standards, rules, and responsibilities. Thus, representatives of the teams in each domain must lead the operational governance of the model.
Infrastructure for making data available as self-service
These are technologies that allow data to be decentralized, such as integrated platforms for managing data from start to finish (analytical and operational). They also allow new domain teams to be created without depending on a centralized team.
Decentralized data architecture
This is the business context in which data product teams operate. The teams become the owners of the data lifecycle, must guarantee its quality, and are responsible for delivering value.
What role does this concept play in Analytics?
The implementation of Data Mesh has a direct impact on Analytics, bringing benefits to data analysis. Find out how below.
Agility and scalability
In general, the time to market, agility, and scalability of the business domain are improved. This approach also reduces the IT backlog, as project teams can act independently and focus more on products with data that is more relevant to the business.
Strong central governance
This form of structure makes it possible to control data compliance from end to end. Traditional architecture (such as data lakes and data warehouses) makes it difficult to reconcile semantics with the sheer volume of data.
By decentralizing operations and applying global governance guidelines, it is possible to deliver quality data, facilitate access and manage it more easily.
Cross-functional domain teams
Traditional data architecture approaches end up isolating teams. However, Data Mesh proposes a solution in which domain owners and specialists have broad control. This is guaranteed by the fact that IT teams have greater knowledge and control of the domain, work closer to the business and, if they are virtual, are more agile.
Faster data delivery
Often the configuration of the data infrastructure is an obstacle to data management. This includes activities such as data storage, identity management, data processing and monitoring, among others.
As Data Mesh provides an infrastructure that is easier to govern, your team becomes less burdened with problems and issues to resolve, allowing for faster data delivery.
What are some examples of the use of Data Mesh?
Data Mesh has practical applications that can boost a company’s development by solving common problems in an IT team’s routine. Find out below what these obstacles are and how this new concept can solve them.
Difficulty centralizing data
The common approach requires collecting data from various sources and connecting these different points to a central Data Lake or Warehouse. This movement of information is considerably expensive and can take a long time.
Thanks to Data Mesh, datasets are separated in each business unit, which minimizes the time needed to obtain insights. Operational teams can access and analyze data more easily and quickly.
Increasing volumes of data
The amount of data being collected is growing exponentially all the time, especially as more and more solutions are using data to improve their activities. As a result, the number of sources for data collection is also increasing, which negatively affects companies’ agility to derive value from this information and respond to changes.
The Data Mesh delegates various activities to individual teams or business users, bringing greater business agility and transformations at scale. In practice, different servers are used to collect the information and they update reports in real time, allowing decisions to be made more quickly.
Compliance with regulations
If the company collects data from other countries, it has to comply with the data legislation of that region, which also requires a lot of time and resources from the company.
Data Mesh allows for decentralized data management, eliminating the need for the entire server to comply with the legal jurisdictions of various locations around the world.
Data Mesh is a concept that is being studied and tested by companies around the world. However, it is certainly a trend that could add a lot of value to companies by solving growing problems for all companies that operate with data.
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