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Data Mesh: The Evolution of Data Products and Their Significance in Modern Data Architecture

June 12, 2024

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As the amount and complexity of data keeps growing, organizations are changing how they handle it. Saving data in only one place doesn’t work well anymore. That’s why new ideas like data mesh are becoming popular. This system focuses on spreading out control of data to the different parts of a company that use it.

 

The Changing Data Management Landscape

While data warehouses are good for storing lots of structured data, they can be tricky to use without specialized skills. This can cause data to be kept in separate places, making it hard for businesses to adapt quickly. With the rise of big data and the increasing need for quick insights, things had to change.

Data lakes were created to address this issue. They can handle all sorts of data, unstructured or not, and are more flexible. However, data lakes can still be messy and difficult to use.

A new way to organize data that keeps it flexible like a data lake but also makes it easier to manage is through a data mesh.¹ The concept was first introduced by Zhamak Dehghani in 2018 while she was working as a principal consultant at Thoughtworks. It is based on four core principles:

  • Domain Ownership
  • Data as a Product
  • Self-serve Data Platform
  • Federated Computational Governance

Out of these four, data product is the most fundamental.

 

What are Data Products?

Data products are like the smallest unit of a data mesh architecture. They’re self-contained, self-describing, and business-centric tools that handle data from a particular domain and supply datasets for analysis through output ports. They benefit users in several ways:

 

Enabling Democratization

Democratization involves making data easy for everyone who needs it to access and understand. Data products have simple interfaces and features that let people look at and understand data on their own without needing help from data experts or IT. This can help the company make decisions faster and use data better.

 

Improving Data Quality

Data products improve data quality by offering tools to check, clean, and standardize data. By doing this automatically, they make sure data is accurate, consistent, and up to date, cutting mistakes and lifting data quality.

 

Increasing Data Agility

Data products can boost data agility by giving instant access to data and letting users adapt fast to new business demands. With flexible and scalable data systems, organizations can swiftly adjust to seize new chances or tackle challenges.

 

The Make-up of Data Products

At its core, data products consist of three major components.

  • Metadata: This is data that describes other data, giving it context and meaning. Metadata-as-code uses code-based tools to manage metadata, making sure everyone understands the data’s meaning and keeping it up-to-date automatically.
  • Code: In data systems, code refers to software or scripts that process, analyze, or change data. Tools like infrastructure-as-code and metadata-as-code help manage data systems consistently and reliably, so decisions can be made faster.
  • Infrastructure: This includes the hardware and software that store, process, and move data. Infrastructure-as-code manages infrastructure using code, ensuring consistency and reliability across the organization.

 

Examples of Data Products

Depending on what’s needed, data products can take different forms.

 

Data Analytics

These are software solutions like Google Analytics or Power BI that help businesses collect, process, analyze, and visualize large amounts of data. They enable users to gain insights, clean and transform data, and perform advanced analytics techniques.

 

Recommendation Systems

These automated customer support systems use user data analysis to provide tailored recommendations and assist users in finding relevant goods, services, or content. They are widely used in a lot of different industries, like social networking services, e-commerce, entertainment, and content platforms. Google Maps, Airbnb, and Spotify are a few well-known examples.

 

Predictive Models

Predictive models, such as weather forecasts, are statistical models that forecast future events or trends based on past data. They assist in decision-making, operational enhancement, and risk identification across a range of industries.

 

Generative Models

These statistical models, like GPT or BigGAN, create new data similar to training data, often used in unsupervised learning for tasks like image or text generation.

 

Real-time Dashboards

These are the kind of visualizations that display the most recent data on the user’s screen automatically. They offer up-to-date insights, enabling users to track and monitor data in real-time, which can enhance business operations and increase productivity. In addition to being used for data monitoring and analysis, real-time dashboards can also be used to create a monitoring and analysis process that is armed with business intelligence alerts that will alert users as soon as a specific event takes place.

 

Data APIs

Data Application Programming Interfaces (APIs) are a collection of guidelines and procedures that make it possible for various software programs to easily exchange and communicate with one another. By acting as a link between different systems, they enable the request, retrieval, and manipulation of data from databases, web services, and other sources. Real-time API integration in the financial industry is one example of how data APIs are being used in the real world. Stock trading platforms use data APIs to give investors the most recent market information.

 

Common Challenges with Data Products

Using data products has lots of good points, but it also brings some challenges.

 

Change Management

Moving from a centralized data lake to a data mesh changes how companies deal with information. This shift needs everyone in the company to think differently about data. Instead of one team controlling everything, each area of the company should have its own data products. This means giving them the tools they need and making sure they feel responsible for it. It’s important to create a teamwork atmosphere where different parts of the company can share ideas and help each other out to make this new way work well.

 

Standardization and Governance

Data mesh is all about sharing data responsibility, but if everyone does their own thing entirely, it can turn into a mess. The key is finding a balance. Even though different departments manage their data, it all needs to work together. This means using the same definitions for things, keeping the data accurate, and deciding who gets to see what. Think of it like a team project – everyone has their part, but you still need clear rules and ways to share information, so everything fits together smoothly. Special tools called “APIs” can also help different departments exchange data easily.

 

Data Product Management

Data mesh brings new roles. Each department that uses data will need a “data product owner.” This person is the boss of their data, from figuring out what users need to make sure the data is always useful and accurate. To make this work well, companies need to train these data product owners and teach them how to manage data products. This way, everyone gets the data they need in a way that’s easy to understand.

 

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Whether you’re seeking exceptional professionals to enhance your teams or tech solutions to increase operational efficiency, Raso360 is your strategic partner. We’re dedicated to providing cost-effective, white-glove solutions and top-tier candidates to drive businesses forward.

Contact us today to learn more about how we can help.

 

References

  1. “The Four Principles of Data Mesh.” Thoughtworks, Accessed 19 April 2024, www.thoughtworks.com/en-us/about-us/events/webinars/core-principles-of-data-mesh.

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