5 Tips for a Successful Data Mesh Implementation

Executive Blog
Written by Vishal Patel, Chief Data Officer & Head of Data Engineering, Webster Bank

Vishal Patel

Chief Data Officer & Head of Data Engineering

Webster Bank

NOVEMBER 22, 2022

Data mesh is a trending topic among Chief Data Officers, and many are contemplating whether it is the right approach for their future data architecture needs. It may be a good option for data leaders who are looking to simplify data accessibility within their decentralized governance frameworks, and Evanta’s CDAO communities are gathering across the globe to discuss the risks, benefits and strategies for implementation

In this Executive Blog, Vishal Patel, Chief Data Officer & Head of Data Engineering at Webster Bank and Co-Chair of the New York CDAO Community, shares insight into the value of data mesh and how data leaders can utilize it to promote self-service capabilities throughout organizations:

One of the key aspects of a CDAO’s responsibility is data governance, which treats data as a business asset to be owned and managed by the user community. When implemented properly, organizations derive invaluable benefits from self-service data models, notably:

  1. Increased adoption of fact-based decision-making using data at hand
  2. Drastic improvement in “Time to Market” 
  3. Improved operational efficiency due to the reduced use of costly technology resources

Data mesh empowers this model. It allows user communities to take charge of the data available to them for critical business decision making, and it gives them the authority to build appropriate data products. Organizations can then reuse these data products for numerous business use cases to mitigate potential risks or drive business growth. For example, Customer 360 data products can be leveraged for marketing purposes for business growth strategy, as well as for supportive use cases to improve customer experience.

The advantages of the data mesh approach can be great, but as with any shift in data management, various elements need to be in place to achieve optimal results. Here are five tips for implementing data mesh and avoiding pitfalls:

  1. Data fabric is foundational for a successful data mesh model: It is important to have a sound, centralized architecture that would allow the exposure of data from a variety of sources with appropriate metadata via a simple view of the data catalog.
  2. Continuous data governance: As data from multiple Systems of Record “SoR” are exposed to the user community via data fabric architecture, it’s critically important to have a balanced data governance that manages ownership of the data, data products and more importantly, the quality of data that makes it “fit for use” to build business, domain-based data products.
  3. Well-defined operating model for data access entitlements: Having a governed way to manage data entitlements at a data fabric level prevents business functions from accessing data outside of their specific use cases - based on the organization’s policies. This avoids breaching data privacy concerns.
  4. Continuous improvement in data literacy maturity: Data literacy is one of the key challenges that data leaders face when it comes to the success of a data mesh implementation. Reduced data literacy can easily lead to siloed data products, siloed data delivery channels and increased technical debt that comes with additional costs. It is important to ensure that there is continuous learning via appropriate training and that data users are regularly encouraged to reuse existing data products to avoid data product silos and data duplication.
  5. Rich data visualization capabilities. Having a “Data Showroom” that displays data products created using data mesh provides users with enhanced data capabilities to drill down into the data for more actionable insights. It also allows the user community to share data products and promote self-service capabilities within an organization.

Data mesh by itself may not be a magic bullet that will solve overall end user data problems or data needs. Having a multi-layer approach with data fabric, coupled with a sustainable operating model for data governance and data literacy would be key for data democratization. It will be up to organizations to define a balanced approach in line with their risk appetite and level of benefits derived with the chosen balanced approach.

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