Webinar Q&A

FEATURING FORRESTERWhy Building a Data Mesh Goes Beyond TechnologyDecisions—and How You Can Do It Right

We recently hosted a webinar entitled “Data Mesh: From Concept to Implementation”, with guests from Forrester and Lumen. We’recontinuing the conversation in this Q&A with Michele Goetz of Forrester.
As businesses take a hard look at getting the most value out of their data, the concepts of data products within a data mesharchitecture become essential. What is less clear, however, is how to get started on building a data mesh and how data pipelines,including streaming pipelines, can help teams produce, share and consume self-service data products. That’s all while ensuring theappropriate quality controls and security policies are applied to get the data from the right teams, to the right places, in the rightformat, at the right time.

Watch the full data mesh conversation

Michele Goetz, VP, Principal Analyst, Forrester
Michele Goetz serves enterprise architects, chief data officers, and business analysts trying tonavigate the complexities of data while running an insight-driven business. Her research covers
artificial intelligence technologies and consultancies, semantic technology, data management
strategy, data governance, and data integration.

1. What are some common challenges inbuilding a data mesh?

2. What role does data governance (or the lackthereof) play in building a data mesh?

The first hurdle organizations face is ownership. There are

When we apply data mesh principles to streaming real-time

considerations for the domain, the data products, and theuse cases, data governance becomes critical. Traditional

components and services. Decentralization, particularly in the lensdata governance approaches like data quality or masterof self-service and data governance, exacerbates the ownershipdata management can introduce significant latency or cause

challenge because business subject matter experts, analysts,

inconsistencies and conflicts in data pipelines and schemas. To

and data engineers all have different ownership stakes andavoid these issues, the business logic to comply with policies

responsibilities for the data products and their assets. The next

is maintained as a service that can be subscribed to within achallenge is how to define a data product. It can be as simple as

streaming pipeline. That way, data governance audits and rules

a data set or as complex as a machine learning model running

address data trust (data and schema quality) in a pipeline andon a streaming pipeline. Organizations need to define a value

across topics in context of data consumption. We can think

for the data product up front, to the point that the value canabout this as data governance on read (applied in motion) rather

monetize the data. For example, if the product is a data set, howthan data governance on write (applied to data at rest). Datais subscribing to that data set helping internal teams to make agovernance in this federated model provides more granulardecision on the creditworthiness of a new customer?

trust and control to meet the increased complexity of our digital© 2023 Confluent, Inc. | Confluent.io

distributed ecosystems.

1Forrester | Webinar Q&A

3. How do you get started with implementing adata mesh?
Start with a business process. Data mesh demands context, withthe business value and objective defined up front. A businessprocess provides that. If you are onboarding a new customer,
managing commerce transactions, or orchestrating a supply
chain, each of these processes help define what data is needed,when it is needed, and how quickly an insight or action needs to betaken. And importantly, the business process indicates the typeof data governance policies required and where and when to best
apply those policies. If organizations start with the data rather
than the process, they are constantly fishing for use cases, leaving
data platform investments to languish.

business subject matter expert (SME) counterparts. As businessSMEs design data-driven and intelligent digital experiences, theyshould work closely with technical teams to build prototypes andwireframes to create proof of concepts. Those can be refined
into production-ready data products faster and easier, sinceknowledge is shared in collaboration and technology teams areable to access available components to rapidly develop newapplications.

5. Are there any anecdotes you can shareor success stories you have heard fromorganizations that have successfully built
a data mesh?
A pharmacy created a digital experience to help schedule and4. Data mesh is more than just technology as
it involves people and processes. With that inmind, what should tech leaders going down thispath be aware of?

manage COVID testing and vaccinations. Their data mesh

Data products can be shared via real-time streaming pipelines,

be used as starting points. Taking advantage of existing online

a core data engineering responsibility. However, data value, datatrust, and data governance are defined by a business use case.
It is the line-of-business experts, developers, analysts and data
scientists, and data stewards who have the most knowledge andawareness of how data products need to function. Centralized
data engineering teams often do not have the right proximity to
the line of business for this context. Decentralizing data engineers
and moving them into line-of-business solution teams withapplication developers can alleviate this disconnect. In addition,

approach focused on this experience and customer journey todetermine the data domains and ownership, and explore existingdata artifacts (data sets, pipelines, policies, APIs, etc.) that could
pharmacy, scheduling, and flu vaccination capabilities allowed
the pharmacy to not only support and offer COVID testingand vaccinations quickly, but also integrate that with existingcustomer and patient accounts for a complete patient view.
While the initial platform for scheduling and vaccinations wasusing traditional data platform and integration capabilities, theinitiative for COVID allowed the company to modernize and moveto a streaming platform for real-time access and updates acrossall pharmacy and health services.

decentralization is a key principle of data mesh, ensuring thattechnical resources and experts are more closely aligned to their© 2023 Confluent, Inc. | Confluent.io2