What solution has emerged to address data access challenges in large organizations?
A data mesh has emerged as a possible solution to the challenges of data access plaguing many large organizations. This approach takes data out of stovepipes and puts it directly in the hands of business users, but in a controlled manner that maintains strong governance.
Who coined the term “data mesh” and when?
The term “data mesh” was coined by Zhamak Dehghani in 2019, when she was a principal at Thoughtworks. It caught on as a way of capturing the idea of distributed data access.
How does McKinsey define a data mesh?
McKinsey defines a data mesh as a data-management paradigm that organizes data in domains, treats it as a product, enables self-service access, and supports these activities with federated governance.
How does domain-based data management work in a data mesh?
Domain-based data management allows data to sit anywhere. Business teams own the data and are responsible for its quality, accessibility, and security.
What is the role of a self-serve data infrastructure in a data mesh?
A self-serve data infrastructure underlies the data mesh and acts as a central platform, providing a common place for business users to find and access data, regardless of where it is hosted.
How is governance managed in a data mesh, and what is the approach taken?
Governance is managed in a federated “hub-and-spoke” way. Under this approach, a small central team sets controls, and a supporting data infrastructure enforces them.
What advantages can a data mesh deliver when executed well?
Executed well, a data mesh can deliver powerful advantages: Speeding time to market for data-analytics applications, Unlocking self-service data access for business users.
How does a data mesh speed up time to market for data analytics applications?
Data products can react more responsively to data demand and provide business users with scalable access to high-quality data through the direct exchange between data producers and data consumers.
How does a data mesh unlock self-service data access for business users?
Domain-based structures reduce dependency on centrally located teams, putting insights within more immediate reach of business users and enabling them to get “skin in the game.”
How does a data mesh enhance data IQ?
Greater engagement with data builds learning, enabling business users to design increasingly sophisticated applications over time. By shaping the data and assets they use, business users ensure that what’s created is fit for purpose, driving greater return on investment.
How did a large mining organization benefit from implementing a data mesh?
After shifting to a data mesh, the company cut time spent on data-engineering activities dramatically and developed use cases seven times faster than before while also increasing data stability and reusability.
What does obtaining the full benefits of a data mesh require?
Obtaining the full benefits of a data mesh requires careful choreography. While domain-based architectures have attracted growing interest, the technological discussion often predominates, overshadowing other critical elements.
What challenges do businesses face when considering a data mesh?
Business users, for instance, may recognize that their current data-management systems are problematic but feel it’s better to stick with what is known than undergo the disruption of assuming direct ownership for data domains and products.
When is it better to move toward a central data platform instead of a data mesh?
Those that are in the middle of an enterprise resource planning (ERP) transformation or other large IT change might find it better to first move toward a central data platform and create a single logic on core data products.
What is the typical starting point for most organizations in implementing a data mesh?
Most organizations begin with a mix of centralized and localized data products that reflect their particular business, technology, capabilities, and go-to-market requirements.
Does a data mesh need to be constructed all at once?
The data mesh does not need to be constructed in one fell swoop. Many companies attain positive results by taking serial steps.
What capabilities are needed for data mesh success, and how can they be developed?
Executive and nontechnical business users will all need a basic level of data literacy for data mesh success. Coaching, hackathons, online programs, and analytics academies can all work well.
How can leaders keep the conversation going about a data mesh implementation?
Leaders make a point of regularly communicating with the organization, in large-scale town halls and intimate team meetings, on what the company is trying to achieve and what the road map looks like in terms of timing and capability building.
What is required to bring a data mesh from concept to reality?
But bringing a data mesh from concept to reality requires managing it as a business transformation, not a technological one.
Can a data mesh help large organizations manage data successfully?
A data mesh can help large organizations manage data successfully-if it’s understood that implementing one involves more than technology considerations.