The Data Silo Problem: Is Data Mesh the Solution?

Breaking Down the Walls

Every organization deals with data in one way or another—whether in a database, data warehouse, or other architecture type. With this data comes a management burden, as customer data must be protected in line with data regulations. IT teams struggle with data pipelines: controlling access to datasets across numerous products, services, and business applications.

Improper data governance and security configurations can prevent data access entirely and leave data in the wrong internal or external hands.

With data management being difficult enough, many businesses have additional problems with data silos. These data silos represent isolated data locations that other business entities will want to access. The data silo is a brick wall in this case, preventing these other business entities from accessing the data without convoluted data requests, data transfers, and data verification steps upon acquisition .

Let us explore data silo difficulties and how modern data mesh solutions can resolve such access hurdles.

Example of a Data Silo

Data silos are also referred to as information silos. Simply, this references any data source that is accessible by one team or business application, but difficult or impossible to access elsewhere.

One example of a data silo can be found in the sales department. Each employee tracks their client interactions, cold and warm leads, and revenues in a personal spreadsheet document. This is much like having different databases, each containing valuable information. At the end of each week, these spreadsheets are uploaded to the sales team’s shared folder and manually reviewed by the sales manager who can then update the weekly sales figures.

Imagine at the end of the week, three team members have duplicate clients in their spreadsheet. A warm lead in one employee’s spreadsheet has been confirmed as a sale without this information being shared with the wider team. Another sales representative contacted this confirmed customer attempting to sell them the same product or service they had just purchased. This causes customer annoyance and implies to the customer that the sales department is disorganized, leading them to cancel their purchase due to an erosion of trust. No amount of marketing can undo a bad customer interaction such as this.

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This is one example of how problematic data silos can be. They not only reduce awareness but can also lead to mistakes with customers and clients .

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Common Challenges Caused by Data Silos

A silo mentality introduces a number of challenges around data access, data analysis, utilization, and governance:

Data Silos Reduce Business Visibility

Data silos isolate information from the wider business. If an executive performs business intelligence or analytics workflows with a siloed dataset, these insights may be inaccurate and very likely inconsistent with different departments. Similarly, a product warehouse may not be aware of an unusually high number of incoming sales, leading to reactive ordering and surplus stock once sales normalize. Real-time access to a unified data source or ‘single source of truth’ (SSOT) would resolve these issues.

Data Silos Threaten Data Integrity

Across the data lifecycle, accuracy and consistency must be maintained. This is the concept of data integrity, which data silos threaten. The customer service team may have more regular contact with a customer, wherein they acquire the latest contact details. The finance team contacts this customer to pursue a subscription payment, only to hear an out-of-service phone number recording. The customer service and finance teams have inconsistent data, caused by data silos.

Data Silos Waste IT Resources and Expenditure

If every department has its own database, and every database contains ~75% identical data, this is one way data silos can waste IT resources. These siloed datasets could converge into a single database to reduce storage requirements, while still offering the same data points to each department. Similarly, if employees are exporting multiple copies of the same data to a storage location, this is a form of redundant data that wastes IT resources and raises operating expenditures (OpEx).

Silos Create Silos and Reduce Collaboration

A data silo can lead to the undesirable circumstance of cultural and organizational silos or cliques. These cultural and organizational silos then create a feedback loop that reinforces the need for data silos. Silos make data and knowledge sharing more difficult, leading to lower collaboration between different teams.

A Solution to Silos in Data Mesh Architecture

The solution to problematic data silos lies in modern data architectures like a data mesh.

A data mesh unifies silos by creating a community that connects those who wish to share data with those who wish to use data, in a secure and governed manner. This means that a data product could be created that integrates these four separate databases as one, using an invisible data control layer. This layer is a data mesh.

A data mesh allows for the same access controls, governance policies, and security rules to be applied everywhere data is accessed. All data requests and transmissions occur through this central layer, so defining rules once on this layer means all business systems are governed by the same policies .

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IT teams similarly struggle with responsibility and ownership of data. By creating data domains, this responsibility of ownership can be farmed-out or “federated” to those domains. This makes sense—considering the domain creates and uses its own data, it is also best equipped to manage and control this data thanks to greater familiarity with the source.

The end state with a data mesh is unified, federated governance with security and access rules, alongside devolved responsibility of data ownership to individual domains. A standardized method of access via data mesh leads to greater accessibility, fewer manual data requests, and greater data integrity. Data incompatibility can be resolved using data normalization, and ad-hoc analytics workflows can convert to real time thanks to a direct query architecture (DQA).

Adopt Data Mesh Architecture with Trianz Extrica

Extrica is a cloud-native, domain-oriented data mesh solution that allows businesses to have a conversation with all data, in any location, and with greater security and reliability, building a community, by connecting those who want to share data with those that want to use data.

Extrica can tap into any data, regardless of technology or location, utilizing a direct query architecture, increasing data availability whilst greatly reducing cost.

A core tenet of the solution is federated data ownership, where Extrica supports the creation of a data community. Here, technical and non-technical users alike can create and share data products, to uncover hidden opportunities and insights contained within the myriad of data across the enterprise. This involves data producers who own and share data, as well as data consumers who use this data in their day-to-day workflows.

Experience the Trianz Difference

Trianz enables digital transformations through effective strategies and excellence in execution. Collaborating with business and technology leaders, we help formulate and execute operational strategies to achieve intended business outcomes by bringing the best of consulting services, technology expertise, and execution models.

Powered by knowledge, research, and perspectives, we enable clients to transform their business ecosystems and achieve superior performance by leveraging infrastructure, cloud, analytics, digital and security paradigms. Reach out to get in touch or learn more. Reach out to get in touch or learn more.


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