Government Technology

What is Siloed Infrastructure?

What Are Data Silos?

A data silo occurs when data is stored in an isolated location inaccessible or unknown to others. For example, data might reside in a marketing platform and not be shared with sales support. For the Government, an example is data living in an online customer service solution not shared with a back-office permitting platform.

Government agencies and companies can get incomplete data views without a data infrastructure that eliminates data silos, leading to less-than-optimal business decisions.

Why Do Data Silos Exist?

Data silos exist as agencies and companies grow. Decentralized data, systems, and legacy platforms create data stored in different places and applications. In many cases, the silos occur naturally. Departments or business units often operate separately, have varying goals, and gather and store data to meet those goals. This data might get stored in one department’s database, on a separate server, or in cloud resources dedicated to business units.

Most legacy systems were not designed to share information easily. In many cases, the software could not integrate with other systems or use proprietary formats, making it difficult to share with other stakeholders.

The Cost of a Siloed Infrastructure

Organizations can pay a hefty price for a siloed infrastructure in multiple ways. Data accuracy is one of the biggest problems an information silo can impact teams.

Data Accuracy

When some data is locked away from users, the data others see can easily be inaccurate and result in flawed decision-making. There may be duplicate versions of the information that need to be in sync. For example, financial data in spreadsheets may get updated and stored in the department’s storage system, while older versions sent by email may no longer be current.

In addition, incorrect instances of the data still need to be corrected when users update only the data in their system. Not only can this impact the data quality, but it can undermine trust in data accuracy.

Department Efficiency

This data silo mentality can be more than just a technical problem. Team members may need to learn some data exists. If they do, they must remember its location, pull it from the source, and integrate it into their analysis. Siloed information hurts collaboration and efficiency. It’s inefficient and time-consuming.

Data silos can slow down the way Government operates. Often, data is no longer accurate when it's located, gathered, and incorporated into other systems.

Wasted or Over Burdened Resources

When the same information is stored unsynced in different places, it creates additional bloat. Multiple data warehouses or data centers, numerous best-of-breed solutions, or multiple similar solutions for different government departments translate to unneeded time and hands-on management of complex data infrastructures.

Costly Egress Fees

Today, more agencies use multi-cloud or hybrid cloud solutions and SaaS applications. These services help facilitate collaboration and make access easier for distributed work teams and remote employees. However, without a centralized data source, resource use continues to grow at a cost. A well-designed data architecture can be expensive, too.

Government agencies can also spend significant money pulling data from one area to use in another. For example, you may store data in Amazon Web Services (AWS) but process it in Microsoft Azure or Google Cloud Platform (GCP), or you may need to pull data from one region to another. Either way, you may be hit with significant egress fees.

Data Security and Compliance Concerns

When data is spread out, it makes security and compliance more difficult. Users may have data stored in spreadsheets on their computers or mobile devices. They may use multiple cloud services on non-authorized platforms.

Silos complicate privacy and security and may fall short of regulatory compliance requirements.

How to Get Out of a Siloed Infrastructure

The first step in solving any problem is recognizing that you have one. Then, it would help if you committed from the top of the organization to mitigate it. It is possible to solve with an executive-level buy-in because you would be crossing departmental boundaries and forcing change in how people work across an organization.

Open Lines of Communication

Once you commit to change, it’s essential to open up the lines of communication with team members. They must understand what needs to change and why it is necessary.

Clear and Transparent Processes

It would be best to be clear and transparent about each step's changes and process. You will be changing workflows, so everyone needs to know how they will be affected and what to do (or stop doing) to move forward.

Centralize Data

Data needs to be streamlined into a centralized source that is available to any authorized users who need it. A modern data architecture separates data and processing layers. Data is stored in a data lake or other centralized storage area accessible by any cloud platform or SaaS software and effectively integrated with the various solutions.
For data analysts, this allows them to process data in the best-fit cloud resource without incurring significant egress fees for moving data.

A centralized data store effectively eliminates data silos and duplicate data. Users will always see accurate and current data when one central version of the truth is updated.

In today’s demanding government services environment, a hybrid approach is typical, with one centralized source for core data and automated synchronization across other departmental point systems utilizing modern no-code integrations.

Establish a Data Governance Framework

A data governance framework ensures your data is available, usable, and secure. It would be helpful if you set up the guidelines, procedures, and processes needed to ensure users follow your framework and implement measures to ensure compliance.

Retire Legacy Systems

Especially in the beginning, you will likely need to monitor what team members are doing. People often fall back on old habits, so monitoring and reminders are necessary. Ultimately, you will need to retire legacy systems.

If you don’t retire legacy systems and processes at some point, team members may continue to use them. Set deadlines, give people notice, and then pull the plug.

Building a Future-Proof Data Architecture

Eliminating data silos is essential to making better business decisions and operating more efficiently. It takes a commitment to modernizing your data architecture and upgrading your systems and processes. The good news is that once you put the exemplary architecture, procedures, and policies in place, you will be in a great position to evolve as business conditions change. You’re essentially future-proofing your business.