Data Silos: Causes, Problems, and Solutions

Data silos are a concept that can apply to several distinct situations. At a basic level, data being in a silo simply means that information is trapped — it’s restricted to one department, system, or platform. This lack of data movement is a problem because it limits what the company can do with the content.

A data silo could manifest as one business unit lacking the software to access another’s raw information. Siloed data could also lead to searches that fail to yield results because the searcher, either knowingly or unknowingly, only has access to one of the company’s multiple servers, networks, or data lakes. If employees are struggling to combine information sources, they’re dealing with data silos.

Data silos can arise for several reasons, including:

• Incompatible technology: Today’s organizations have spent years building their tech stacks. This can lead to several generations of legacy systems that don’t always integrate effectively. So-called shadow IT makes this problem worse, with departments or individuals adding hardware or software that doesn’t fit in with the company’s overall IT strategy.

• Organizational structure complexity, spurred by growth: Different departments may have divergent data storage and use policies, locking down their proprietary information. This is especially common when organizations expand through acquisitions, adding employees and technologies that come from different environments.

• Company culture issues: In some cases, companies’ policies and norms can encourage data restrictions rather than sharing and collaboration. This could occur for legitimate reasons, like enforcing data governance and data security rules, or less positive ones, including data hoarding spurred by internal competition.

No matter why data silos arise within a company, it’s important to recognize and deal with them. Since the beginning of the big data era, data’s promise as a business tool has required it to be accessible and available. Using data to fuel analytics or, increasingly, generative AI (GenAI) is only viable if that data is as complete and free as possible.

Once companies compute the cost of siloed, fragmented, or inaccessible data, they realize the need to develop solutions to resolve the issue.

Some of the specific problems that come from data silos include:

• Incomplete insights: Analytics and business intelligence are major parts of decision workflows today. When analysis tools can only access a limited set of data, their resulting output will also be limited.

• Poor decision-making: Leaders in data-driven organizations apply the results of their analytics programs directly to strategic development. Choices made based on compromised data sets can lead to suboptimal strategies.

• Slower operations: Working with data silos isn’t just less strategically helpful than using full data sets; it’s also slower. Manually pulling data from various sources is far less efficient than process automation, but in heavily siloed environments, it can be the only viable option.

• Negative customer experience: Customer data is one of the richest and most valuable content sources for any company because it allows them to customize experiences. Incomplete data access degrades and weakens customer service capabilities and limits the marketing team’s effectiveness.

• Research and innovation difficulties: The development of new products and processes is powered by data. If researchers can’t access information on a subject, their progress may suffer.

• Missed opportunities: Lack of accessible data may mean forefeiting value. This goes beyond customer experience and research and includes real-time strategic opportunities. Companies without unified data sets may miss promising signals or detect warnings.

Data in silos loses a large amount of its value. While siloed data can still act as fuel for business operations, the process is slower, more labor-intensive, and leads to less satisfying outcomes.

Today’s companies keep multiple types of data, and it’s worth considering how data silos affect structured and unstructured information. These terms have specific meanings:

• Structured data: This content lives in utilities specifically designed for data storage, search, and recovery. Databases and customer relationship management (CRM) systems are examples of structured data resources.
• Unstructured data: This content exists in an original format that doesn’t fit neatly in databases. It includes raw document files, images, videos, audio recordings, and various other outputs.

In some cases, unstructured data production can form a new data silo merely by occurring. For example, a company that retains a recording after every video meeting is potentially producing a data silo. The content is difficult to access because it doesn’t exist within the standardized confines of a database, and it may go overlooked by searches and analysis.

Limited access to unstructured data is problematic because the content could have more business value if it were easily accessible. This squandered worth raises the importance of breaking down data silos, especially because so many organizations are accumulating large reserves of video, audio, and other multimedia content in the course of operations.

AI models do their best work when they have access to clean, accurate, and complete data. Companies that can’t make their information available to these systems and enforce data quality are at a disadvantage. This is especially concerning because of the breadth of use cases now associated with GenAI.

Businesses are using LLMs and LVMs to draw novel insights from their content and automate common workflows to empower their employees. There is a risk of a competitive gap opening up between firms that have ways to break down their data silos and those that can’t, based on the different outputs of their GenAI algorithms.

Making data available for use in GenAI models doesn’t necessarily mean imposing a structure on it. LVMs, for instance, are designed to draw insights from video content that hasn’t been converted to another format. It does, however, call for specialized tools and technologies.

Even at companies without a history of using AI utilities, thinking about how to turn data into fuel for analysis is likely to be a major discussion point soon, if it isn’t already. Data sharing is part of this overall conversation and a key piece of AI best practices.

After committing to breaking data out of silos, leaders have to find the best solutions for this purpose. It’s not always easy to accomplish this task. One of the most challenging aspects is that some seemingly effective data integration methods still come with drawbacks and limitations.

Problems with standard data silo remedies can include:

• Data lake limitations: Placing unstructured content in a centralized data lake is one of the most common ways to approach this data. Unless there are tools in place specifically designed to make the data accessible and usable, however, this method doesn’t automatically lead to unsiloed data availability.

• Centralized repository issues: Data repositories are another solution with drawbacks. Unless there are search methods, data can still end up siloed within these repositories. When content is divided by format, creator, or other traits, and there isn’t a universal way to find it, that data remains effectively siloed.

• The complexities of tool overload: When companies implement too many tools to make data accessible and useful, they inadvertently introduce a new layer of silos on top of their existing storage solutions. Fragmentation is one cause of data silos, and excessive data management systems perpetuate this problem.

• A lack of metadata and context: No matter how many solutions a company implements, it can be hard to use data unless there’s a quick way to surmise context. Creating metadata is the standard way to add contextual information, but many businesses lack automated metadata creation.

While these limitations don’t mean companies should give up on resolving their data silo issues, they do mean there’s a need for a new generation of best practices and solutions that can unite disparate structured and unstructured data resources and turn content into value.

Video content presents a perfect example of a data type that can be a powerful resource when released from silos, including the natural information silo that comes with its unstructured nature. By using a sophisticated video management platform, companies can achieve this feat of data integration without converting their videos to another format.

• The potential: Videos contain strategic information that companies can extract and use. Meeting recordings, customer interactions, and other automatically generated videos can shape companies’ strategies when analyzed for insights.
• The challenge: Traditionally, unless companies transcribe video and produce searchable text logs, the value remains locked away inside a natural data silo. Furthermore, visual information from on-screen action is even harder to extract and use with traditional technology.
• The context: Data from videos isn’t just important to the teams that initially produce and own them. If teams make information from video files accessible to analytics algorithms and GenAI systems, it can act as important context and help answer questions.
• The goal: Videos are a rich vein of knowledge that, if connected to a company’s overall strategy, can help keep that business on the right strategic path. The only challenge is to release that data from its information silo.

An advanced platform like Vbrick allows organizations to use their video content to its fullest potential. Analysis tools draw insights from videos without conversion and release those videos from their unstructured data silo. The additional context provided by this content can fuel informed decisions, large and small.

Better awareness of video content also enables better data hygiene, retaining videos for as long as they’re relevant and applying appropriate security and data governance measures to confidential or regulated information without tedious manual tagging. Video data doesn’t have to be a burden, but companies do need the right technology to unlock its potential.