Training and operating AI requires large amounts of high-quality data. Unfortunately, that data is often siled, meaning it’s not centralized, well-organized, or well-vetted.
Data silos are isolated information repositories that are isolated from each other and from departments and systems across the enterprise. Isolation typically means that data within one silo is not easily accessible to other systems or users outside of the directly related systems or departments. Think of data silos like this: islands of information.
Companies implementing AI regularly grapple with data stores that are isolated, fragmented, poorly integrated, poorly organized, and outdated. As a result, AI performs poorly, reduces accuracy, limits effectiveness, and reduces business value. Avoiding AI data silos must be a board priority.
Why are AI data silos a problem?
Data silos are not new to enterprise IT. However, AI is drawing new attention to them. The same isolation that prevents data sharing between traditional applications and departments also prevents AI platforms from accessing, training, and using siled data. Silos are a drawback for traditional business infrastructure, but they’re especially harmful for AI systems.
The power of AI lies in its ability to establish relationships, find patterns, identify trends, and enable accurate predictions and sound decisions. This basic functionality is based on relationships between different data sets, such as quarterly or annual marketing, sales, and manufacturing data. It is in the relationships between these different data sets that nuances and patterns emerge. If AI systems can’t access siled data, they won’t be able to see the big picture and make accurate analyzes and predictions.
Data silos can prevent AI from working properly, and the consequences can have a significant impact on your business. They include:
- AI accuracy or performance is limited. AI cannot make predictions or make decisions at the level that businesses need. For example, sales forecasts may be inaccurate or medical diagnoses may be wrong. This quickly leads to poor UX and reduced user engagement with the AI platform.
- Waste and operational inefficiency. Inaccurate or incomplete decisions and recommendations made by AI can lead to financial and material expenditures that do not generate revenue or improve business efficiency.
- Stagnant innovation. Missing data can cause AI systems to miss relationships and opportunities that could otherwise be found, creating a significant impediment to business innovation, as data silos prevent a complete picture of the situation.
- failure or abandonment of the AI Platform; In extreme cases, consistently poor UX can negatively impact an organization’s reputation. This may cause companies to stop using or devoting resources to maintaining the AI platform.
Causes of AI data silos
Given the sophistication and importance of AI in modern business, why do data silos still exist?The reasons are varied, but follow many traditional causes that are left unaddressed by a rapidly changing business and technology environment. Some of the most important causes of AI data silos include:
- Business leadership is unclear. Mastering technology is essential for modern business, but it’s never quick or easy. The path to technical success starts at the top with clear leadership and direction. Data silos thrive when there is a lack of AI vision or when leaders do not properly address AI projects before starting them.
- Technical issues. Companies often choose the path of least resistance to rapidly deploy tools, platforms, and systems, leading to interoperability and integration issues. When systems do not communicate well, data silos form, trapping data that cannot be easily accessed by AI projects.
- Differences in organizational goals. Managers often make technical decisions that suit their department’s needs, but do not prioritize broad interoperability with data from other business units. Manufacturing departments may collect and maintain data at fundamentally different details and formats than sales and marketing departments, without considering each other’s data practices. This quickly leads to technical issues that perpetuate data silos that are detrimental to AI efforts.
- Regulatory requirements. Different approaches to data governance, data quality, data retention, and regulatory compliance can result in different data management practices across business units. For example, data stores containing sensitive personally identifiable information (PII) may be separated from other systems by design or intent. Security needs often take precedence over data interoperability.
- business growth. As your business grows and changes and new technologies are introduced, data silos can arise. For example, a merger may force the acquired business unit to adopt the parent company’s tools and data standards, potentially siloing old data and making it difficult to access.
How to find AI data silos
Silos of AI data are difficult to spot. Each siled instance may work perfectly for related workflows, but problems arise when AI systems try to use multiple data sources together. These best practices can help AI teams identify data silos that can impact AI systems.
- Inventory your AI data sources. This typically includes a detailed audit of all applications and data sources in use, and correlates which departments or business units are using a particular application and corresponding data source. Knowing what’s out there is key to determining what an AI system can use and how easily it can access it. This often requires IT support. AI teams often discover overlooked enterprise applications and data stores.
- Find orphaned applications. Modern businesses use centralized data systems that are far more versatile and interoperable than local workspace data such as Excel spreadsheets or SQL databases. If local applications are used within business workflows, there will almost certainly be data silos, and it is highly unlikely that an AI system will be able to access that data.
- Look for duplicate data. Separated applications may use different copies of data that may exist in different formats in different business units. These duplicate data stores can quickly become out of sync, and content for the same entry or reference can result in different results. Duplicate data can confuse AI systems, so it’s important to reconcile duplicate data before it’s used by AI.
- Check your data metrics. It is common for different applications and proprietary data stores to generate similar business information. For example, sales and finance teams may use their own data stores and applications to calculate common metrics, such as quarterly marketing budgets. Adjust these data silos to avoid applications reporting different results for the same query.
- Identify data access delays. Multiple applications can access a centralized data source with little, if any, delay throughout the workflow. Data silos exist when data access is delayed because the data must first be requested, reformatted, or transferred between stores, especially if this operation is manual. AI cannot operate effectively when such delays occur, so they must be fixed before the AI can access the data.
- Consider system integration and compatibility. Data silos can arise from poor system integration and interoperability, such as critical legacy systems operating in parallel with cutting-edge enterprise platforms. These disconnects often create data silos that AI systems cannot use effectively. Business leaders may need to invest in new technology, such as updating legacy systems to use a common data source, before the data can be fully used by AI teams.
- Recognize data access restrictions. Some data stores may be intentionally inaccessible due to security measures that limit access to sensitive data or PII. These data stores may not be usable for AI projects without safeguards such as anonymizing the data or generating synthetic data. Proper access to restricted data requires careful collaboration between IT, business, AI, and governance teams.
Overcoming AI data silos
Finding silos of AI data is a challenge. Fixing these data silos to properly access AI is another matter entirely. Once AI data silos are identified, there are several strategies to fix them.
- Modernize your data storage technology. Data lakes and data warehouses can support centralized data storage for both structured and unstructured data. This provides a single repository that the AI platform can access and process. Other technology options include data fabric environments that use a virtual data management layer to create a unified data view and allow data to remain in its original storage location. Similarly, APIs allow different systems to communicate and exchange data without changing or migrating the information.
- Perform data storage inventory on a regular basis. Data inventory is not a one-time strategy. Doing these regularly will prevent new data silos from appearing over time. Regular inventory makes it easier to find, clean, and delete duplicate or outdated data. This helps maintain a high level of data quality for AI training and optimization.
- Use data management tools. Data management tools ensure data quality and keep it organized for AI training. For example, extract, transform, and load (ETL) tools can automatically handle data extraction, transformation, and loading to keep your AI data prepared and organized. As another example, data management tools can be used to establish and maintain shared data structures. This helps ensure consistent and well-structured data for AI training.
- Implement comprehensive data governance. Create, deploy, and regularly update data governance policies that apply across your business. This defines data ownership and scope of data responsibility. It also sets guidelines that define data access, modification, and sharing among users and business units. Balancing compliance and data interoperability for AI projects requires careful leadership and regulatory clarity.
TechTarget’s Senior Technology Editor, Stephen J. Bigelow, has more than 30 years of technical writing experience in the PC and technology industries.
