How Data Silos Hurt Business Performance ─ And What to Do About It
Businesses rely heavily on data to make informed decisions, drive innovation, and stay competitive in the market. In fact, data volumes are expected to increase by more than 10 times from 2020 to 2030, according to McKinsey. However, many organisations still struggle with fragmented data.
Storing data on different applications or platforms creates data silos in organisations, which significantly impedes operational efficiency by restricting information discovery and access to all stakeholders in the organisation. According to Salesforce’s research, 81% of IT leaders believe that data silos are obstructing their digital transformation initiatives. With generative AI (GenAI) becoming a huge business enabler, enterprises face an increase in data volumes, making data management more complex. This premise further increases digital transformation challenges as 62% indicate that their data systems are not optimised to fully utilise AI.
To help organisations in their data strategy, this blog post will explore the impact of data silos on businesses and provide strategies to solve this challenge.
The Impact of Data Silos on Businesses
The proliferation of applications and data in organisations has led to significant fragmentation, complicating the process of data retrieval and analysis.
As organisations increasingly rely on a multitude of apps, the challenge of managing and integrating data from these disparate sources becomes more pronounced. Here are ways data silo negatively affects organisations:
Poor Data Quality Erodes Trust in Data
Data silos lead to discrepancies and errors in data, making it difficult to trust the accuracy of the information. In fact, incomplete (71%), inconsistent (67%), and inaccurate (55%) data are the top data quality issues organisations face today, according to McKinsey. More alarmingly, 62% of organisations do not have well-defined processes for centralising new and existing data sources. For example, having different sources of documents can result in stakeholders not having access to the most recent version of the file, causing misinformed decisions and hindering effective business operations.
Additionally, data repositories should be cleaned and updated not just for data discrepancies. In this AI era, businesses must have a solid foundation of information to work with. Without reliable, relevant, and centralised data, AI will fail to create accurate analyses or pull up-to-date information from your organisation’s database. If data is siloed across different platforms, AI will only work to gather information stored in the specific platform it is running on, leaving behind data stored elsewhere.
AvePoint CEO Dr. Tianyi Jiang (TJ) explains:
"The most critical part of any AI strategy is first ensuring a strong data foundation. AI and machine learning models are heavily dependent on quality, well-managed data to produce accurate insights and effective automation. This makes a thoughtful data strategy and information management capabilities essential prerequisites before implementing AI.”
This highlights the importance of having a centralised infrastructure before implementing AI for better productivity.
Slower, Less Effective Decision-Making
Data fragmentation hinders the ability to quickly find, gather, and analyse information, leading to slower decision-making processes. According to Mulesoft’s 2025 Connectivity Report, users have an average of 897 apps, with 45% reporting using over 1,000 apps.
Having data stored in multiple apps makes finding data and using it for analysis or collaboration more difficult. For example, if sales data isn't accessible to the marketing team, creating a cohesive campaign becomes unfeasible. Say, marketing data reveals a surge in interest from a specific demographic, but absent a collaborative digital working space, the sales team may miss the opportunity to effectively target that demographic.
This lack of agility can prevent timely responses to market changes and business opportunities. When critical information is locked away in different systems, decision-makers must either proceed with incomplete data or delay decisions while waiting for information to be manually compiled and shared. In fast-moving markets, this delay can be the difference between capitalizing on an emerging trend and missing it entirely.
The decision-making process becomes further complicated when different departments base their analyses on disparate data sets. Without unified information, cross-functional teams may reach contradictory conclusions, leading to misalignment in strategic direction. This creates additional friction in the decision-making process as teams must first reconcile these differences before moving forward.
Increased Operational Costs and Reduced Productivity
Maintaining separate systems and processes for different data repositories can be costly. In fact, Flexera’s 2024 State of the Cloud report revealed that 26% of enterprises spend over $12 million on software-as-a-service (SaaS) applications.
When data exists in separate repositories, organizations often pay for redundant storage solutions and computing resources that could be consolidated for significant savings. According to the AI and Information Management Report, 41% of organisations manage at least 500 PB of data. If redundant, obsolete, and trivial (ROT) data continues to be stored in organisations’ digital environments, data volumes can further increase, leading to increased storage costs.
This financial burden extends beyond direct storage expenses to include the hidden costs of managing fragmented systems. Organisations must allocate substantial resources to maintain multiple platforms, each requiring dedicated IT support, regular updates, security patches, and specialized administrative personnel. The complexity of managing these disparate systems often necessitates larger IT teams than would be required with a unified data infrastructure, further inflating operational budgets.
Strategies to Eliminate Data Silos
As we've explored, these silos not only slow decision processes and increase operational costs but fundamentally undermine an organization's ability to leverage its most asset: its collective intelligence.
Fortunately, strategic approaches to data management and integration can break down these barriers. The following strategies provide a practical roadmap for organisations looking to transform their fragmented data landscape into a cohesive, accessible ecosystem that drives innovation and competitive advantage:
Assess Data Quality
To effectively break down departmental silos, start by identifying where data currently resides. Determine how many platforms the organisation uses and whether they are on-premises, in the cloud, or both. To understand this, assess the volume of data stored on these platforms, how data is collected and stored, and its relevance and accuracy.
To ensure data quality, organisations must first organise data by removing ROT data across these platforms. Once the data is gathered and audited, engage your teams to identify daily challenges with the existing data management systems.
Use a Centralised Platform
To address data fragmentation, organisations need to transition to a centralised cloud-based platform to integrate data across various repositories and systems. Cloud-based collaboration tools, such as Microsoft 365 and Google Workspace, allow real-time co-authoring, version control, and commenting, helping teams work together seamlessly.
Establishing a single source of truth for data ensures consistency and reliability. It ensures everyone has access to the most up-to-date files, making it easier to work together efficiently. Additionally, the platform provides detailed information on document ownership and tracks updates, enhancing transparency and accountability within the team. This not only streamlines workflows but also fosters a more organised and productive work environment.
Educate the Workforce
Implementing comprehensive training for the effective use of new platforms is necessary for maximising their capabilities. Well-trained employees can efficiently access, interpret, and share data, significantly reducing the risk of data being isolated in silos.
Develop structured training programs that encompass all aspects of the new system. These programs should include hands-on workshops, online tutorials, and regular Q&A sessions to ensure employees are proficient and confident in using the new tools.
Similarly, promoting data literacy within the organization is essential. Employees must understand not only how to use the new platform but also the importance of data quality and consistency. Data literacy programs can help employees appreciate the value of accurate and reliable data, which is vital for preventing discrepancies that often lead to data silos.
Stay Ahead of the Curve with AvePoint
Fostering a data-driven culture starts at the top, with leaders prioritising data in their strategic decisions and setting a precedent for the entire organisation. This approach encourages a data-centric mindset across all departments, promoting transparency and aligning departmental goals with the organisation’s overall data strategy.
AvePoint offers comprehensive migration services designed to simplify and accelerate transitions, whether from on-premises to cloud or cloud to cloud.
Our migration service helps organisations eliminate silos by transitioning into a unified, cohesive platform. This process involves a pre-migration discovery, helping organisations understand the amount and types of data stored in the digital environment.
AvePoint provides a full-service approach, allowing organisations to:
- Migrate data based on need, whether at full scale or ad hoc.
- Define the filter policy, method, and schedule.
- Maintain permissions, version histories, and sensitivity labels.
Through our migration service, AvePoint will handle all migration complexities with best-in-class technology, along with a comprehensive and secure process.
By leveraging our migration services, organisations can ensure a seamless transition to a centralised platform, allowing for a more efficient and collaborative digital environment.
Mayella is a Content Marketing Specialist at AvePoint, focusing on digital transformation, AI confidence, and education technology. With a background in content production and communications for B2B and e-commerce, she excels at helping businesses transform their digital workspace into a dynamic and efficient environment.
