5 best practices for managing application growth and bridging data silos

Today’s organizations are drowning in applications. With hundreds and sometimes thousands of disconnected tools, data silos are trapping valuable information and isolating teams from the insights they need to drive the business forward.

With estimates of around 1 billion new logical applications being created worldwide by 2028, managing data effectively across disparate systems has become a pressing challenge. When critical information is locked away within individual applications, it becomes inaccessible to other teams that could benefit from it.

It is estimated that there are over 500 average dependencies per application today. New companies are entering the dev tool market at a CAGR of 14.6%. The growth of applications is fragmenting data across organizations, and organizations need actionable strategies to bridge these gaps effectively.

Organizations today are adopting more cloud, SaaS and custom-built applications to address their unique needs. What used to be a centralized IT environment has evolved into a vast ecosystem of tools spanning departments, regions and teams.

Research shows that organizations use an average of 130+ applications and for larger enterprises that number can exceed 1,000. This application growth adds complexity, making data management and accessibility a growing challenge.

With more applications comes an increase in complexity. Data silos are created when applications operate independently, preventing access to essential information and hindering collaboration. This fragmentation not only complicates data management but also hinders decision-making as valuable insights remain untapped.

In fact, according to a June 2023 blog post written by IDC, 33% of executives admit they often don’t get around to using the data they receive. This underscores the real cost of data silos: critical information is gathered but fails to drive action because it’s inaccessible, poorly integrated, or simply overwhelming to manage. Some challenges of this fragmentation include:

• Decentralization: As organizations shift from monolithic to microservices architectures, the number of independently operating applications grows. This decentralized ecosystem fragments data, complicating both management and accessibility.
• Shadow IT: Employees frequently adopt their own tools without IT oversight. This often results in unmanaged applications that don’t comply with organizational standards, leaving data isolated and unmonitored.
• Inefficiency: Teams waste time manually gathering data across systems and these manual processes introduce a human error factor which can cause inaccurate results.
• Poor decision-making: Incomplete data leads to missed insights, limiting strategic choices.
• Security risks: Siloed data makes it harder to enforce consistent security measures, increasing vulnerability.

Imagine a global retail company managing its inventory across multiple regional markets. Each region uses a different application to track stock, sales and customer orders. These systems aren’t globally connected and thus don’t communicate with one another, creating data silos. This company’s specific challenges include:

• Inefficiency: The operations team in the headquarters spends hours each day manually gathering and consolidating data from various regional applications. This delays critical decisions about which products need to be restocked or moved between markets.
• Poor decision-making: Without a unified view of the data, the company’s leadership makes incomplete or delayed decisions, leading to stockouts in some regions and overstocking in others. This mismanagement directly impacts revenue and customer satisfaction.
• Security risks: Different regions apply inconsistent security protocols due to using independent systems. This opens the door to potential data breaches, particularly in regions where standards are less stringent, creating vulnerabilities that can lead to non-compliance with international data protection laws.

Implementing these strategies will help organizations better manage the complexity of application growth while bridging data silos effectively.

1.    Centralize application management

Using a centralized management approach provides modern IT and business leaders a way to maintain efficiency, security and control across their application portfolio. This provides enterprises with a clear and structured view of applications, their usage and the data they manage to promote business agility. A centralized management approach includes:

• Application inventory: Create a comprehensive application registry that tracks ownership, dependency mapping, license management, usage and purpose for each application. This makes sure that all applications are accounted for and connected to strategic goals. With all the information in one central place, analytics can be run across your application portfolio to drive insight around business value alignment, performance benchmarking, cost allocation, usage metrics and trends.
• Real-time dashboards: Set up real-time dashboards for monitoring and alerting through an event-driven architecture that allows seamless data flow. This creates a single source of truth for all application metrics while providing enterprises with a method of tracking resource utilization, cost management and integrated performance. This gives control to stakeholders through insights they need to make data-driven decisions quickly.
• Standardized workflows: Automation of standardized workflows moves manual processes to efficient time and cost saving workflows. Keep all information in a centralized place by using automated deployment processes, approval flows, integrated CI/CD pipelines and a unified change management system.
• Integrated security management: Develop integrated security controls for centralized access management with unified security policies. This allows IT professionals to monitor security practices, create automated compliance checking and manage vulnerabilities. Enterprises can stay ahead and efficiently manage risk through compliance monitoring, audit trail maintenance and incident management to become proactive instead of reactive.

2.    Safeguard data interoperability across applications

Systems must communicate seamlessly to reduce data silos and improve accessibility. This allows enterprises to improve analytical results, drive strategic planning, increase operational efficiency and easily manage risk and compliance.  System communication solutions include:

• Application programming interfaces (APIs): APIs allow applications to exchange data directly, reducing isolation and manual data handling. Adopting an API-first approach helps manage application growth with greater flexibility in technology choices, reduced integration complexity, improved application interoperability and increased developer productivity.
• Standardized data model: Having a standardized data model reduces maintenance costs, data integration costs and improves efficiency across the organization by decreasing manual tasks. It also enables strategic growth through faster market expansion and new product development while accelerating innovation through data science and AI initiatives.
• Middleware or AI-powered solutions: Middleware and AI tools act as translation layers, standardizing and translating data formats across siloed applications to facilitate cross-system communication.

3.    Establish a data governance framework

A robust data governance strategy with organizational alignment and technical infrastructure maintains consistency and safeguard data quality across the enterprise. Elements of this strategy include:

• Data policy and standards: Establish organization-wide data standards to drive compatibility across systems including security, privacy, usage and access. Standardized data enhances data sharing, trust and accessibility through data consistency, completeness and accuracy to make sure data is reliable.  This allows organizations make accurate data-driven decisions that reduce errors and inefficiencies.
• Master data management (MDM): Use MDM to create a single source of truth, providing departments with consistent, trusted data. This provides the framework to increase data accuracy, reduce data redundancy, enhance data-driven insights and create consistent data across applications.
• Integration: Ensure alignment of the data governance framework across the portfolio with business strategy, risk management and IT governance. This allows for the reduction in duplicate workflows and promotes regulatory compliance with minimal cost.

4.    Leverage AI and automation

AI and automation can streamline data access, improving efficiency and cross-functional collaboration. Solutions include:

• AI-driven insights: AI can identify trends and patterns across applications, providing actionable insights and identifying hidden relationships that human teams might miss. This enables teams to be proactive by surfacing insights that improve operational efficiency and meet industry best practice across their application landscape.
• Automation: Automation reduces manual intervention in data collection and processing, saving valuable resources and ensuring timely access to information. Using AI to automate metadata management by classifying and suggesting connections between siloed data, enterprises can track data lineage to identify usage, dependencies and origin to make data more discoverable and accessible.
• Real-time data synchronization: To reduce latency while accessing data across systems and tools, integrate AI to detect and prioritize changes between data and applications. This process makes sure that critical updates propagate first. Real-time data analysis and synchronization improve business agility by enabling teams to react quickly to changes with decisions based on the most current data across the enterprise.
• Data federation: Leave data at the source by creating a data virtualization layer without physically moving the data. Enable AI to intelligently cache frequently accessed data and optimize query performance. This provides a unified view of the data sources and reduces latency and costs associated with traditional data consolidation.
• AI integration: Bridge the knowledge gap by using natural language processing and generative AI. This enables non-technical employees to ask questions and retrieve actionable insights to make informed, data-driven decisions. Use AI integration to build a data culture that connects data silos between systems and generates real-time data mapping, automated metadata management, self-service analytics, governance and compliance regulation requirements. This integration allows organizations to unlock key opportunities for growth and reduce manual bottlenecks to improve decision making and stay competitive.

5.    Foster a culture of collaboration

While technical solutions are essential, a collaborative culture is critical for breaking down silos while managing application growth. Building a culture of collaboration combines technology, people and processes to improve data quality, data connectivity and data-driven strategic planning. Increase collaboration with:

• Cross-functional teams: Encourage teams from different departments to work together on data-related projects, fostering a shared approach to information and innovation. Make business insights accessible to all stakeholders and encourage cross-functional alignment to break down both data and organizational silos.
• Transparent data sharing: Leadership should promote open communication between IT and business teams, ensuring that data flows freely and that everyone has access to the information they need.

While best practices can help mitigate the challenges of application growth and data silos, organizations need advanced tools to manage this complexity at scale. IBM® Concert® provides a centralized view of applications, including performance and health.

It also maps dependencies and consolidates insights on vulnerabilities, compliance and certificates. With AI-driven insights, workflows, real-time custom dashboards, automated remediation and customizable metrics to measure posture against best practice, IBM Concert enables organizations to streamline operations, boost security and drive effective collaboration across departments.

As application ecosystems expand, so do the challenges of managing data silos and application sprawl. By implementing these best practices and leveraging innovative tools like IBM Concert, organizations can foster a more integrated, efficient and secure environment.

Eliminate data silos and build a more connected future with a free 30-day trial of IBM Concert