Understanding Siloed Data: Challenges and the Solutions

In today’s data-driven business landscape, the ability to effectively manage and leverage data is critical for organizational success. However, one of the biggest obstacles standing in the way of effective data utilization is the presence of data silos.

Data silos occur when data is isolated in separate repositories controlled by different departments or business units, making it difficult to access and integrate data across the organization. This siloed nature of data leads to a host of challenges that can hinder operational efficiency, decision making, and ultimately business growth.

In this article, we will dive deep into the concept of data silos, exploring what they are, why they occur, and the negative impact they can have on organizations. We will also discuss strategies for identifying data silos and best practices for breaking them down to enable free flow and effective utilization of data across the enterprise.

A data silo is a collection of data held by one group that is not easily or fully accessible by other groups within the same organization. The data in a silo is typically stored in a standalone system and is often incompatible with other data sets, making it difficult to integrate and gain a unified view.

Some common examples of data silos in business contexts include:

• The marketing department using a separate CRM system from the sales team
• The finance department maintaining their own data mart separate from operational systems
• Legacy systems that are not integrated with newer cloud applications
• Individual employees storing data on local machines or personal cloud drives

The concept of silos dates back to the early days of computing when different systems were purpose-built for specific functions and did not easily interconnect. However, despite advancements in technology, the data silo problem persists in many organizations today.

In fact, according to a survey by Forrester, a staggering 72% of organizations say their data exists in disparate silos. And a report by Productiv found that the average enterprise uses over 200 SaaS applications across various departments, many of which are not integrated, further exacerbating the data silo issue.

There are several key factors that contribute to the creation and persistence of data silos within organizations:

One of the primary causes is organizational structure and politics. Different departments like finance, marketing, sales etc. each have their own objectives, budgets and preferred tools and systems. This departmental autonomy can naturally lead to siloed data.

For example, the finance department may have strict data privacy controls and maintain their own database instances, while marketing adopts the latest cloud CRM for campaign management. These choices are made within departmental confines without a holistic data strategy in place.

Company culture also plays a major role in enabling or discouraging data silos. A culture lacking strong central leadership for data and an “everyone for themselves” mentality will be more prone to silos.

For example, some companies may not prioritize a data-driven culture or see the value in cross-departmental data sharing. Cultural resistance to change and desire to control data within one’s purview can stand in the way of data unification efforts.

Finally, the technologies in use can inherently lead to data silos. Legacy on-premises systems tend to be harder to integrate than modern cloud platforms. The increasing adoption of SaaS applications, while providing flexibility to individual teams, can create integration challenges when dozens or hundreds of disparate apps are in use across the organization.

For example, a company’s core ERP may be an on-prem Oracle system while the HR team uses Workday and Sales uses Salesforce – each a best-in-class solution but not inherently integrated with each other, creating silos of HR, financial and customer data.

The siloed nature of data within organizations is far from just an IT concern. It has real and significant negative impacts on the overall business:

When the same or related data resides in multiple disparate systems, it almost inevitably leads to data quality issues. Data may be entered or updated in one system but not others, leading to out-of-date or inconsistent information across silos.

For example, a customer may notify the support team of an address change, but if the billing system is not updated, invoices will be sent to the wrong location. Or the sales team’s quarterly numbers in their CRM may not match the official numbers reported by finance. These data integrity issues can erode trust in data and lead to misinformed decisions.

Operational inefficiencies abound when data is fractured across the organization. Teams waste time and effort manually gathering, aggregating and reconciling data from various sources to get a complete picture.

For instance, the marketing team may need to pull together customer lists from the CRM, marketing automation platform, customer support system and sales records to build a complete view for segmentation, an onerous task when data is not integrated.

There are also very real financial implications and costs associated with data silos in terms of resources and missed opportunities.

First, siloed systems often lead to duplication – the same data being stored in multiple places and re-entered by different teams, increasing storage costs and maintenance efforts. Redundant systems, excess software licenses etc. across the company can add up.

But even more impactful is the opportunity cost. When data is hard to access and blend for analysis, it’s difficult to uncover crucial business insights in a timely manner. Decisions may be delayed or made based on partial information.

According to IDC, companies lose over $4.5 million annually due to data quality and access challenges. And a study found that 67% of businesses are missing opportunities because they can’t identify the right data for analysis.

With an understanding of what data silos are and their potential impact, let’s look at how you can identify if your organization is struggling with this issue. Some telltale signs include:

• Lack of a complete customer view: If gaining a 360-degree view of your customers and their journeys is excessively difficult, requiring piecing together data from many sources, you likely have customer data silos.
• Inconsistent reporting: If different departments regularly report varying numbers for the same KPIs, or can’t provide real-time updates, that’s a red flag that data is siloed.
• Duplication of efforts: If teams are spending time entering the same data into multiple systems or undertaking the same analyses separately, that’s a clear sign of silos.
• Reliance on spreadsheets for data consolidation: While spreadsheets have their place, if multiple versions are floating around and being used as a primary method for piecing together data from different sources, that indicates an underlying data silo problem.

• Data mapping: Documenting all the data sources, types, and flows within your organization is a crucial first step to uncovering silos. Data mapping provides a clear picture of your data landscape.
• Audits and assessments: Conducting systematic audits of your data and systems, and objectively assessing integration and accessibility, can shine a light on silos. Evaluate factors like data completeness, consistency, and ease of reporting.
• Stakeholder interviews: Discussions with data users across various teams to understand their challenges and where they spend undue time wrangling data can surface silos and inefficiencies ripe for improvement.

Once you’ve identified data silos in your organization, it’s time to take action to break them down. Let’s look at some key strategies:

Eliminating data silos requires change, and change starts with people and culture. Foster executive alignment around the importance of data sharing and accessibility. Communicate the benefits of a data-driven culture and the downsides of silos.

Initiate training and enablement programs to promote data and analytics best practices. For example, implementing self-service analytics capabilities with proper governance can empower teams to access the data they need while maintaining security and consistency.

Invest in a modern data architecture that enables centralized data management. A unified data platform, such as a cloud data warehouse or data lake, can serve as the single source of truth, consolidating data from disparate systems for analysis.

For example, solutions like Snowflake, Redshift, support ingestion from hundreds of structured and unstructured sources and enable secure, governed access for reporting and analytics. To break down these silos and protect sensitive data, organizations should explore strategies like data flow mapping to improve security. Data flow mapping provides a visual overview of how information traverses systems, identifying key integration points and potential vulnerabilities.

Adopt proven data integration techniques to break silos. ETL (extract, transform, load) processes can pull data from source systems, transform it into a standardized format, and load it into a target platform for centralized access. Modern ETL and data integration platforms like Hevo Data provide user-friendly, low-code interfaces to map data flows across hundreds of sources and automate integration. APIs and ESBs (enterprise service bus) can also enable real-time sharing between applications.

Data silos often arise in the absence of enterprise data governance. Establish a robust governance framework with clear policies, processes, and responsibilities for data management. This includes everything from data quality rules and master data management to access controls and usage policies. Proper governance ensures consistency and security as you break down barriers between datasets.

Let’s examine how some real-world organizations have tackled their data silo challenges:

Covanta, a waste management company, previously had valuable operational data spread across numerous systems – asset management, invoicing, CRM etc. This made it impossible to get a complete view of the full operational lifecycle to identify efficiencies and model profitability.

Covanta was able to centralize petabytes of data for advanced analytics. They reduced the time to generate operational reports from 8 hours to just minutes and uncovered opportunities to optimize maintenance activities by 10%.

Home improvement giant Lowe‘s struggled with fractured customer and sales data. Terabytes of data from e-commerce, POS, marketing, supply chain etc. resided in silos.

By building a unified data lake on Google BigQuery and using Informatica’s iPaaS and data integration platform, Lowe’s was able to blend data across domains for a complete omnichannel view of customers and operations. This enabled optimized merchandising, targeted marketing, enhanced forecasting and more.

UnitedHealth, a Fortune 10 healthcare and insurance provider, had valuable patient data locked in legacy platforms across its many subsidiaries, making it difficult to deliver connected care.

They leveraged Talend, Azure Data Factory and Azure Data Lake to create a unified data architecture integrating claims, clinical, pharmacy and lab data. This provided a 360-degree patient view to improve care coordination and population health initiatives. Fraud detection efforts were also accelerated by 3X.

These examples demonstrate how unifying data through modern platforms and integration can drive transformative results by eliminating silos.

Breaking down data silos is one of the most critical challenges facing data-driven organizations today. The ability to effectively integrate and leverage data across the enterprise is essential for remaining competitive.

Throughout this article, we’ve explored the concept of data silos – what they are, why they occur, and their damaging impacts on everything from operational efficiency to customer experience to the bottom line. Recognizing the warning signs is the first step.

We’ve also examined proven strategies for eliminating silos and liberating data, from change management and governance to modern architectures and integration techniques. Real-world success stories show the transformative power of these approaches.

The future of data management will belong to organizations that can harness the power of all their data to drive insights and innovation. Overcoming data silos is foundational to this aim.

If you’re looking to break free from the constraints of siloed data, Tooljet can help. With powerful no-code data integration capabilities, Tooljet enables you to easily connect data from all your sources to build modern, composable applications.

So why remain constrained by fragmented data assets? Start your journey toward a unified, insight-rich data future today. Get started with Tooljet.