A data silo is a store of data maintained by one division or team and isolated from the rest of the company. The phrase has agricultural origins and refers to the ideal circumstance in which grain and grass in a field “silo” are shielded from the weather.

Within a business unit, however, siloed data is far from desirable. It is frequently incompatible with other data sets, making it difficult for users in other parts of the organization to access it for insights. For instance, finance, administration, human resources, marketing teams, and other departments require precise information to execute their jobs. These departments typically store their data in distinct locations known as data or information silos. As the quantity and diversity of data assets increase, so do data silos.

Technical, organizational, and cultural factors can all contribute to data silos. They are common in large corporations because various business units can function independently and have their own goals, priorities, and IT budgets. However, without a well-planned data management strategy, any firm can end up with data silos.

The Problem with Data Silos

Data is healthy only when it is freely accessible and understood within your organization. If information is difficult to obtain and use promptly, or if it cannot be trusted, it does not offer value. A company that digitizes but does not break down data silos will not reap the full benefits of digital transformation. 

Organizations must provide decision-makers with a 360-degree perspective of data relevant to their analyses to become genuinely data-driven. Data silos can lead to a host of challenges:

Leads to flawed decision-making 

Incomplete and inconsistent data sets can result in poor decision-making. Since data silos prevent users from accessing data, corporate plans and decisions are not based on all accessible information. Data warehouses and lakes that integrate diverse data types for business intelligence (BI) and analytics applications might be derailed by silos.

In addition, data from one silo could be inconsistent with other data sets. For instance, a marketing department may arrange consumer data differently than others. Such inconsistencies can produce data quality, accuracy, and integrity challenges that affect both operational and analytic applications’ end users.

Gets in the way of productivity

Data silos drive up an organization’s IT costs by acquiring more servers and storage devices. In many cases, these additional procurements are also implemented and managed by departments rather than the organization’s data management team, leading to increased costs and inefficient use of IT resources.

Moreover, isolated data sets limit opportunities for data exchange and collaboration among users from multiple departments. Achieving business objectives or a shared vision is impossible without teamwork and open communication.

Teams working on a self-contained project may overlook critical data streams or mix up comparable data sets that should be kept separate. They may even be working with an outdated version of the same data. When teams only have access to a portion of the data, they may operate with inadequate information, which can limit efficiency and lead to duplication of effort since some of the information requested by one department may already exist in another. 

Causes data security and compliance issues

Businesses with siloed data find it challenging to establish a complete and effective data governance structure capable of protecting them from data breaches and cyber threats. Data silos also impede an organization’s capacity to detect occurrences that could potentially result in data privacy violations. Such businesses are highly vulnerable to noncompliance with data privacy requirements. 

Silos also make it more challenging to comply with data privacy and protection requirements. Individual users, for instance, may keep certain data in Excel spreadsheets or online business platforms like Google Drive. This data can create a security challenge, especially if accessed via mobile devices.

Provides a lackluster customer experience

47% of marketers believe that data silos are the key reason they don’t have a complete picture of the customer journey. By decreasing productivity and making it harder for enterprises to access relevant information, client satisfaction and customer experience suffer.

Throughout their buying journey, customers have multiple interactions with a particular company, including marketing and sales communications, website and social media visits, and support and billing discussions. When all this information is kept in separate silos, it can pose considerable hurdles and hinder marketing efforts.

How Valiance Can Help 

At Valiance, we have a simple approach to breaking down data silos.

Integrating data

The most obvious strategy to break down data silos is integrating them with other systems. The most common type of data integration is via extract, transform, and load (ETL), which involves extracting data from source systems, consolidating it, and loading it into a target system or application. Real-time integration, data virtualization, and extract, load, and transform (ETL) are other data integration approaches that may be employed against silos.

Centralizing data repositories

These repositories could be data warehouses or data lakes and contain massive amounts of data from many systems in the form of structured, unstructured, and semistructured data, which are utilized in data science applications. Structured transaction data is stored in data warehouses for BI, analytics, and reporting applications. These centralized repositories, when combined, provide a logical solution to silos.

Enterprise data management and governance

A good data governance program may directly minimize the number of data silos in an organization and promote shared data standards and norms. An enterprise data strategy better connects the data management process with business activities. This method will not only remove current data silos but also prevent the formation of new ones. A comprehensive data architecture design helps document data assets, maps data flows, and offers a roadmap for data platform deployments.

The right interface

Finding the right interface to allow employees to examine the organization’s data is also important. A change management project to transform an organization’s culture may also be required. Low-code, cloud-native technologies can also help since they can merge various data silos quickly and effectively via intelligent connectivity and automation services. Adding artificial intelligence (AI) and machine learning (ML) to automation speeds up the process by helping businesses identify patterns and build stronger automation that can adapt and change on the fly.

Firms can eliminate data silos and create safe, unified, and up-to-date data by utilizing intelligent automation, standardizing processes for data preparation and cleansing, and automating business tasks through workflows and user engagement mechanisms. Corporate management can then use the newly connected data to gain valuable insights from the firm’s whole digital environment.