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You’re a top-brass general and your squads are on the battlefield. Each has its own mission and territory but they cannot communicate with each other. They can’t collaborate or share intelligence, enemy positions, or effective tactics. In fact, they may actively work against each other without even knowing it.
Organizations face similar challenges when they rely on data isolated in silos. Data silos create a fragmented environment where departments or teams work in isolation. They operate with limited visibility, lack access to critical information, and find themselves unable to integrate resources, operate efficiently, or optimize their decision-making.
Cheesy as it might sound, to succeed in the battle for data-driven insights, organizations must break down these silos and adopt unified data management practices.
What Is a Data Silo?
A data silo happens when a company stores and manages data in a system or database that lacks integration with other systems. The systems may be incompatible or inaccessible to one another. Think of a data silo as a physical file cabinet that locks, and only one person in an entire organization has the key. It’s full of useful data, but no one can make use of it.
In reality, even this analogy fails to reveal the situation’s complexity. Sales data from the CRM may not be compatible with the data the finance team collects. Finance doesn’t know what special deals sales has arranged, sales doesn’t know if a client is regularly delinquent on their accounts, and communication becomes a mess. Whereas unlocking a physical file cabinet can remedy this situation, making data within a data silo more accessible only remedies half the problem.
In order to address the challenges posed by incompatible sales data and the communication issues between the sales and finance teams, it is important to understand the two kinds of data silos: organizational and technical. Organizational silos occur when departments use different systems and don’t share their data, as in the sales-finance example above. Technical silos occur when a company uses multiple programs to store their data and can’t access it through a single interface.
A technical silo occurs when a product team tracks product usage and the IT team tracks product bugs using separate systems. Neither team can access the required data from a single source. Instead, they must rely on the business intelligence team to parse the disparate data sets. Then analysts and engineers get pulled in and everyone spends a lot of time and money putting together kludge-y solutions that don’t make meaningful improvements.
Why Data Silos Are Common
Data silos often emerge naturally and unintentionally. Many organizations allow different departments to prioritize and manage their data independently. Departments don’t communicate, whether through oversight, strategic desire to control their own data, or just keeping tabs on their own work.
Outdated legacy systems that lack interoperability can worsen the problem, as integrating data with newer systems is, at best, a challenge. For instance, a banking system developed decades ago may store customer data in a .dbf file format. Since the format is specific to dBASE, an older database software, it will not be interoperable with any newer CRM system. To fix it would require an additional system that can extract data from the .dbf file, transform it into another format, and send it elsewhere.
However, data silos can also arise when organizations restrict access and sharing, whether for concern of security, compliance, or data governance and ownership practices. This can also happen organically when networks are logically separated and aren’t built for data sharing. These restrictions frequently have a cumulative impact that puts a freeze on collaboration. That’s why it is critical that organizations find a balance between data security and the need for collaboration and data-driven decision making.
Why Are Data Silos Problematic?
Data silos aren’t necessarily a problem. Some companies can operate just fine with data silos, but this is more the exception than the rule. Silos become a problem when teams need to start making decisions or have awareness based on someone else’s data.
Inefficient Data Analysis
If you work in an organization where communication is limited, you may notice workers from different teams unknowingly duplicating tasks. This occurrence is especially frequent in data analysis, where data silos make such duplication common. Not only are these teams wasting their efforts, they’re also failing to benefit from each other’s expertise.
Inefficient data analysis can occur even among departments with related missions. The marketing team may only have access to customer data from the marketing database and online sales. Meanwhile, the sales team has access to physical store transaction data but no visibility into online sales data. As a result, the marketing team can only examine a subset of customer behavior by focusing solely on online interactions.
Incomplete data sets are like puzzles with missing pieces—they’re inaccurate, inconsistent, and perplexing. Such data sets can create a ripple effect and cause even more inaccuracies if they clash with other departments that have inconsistent data. Imagine each department using their own unique way of organizing customer data: one alphabetically by client contact name, the other alphabetically by client company name. This lack of synchronization can lead to significant problems with data quality.
In particular, this lack of synchronization can lead to data duplication (“123 Main St.” is listed under different names like “123 Main Street”and “John Smith 123 Main St.” in different departments’ systems). This lack of synchronization can also compromise data integrity and affect the overall reliability of the customer database.
Inconsistent formatting can cause difficulties when analyzing and comparing customer data. In the example above, the inconsistency between the addresses can lead to data fragmentation. That would make it difficult to generate reliable reports or gain a comprehensive understanding of customer demographics or geographic distribution. Ultimately, data silos can make it challenging to have a unified and accurate understanding of everything from customer information to financial situation.
Data silos limit information sharing and collaboration. Lack of access to relevant data makes it challenging for departments to achieve a common goal. As a result, teams can become narrowly focused within their own domains, restricting their ability to tap into different perspectives and develop innovative solutions.
Such was the case with U.S. intelligence agencies before the 9/11 terrorist attacks. In the years leading up to the attacks, the CIA, FBI, and NSA possessed valuable pieces of information that, if shared and connected, could have potentially uncovered the plot. However, the relevant data remained compartmentalized within each agency. There was a failure to connect the dots and see the bigger picture, ultimately hindering the ability to detect and prevent terrorist attacks.
Higher IT Costs
Data silos can significantly impact IT costs by requiring the purchase of additional servers and storage devices. It gets even more expensive when individual departments independently set up and manage these systems rather than establishing a dedicated data management team.
Organizations have partly remedied this situation with enterprise resource planning (ERP) systems. Before ERP systems, they frequently relied on separate software applications for different departments, leading to data silos and increased IT costs. These disparate systems required additional hardware infrastructure, software licenses, and maintenance efforts, resulting in higher expenses for the organization. ERPs counteract this situation by providing a centralized and integrated software solution that eliminates the need for separate applications for different departments.
Higher Security Risks
Data silos can elevate security risks for an organization in various ways. First, storing data in isolated and fragmented systems makes it more difficult to implement consistent security measures across an organization. Each data silo may have its own security protocols, making maintaining a unified and robust security framework challenging.
Data silos also make it easier to lose visibility and control over sensitive data. Without a centralized view, it becomes more difficult to monitor access rights, detect unauthorized activities, and respond quickly to security incidents.
When data is distributed across multiple systems, enforcing data privacy and compliance regulations becomes much more difficult. Likewise, maintaining consistent security practices and safeguarding sensitive data becomes more complex and time-consuming. Data silos impair an organization’s ability to implement comprehensive security measures, making it more vulnerable to cybersecurity threats and breaches.
Security teams can understandably become frustrated when authenticating and authorizing users across isolated systems. They face the headache of managing numerous systems without having all the necessary information at their fingertips. Each application and IT system has its own unique identity and data security measures. Without a comprehensive understanding of these different IT systems, the security team cannot effectively meet their security requirements.
Higher Compliance Issues
Data silos can hinder an organization’s ability to comply with regulations. To begin with, the data silo model often means manually joining siloed data together for compliance reporting to external regulatory bodies. This manual process increases the risk of missing important information or introducing errors. The entire task is also very time-consuming and costly. It’s like trying to piece together a complex puzzle with half the pieces scattered under your couch.
Imagine a healthcare organization where various departments maintain separate data silos for patient records, including billing, medical records, and human resources. This fragmented structure poses a compliance risk, especially with regard to data privacy regulations like General Data Protection Regulation (GDPR).
Patients requesting that their health data be erased under GDPR pose a challenge to health organizations if their data is dispersed across multiple silos. Without a centralized system for tracking and managing such data, identifying and deleting all relevant information becomes complex, potentially leading to non-compliance with GDPR.
Breaking Down Data Silos
Change management involves effectively guiding individuals and organizations through transitions to ensure successful adoption and implementation of new initiatives. To drive cultural changes regarding data sharing, leaders must lead by example and actively engage with their teams, personally communicating the tangible benefits of data sharing (such as reducing wasted effort) and relating them to the organization’s specific challenges (such as high overhead).
Resistance to change from employees accustomed to existing practices can be problematic. They may not fully buy into the benefits of data sharing. In addition, overcoming the fear of sharing sensitive or proprietary information can be challenging, as individuals may be concerned about the potential risks or consequences.
Centralizing data is perhaps the most obvious way to reduce data silos and promote collaboration. By bringing all siloed data into a single, accessible location, organizational teams can more easily access the information they need, facilitating decision-making and teamwork.
Achieving this goal is frequently more challenging than it appears. Organizations with entrenched departmental silos may face resistance to centralizing data. Different teams may have their own data storage and management practices, making it challenging to align and adopt a centralized approach. In addition, ensuring data quality and performing data integration to create a unified view can be highly complex and time-consuming.
Data Integration Through Scripting
Data integration through scripting uses programming languages and scripts to automate merging, transforming, and consolidating siloed data from various sources. IT departments have historically been responsible for developing SQL, Python, or other language scripts to move data from isolated storage locations to a centralized repository.
There are a few problems with this strategy. As the number of data sources increases, the complexity of maintaining and updating the scripts also increases. Since this process necessitates regular involvement from the IT department, business users frequently experience delays while they wait for IT staff to prioritize their data transfer requests. This arrangement makes manually-coded integrations time-consuming and expensive for IT experts and business users.
Data Integration With ETL Tools
Companies frequently employ on-premise ETL (extract, transform, load) tools to integrate data. Data integration tools collect siloed data from various sources, convert it to a standard format for analysis, and then load the results into a data warehouse or other data management system. ETL tools automate the process of transferring siloed data from these sources to their respective target systems.
Data integration tools require upfront data modeling to define the desired data structure. This process leads to challenges as the data may change and become outdated when the data modeling exercise is complete. This delay can hinder accurate analysis and adaptation to evolving business needs.
Cloud-based ETL tools provide organizations with the same functionality as traditional on-premises ETL tools, with the added benefit of being more scalable, versatile, and cost-effective. This pay-as-you-go model often comes as a managed service, where the cloud service provider handles infrastructure maintenance, updates, and security.
Establish Data Self-Governance
Data self-governance dismantles data silos by clearly defining data ownership, accountability, and management. In brief, the concept embraces the ability of individuals to exercise control and decision-making over the collection, use, storage and sharing of data.
This approach empowers business units to take ownership of their data while implementing a data governance framework and access controls. Organizations can foster consistent practices and streamline data integration by establishing standards and usage policies. However, orgs may need to designate data stewards to ensure that data is accurate and consistent.
How Revelate Achieves Data Sovereignty
Revelate’s enterprise data platform processes, prepares and distributes data through its data marketplace. The company enables data distribution across formats, platforms, and organizations with little technical input. They help teams extract siloed data from any source; their data integration tools are platform-independent. Additionally, they provide an affordable, accessible platform for companies of all sizes, whether a well-established corporation or an expanding business.
Revelate uses automation so that when a market participant requests access to your data, a series of triggers will complete the transaction. Revelate first consults your personalized security and access policies to determine whether the user can access the data. Second, Revelate gathers data from its origin and processes, prepares, and distributes it. Revelate then provides the dataset to the customer via the data market.
Because this process is automatic, anyone, not just your IT department, can fill orders. Data customers can self-serve dataset requests based on your data marketplace products, and staff members can bid on a customer’s behalf.
Revelate’s data marketplace enables different teams and departments to securely access and exchange data, promoting collaboration, informed decision-making, and a comprehensive view of organizational data. By embracing the data marketplace concept, organizations can break down data silos and harness the full potential of their data assets.
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