Why data silos are problematic and five ways to fix them

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How do data silos form?

Data silos form when different departments or business units act autonomously, each possessing its own mission, objectives, and even IT budgets. For example, the HR department will typically have its own employee database optimized for their needs, which it doesn’t share with other parts of the company.

Here are the top reasons data silos form:

  1. Departmental autonomy: Departments within organizations operate independently because they have their own specific needs. Therefore, they end up with a customized or point product that addresses that specific need but doesn’t align with the rest of the business. It then becomes decentralized, managed separately, and not easily integrated.
  2. Lack of data governance: Without clear data governance strategies, such as policies and best practices, businesses end up with non-standardized processes for managing and sharing data across the organization. This results in the proliferation of data silos.
  3. Legacy systems: Old and outdated technology may not be compatible with modern data management solutions. This results in systems being unpatched for the latest cyber threats or data becoming isolated, unable to be used for analysis, re-monetized, or recovered easily.
  4. Use of disparate software systems: Various departments have different needs, but using different systems can lead to data silos because these systems may not be interoperable or able to share data with other systems seamlessly.
  5. Mergers and acquisitions (M&As): An entity acquiring or merging with another business will almost certainly create data silos until all data stores have been adequately transformed or integrated. It’s important to note that in addition to obvious data silos, there will be hidden ones that need to be identified in the new, joined entity.

Why are data silos problematic?

Siloed data can make it hard to make data-driven decisions. By locking up various subsets of information into standalone repositories, organizations limit the insight—and the value—they can derive from it. This is problematic for several reasons, leading to these negative outcomes:

Poor decision-making

Data silos lock information away from users who need it. Business actions aren't based on all available data, which can lead to sub-par decision-making. Silos also create data-quality problems since different data silos can have redundant data that has gotten out of sync, driving inconsistencies between business functions.

Reduced collaboration and cooperation

It's easier for teams to work together to meet corporate goals when they possess a different version of data truth. When data is difficult—or impossible—to share because of silos, collaboration and cooperation between departments suffer, and ultimately the customer is affected by the lack of congruency.

Higher costs

Data silos add to IT costs by increasing the required number of servers and storage devices an organization needs to buy. Renewal costs skyrocket because parts and labor are harder to find. Operational costs also increase as such systems are deployed and managed separately by departments instead of a centralized team, leading to redundant and inefficient processes.

Security, ransomware, and compliance risks

Many data silos are created by individuals or teams generating their own spreadsheets and using their own online productivity tools. This increases data security and compliance risks for organizations without appropriate controls in place. For example, vulnerable silos can give ransomware attackers and other cybercriminals a better chance of success.

What are the benefits of getting rid of data silos?

Eliminating data silos is critical for organizations striving to remain competitive in the digital era. The benefits of getting rid of data silos range from better insights for decisions and strategy, including better communication and collaboration, to increased agility and competitiveness. Organizations that remove data silos also improve operational efficiencies and cyber resilience while enhancing customer experience and lowering risks.

By having access to all data collected and stored by the business, leaders can identify trends that would have previously been invisible and thus make smarter and faster decisions. Without silos, organizations can seize new opportunities and monitor performance precisely, enabling leaders to rapidly improve margins and identify areas for improvement in real time.

Breaking down data silos and integrating data across the organization also improves both communication and collaboration between users from different departments, making it easier to find synergies between them, and to collaborate for the good of the business as a whole.

Data silos challenge users to access all the data they need to do their jobs. They often have to manually request it and transform it laboriously into the right format. Also, IT professionals are needed—whether from a centralized unit or within a department—to manage and maintain these separate data silos, increasing inefficiencies and costs. Removing both these barriers drives organizational efficiency.

Customer trust and loyalty can also be negatively affected by data silos. Disconnected information can lead to delays, confusion, frustrated customers irritated by inaccurate invoices or reports, and repeatedly requesting or providing the same information to different departments.

By eliminating silos, organizations can boost customer-service quality and engagement with personalized offerings and immediately accurate answers to questions.

Lastly, security risks and compliance mandates are constantly evolving. Specifically, ransomware attacks are rising, with cybercriminals going to greater lengths to lock up and exfiltrate data for financial gain. One of the greatest challenges facing all businesses is to minimize risk. Siloed data makes the job harder, as it prevents organizations from accurately assessing risk and, if not solved, can cause both reputational and financial damage.

What are some hidden financial costs of data silos?

Siloed data can have negative financial impacts like, higher data storage and management expenses and renewal costs. These are the most tangible costs businesses can expect from data silos. But data silos also have various hidden costs, including reduced productivity, missed business opportunities, ransomware recovery, and poor-quality customer service.

When faced with data silos, users often are forced to find complicated workarounds that are difficult to maintain and can impair data quality. This turns into a continuous loop in which business processes and productivity deteriorate over time. Moreover, because there’s no holistic transparency into what’s happening—both within and external to the business—organizations can miss potentially lucrative opportunities to increase revenues or cut costs.

Ransomware payments or expensive ransomware recovery time and costs can be another unexpected financial burden of data silos. Disconnected systems or those operated as “shadow IT” can make it easier for cybercriminals to penetrate and exploit them successfully.

Customer costs can also rise when information concerning customer profiles and transactions is fragmented across multiple data silos—say, a point-of-sale (POS) system, a mobile app, and a SaaS CRM system. The business won’t have an informed view of a customer that would enable it to personalize experiences and special offers to maximize customer value.

Five ways to break down data silos

To eliminate data silos requires organizations to take a range of actions. These encompass strategies that are technological, process-based, and organizational.

Evolve the culture

If organizational culture can be responsible for creating data silos, it's logical that changing the culture can eliminate them. Cultural change is difficult, so this is not easy. Critical to success is to tie removing data silos to a tangible data strategy development or data governance initiative. Communication is key: spreading the word about collaboration and sharing resources with others—even people in other departments—and garnering some quick wins that leaders can use to evangelize cultural change throughout the organization.

Centralize data in the cloud

A gold standard for breaking down data silos is to gather all data into a cloud-based data repository where data from different sources can be stored (plus analytics performed on that data) and access can be given to users as appropriate, given security and compliance concerns.

Integrate data

Bringing together data stores is the most popular way to break down data silos. For example, extract, transform, and load (ETL) tools extract data from sources, consolidate it, and load it into another system. A variation on ETL is extract, load, and transform (ELT), which is more suitable for more complex and unstructured data. Writing scripts in SQL or Python can also integrate data silos, but it is time-consuming and ultimately more costly.

Offer self-service access to data

When data is centralized and stored in the cloud, the opportunity to give users self-service tools to access and analyze it without IT gatekeepers goes a long way to preventing new data silos from forming—and for achieving a data-driven business.

Create a strong data governance program

A robust data security and data management strategy helps break down existing silos while also preventing new ones from being created. An enterprise data strategy aligns data with business needs and cybersecurity best practices while establishing standardized, proven data policies and processes.

Cohesity and removing data silos

Data silos are part of a larger problem facing organizations today, which is massively fragmented data. The increasing proliferation of data across numerous locations, infrastructure platforms, and management systems not used every day represents the vast majority of an organization’s data and often has to be stored for a particular length of time to meet compliance mandates.

Teams that have yet to break down data silos can’t use all of their valuable data. They can’t fully digitally transform, moving to hybrid or multicloud environments to achieve objectives.

When fragmented (also known as “dark data”) is scattered across an environment, the enterprise doesn’t know where all of its data is located, let alone what that data is—structured or unstructured, objects or files, or more. This can make siloed data an operational nightmare and a potentially serious compliance or security risk.

Three key factors contribute to massively fragmented data:

  • Multiple point products managing disparate data silos and legacy on-premises infrastructure, which adds complexity to the jobs facing data security and data management teams
  • Lack of integration of point products across core, multicloud, and edge locations, intensifying security risks
  • Copies of data stored in multicloud environments, adding cost and compliance challenges

Cohesity solves the proliferation of data silos problem with a multicloud data security and management platform that converges, backup and recovery, threat intelligence, cyber vaulting, files and objects, development and testing environments, and analytics on a single platform.

With Cohesity, organizations achieve business goals fast by:

  • Removing point products for data protection and management, reducing complexity
  • Seamlessly integrating existing solutions (for example, VMware, Microsoft, Pure Storage, and more) and providing native cloud integration (for example, AWS, Azure, and Google Cloud)
  • Eliminating data copies, reducing costs, and streamlining compliance

The proven Cohesity approach eliminates data silos while dramatically simplifying infrastructure through an on-prem or SaaS solution. With Cohesity, organizations dramatically improve CapEx and OpEx and are capable of using all of their data for competitive advantage.


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Mass data fragmentation

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Ransomware attack

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Ransomware recovery

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Business continuity

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Cyber resilience

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