Why Are Data Silos Problematic for Your Business Growth?

Divyansh Sharma • Last Modified: December 29th, 2022

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Businesses are leaning on data more than ever to make informed decisions, but scattered data systems are creating tunneled vision and misguided initiatives.

Data teams in companies of all stripes face the same big challenge – they lack easy access to data while making the right decisions. Fragmented information and ideas contained in discrete data systems, aka data silos, create barriers to coordinated collaboration and communication and misguided initiatives that impact your business’ bottom line.

In multi-team projects, disconnected data leads to friction in the overall team’s work – they slow down work, make information sharing difficult, add unnecessary costs, and lead to inefficient use of organizational resources. By breaking down data silos, businesses aim to unify all of their operational and experience data. This sets them on a path toward a more proactive use of data throughout the organization. 

In order to eliminate data silos, we must first understand what they are and how they develop. Have a look at the different ways why are data silos problematic, and how data silos prevent your teams & business executives from seeing the big picture and harnessing the true potential of data.

Table of Contents

What Are Data Silos?

Data silos are isolated data stores accessible to a few groups in an organization. They do not have the capability to exchange data with other data systems and can only support a single business function.

A simple analogy is to think of data silos like silos on a farm where grain (data) is stored in separate locations. 

What Are Data Silos: Why are Data Silos Problematic
Image Source: Self

The failure of data silos to exchange information has unfortunately drawn a negative connotation. Indeed, some organizations find them to be a big problem – data silos create longer analytics cycles, produce barriers to information sharing, prevent collaboration, and make it hard for business leaders to see the big picture.

Over the years, businesses have heavily invested in software applications to optimize their business processes, and it’s not hard to see why most organizations are still suffering from the “silo problem”. They have created a number of systems, processes, and applications that all generate data to fulfill their own purpose. In doing so, they have created their own little piece of siloed truth.

A report from Forrester consulting commissioned by Dun & Bradstreet has found that 80% percent of firms struggle to manage the volume, variety, and velocity of their data. The main culprit? Data is difficult to aggregate as it resides in different data silos.

What is more, 72% of firms stated that managing multiple CRM systems across technology silos is moderate to extremely challenging. Another survey of 1000 IT decision-makers from Adobe saw 37% of the respondents quoting data silos to be the biggest challenge in obtaining a single view of their customers.

Data silos have for long been critiqued for their negative impact on data integrity and productivity. For many businesses, it is apparent that they restrict visibility across different verticals, and also negatively impact informed decision-making. 

But if data silos are that problematic, why were they created in the first place?

Back to History: How Did Data Silos Emerge?

Let’s wind back our clocks to see how the concept of “data silos” emerged. 

Some 15 years ago, business operations used to be centralized. Every activity was interconnected and followed a top-down approach. Data and business users would receive instructions from their managers, who would, in turn, receive directions from the top management or C-suite. Everything was sequential, with less confusion and risk, but this approach had shortcomings – it limited innovation and responsiveness. 

To be successful in the market, data and business teams needed greater flexibility and responsiveness.

And hence came data silos into the picture.

Data silos brought agile, siloed functions to every team, giving them autonomy to design and ship products/services based on their market and their innovations. Using this, teams could have their own applications, tech stack, and customized deployment pipelines. 

They were able to churn out unique products/services which were super quick and easy to design. This act brought significant benefits to the customers as well. It improved customer experiences and uncovered what they truly wanted.

This story about data silos appears to be off to a good start. Data silos introduced autonomy with added benefits of privacy so that teams could conceal their sensitive data and store it securely. 

But there are a couple of other reasons as well which have led to the creation of data silos in modern-day business workflows.

Why Do Data Silos Exist?

Our present-day business teams lock away data behind multiple data silos, and multiple reasons can be attributed to the cause of their existence. 

Organizational Structure

Disconnected data systems emerge when disparate teams start adding software applications or datasets optimized for their main function. This framework prioritizes insular function over data sharing. Moreover, due to bureaucracy, restrictive access control systems, and permission schemes, there is usually no one in charge of making these discrete systems accessible within a company. 

Company Politics

Because knowledge is power, different teams within an organization grow distrustful of those who wish to utilize their data. Teams, when incentivized for better workplace performance and execution, tend to insulate data and information flow. This constant contest of possessing and hoarding data creates data silos and works against the organization’s best interests.

Different Technological Requirements

Each department in an organization has different technological requirements, which means they need different tools for their use case.

The rise of cloud computing, low-cost storage with virtually no deployment lead times, and pay-as-you-go models have made it easier for businesses to adopt the best-in-class Software-as-a-Service (SaaS) applications for their daily operations.

Your sales teams might choose to use Salesforce, while your marketing teams might go with Marketo Engage or MailChimp. Most of these applications are built independently of each other and are designed to best serve a specific business function. They have their own custom requirements and data touchpoints. 

If your data and business teams cannot integrate these applications with your tech or analytics stack or share data, they are essentially creating data silos, which can cause many more problems in the future.

Rapid Business Growth

When businesses grow too fast, they run into unexpected problems – infrastructure and processes frequently fail to scale, and during the process, individual departments may set out to adopt different processes and business applications haphazardly. 

This results in creation of isolated data assets that can only be accessed and used by the teams who created them, as well as a backlog of cleansing and integration work for data management and IT.

Vendor Lock-in

A proprietary lock-in is a common business strategy used by software vendors to keep users from switching their business applications. 

When your teams adopt a SaaS application and start using it daily, they grow accustomed to using that application, which makes it difficult to switch to a different one. This increased dependency causes concerns because your teams are not willing to undergo re-training (if you wish to introduce a new application) and make significant changes to their workflow.

Vendor lock-in creates data silos, since your teams may choose to keep using the same software application that cannot be cross-referenced.

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Why Are Data Silos Problematic?

Harvard Business Review has made a very accurate analogy – data silos are “isolated islands of data” that make it difficult to extract data and utilize it. Having data silos prevent you from having a consolidated view of company data and from performing holistic data analysis. 

Data silos become a “pressing problem” when an organization amasses quite a few of them, and they start generating negative consequences in your decision-making – by providing disconnected customer views, slowing down data teams’ productivity, and creating sub-par data analytics.

And this issue isn’t something new.

In 2001, IDC released a white paper titled “The High Cost of Not Finding Information” where structural limitations of information systems made it difficult for knowledge workers to search for information. 

This was reinforced again, in 2018, when IDC released another study that found that “data professionals are wasting 30% of their time searching for, protecting and preparing data”.

Here is a practical example to help you understand why data silos are problematic.

Why Data Silos Are Problematic: A Practical Example

Assume you are a data professional for a multinational retail supplier. For your upcoming quarterly report, you need data from different parts of the company to perform data analysis. 

You require inventory data, supplier data, purchase data, data about factory workers, and other supply chain data from operations teams. You require data from the human resources system to verify these records. It is also necessary for you to have insights into the customer base, their preferences (which you would request from your customer support teams), and sales trends (from sales teams) to project how your business will grow in the future. 

If your teams are using data silos, it will be a difficult and time-consuming task to request and obtain all the required pieces of information. All of these teams will have different policies and processes for managing information, depending on their unique needs and goals. Even if you somehow gain access to their data systems, extracting information won’t be easy.

Our example tells a lot about the state of the “silo problem”. It is beyond doubt that businesses are having a hard time dealing with data silos, and the big data explosion is already here to exacerbate it.

In addition to what we have discussed, there is a growing list of other challenges as well that data silos bring to the table.

Data Silos Bring Fragmented Information and Ideas

Data silos create disconnected views, which prevent you from performing holistic data analysis. When your teams have fragmented information and ideas, naturally the decisions they make won’t be fully informed. 

Making decisions in a sea of disconnected data systems is like observing the tip of a floating iceberg. The real danger is that your data & business users or even business executives aren’t aware of the complete picture. They miss out on important and deeper insights that can help your business create strong growth.

For most organizations, a 360° view of their customers is one of the most prized goals, as it enables them to act proactively and deliver relevant and personalized messages at scale at every stage of the customer journey. Unfortunately, with data silos, achieving single customer views is near impossible.

Incomplete and Inaccurate Customer Profiles: Why are Data Silos Problematic
Image Source: Self

Data Silos Decrease Data Quality and Credibility

Storing business data in isolated data systems decreases its usefulness. Data when isolated becomes outdated or inaccurate very quickly, which can lead to a lack of data integrity in your organization as a whole. 

Silos of data, since they remain disconnected, may never get updated or checked, which can result in inaccurate and stale data. If this inaccurate data is used by your data or business users in their daily operations, it might result in additional problems.

In other cases, business teams may keep duplicate data in their storage systems as well. For a certain project, the marketing and sales teams could duplicate specific fragments of your intended audience. Since there are several sources of incomplete, partial information, this practice of duplicating data compromises data quality.

Data Silos Slow Down Time to Action

As talked about previously, speed and agility are core to a thriving business. In order to succeed in business, businesses must immediately examine every aspect of their customers and provide their demands in a timely manner to get a competitive edge.

When information is segmented, your business teams have to do double duty – there are chances that different teams might end up repeating the same set of tasks since tasks are not clearly defined for them. In these scenarios, the time-to-value ratio gets high, and inevitably “data fatigue” settles in.

Making rapid and informed judgments is a challenge with data silos. Your data and business teams need to gather and process data first from multiple places. By the time this happens, you will have lost the advantage of moving first. Additionally, with data silos, you may find yourself basing decisions on information that is no longer current.

Data Silos Are Barriers to Organizational Alignment

Organizational alignment is a proven driver of business success. It aligns operational and functional teams on a clear and meaningful purpose, ensuring that all aspects of your business- operational and strategic, work cohesively and at optimum capacity. Having a clear path forward allows teams to produce better results faster with less effort because they are more agile and responsive to changes in operating environments.

Organizational Alignment: Why are Data Silos Problematic
Image Source: Self

Data silos are barriers to organizational alignment. They steal away agility, swifter, and more informed decision-making from data and business teams. In the long run, they undermine continuous development, learning, and optimization of talent. As a result of data silos, there is a lack of coordination and cooperation between different teams, causing barriers to productivity and inefficient use of resources.

Overcoming collaborative challenges requires you to streamline your business applications that integrate with each other, and centralize your data – in a cloud data warehouse or a data lake. This enables data and business teams to standardize information and gain a comprehensive view of your organization’s data.

Data Silos Hurt Good Working Relationships

Data silos tell a lot about working relationships between teams. If your teams are storing and working with data in separate data silos, that means either they don’t feel the need to communicate with other teams, or they don’t see the benefits of sharing their data with other teams.

With the existence of data silos, teams’ activities and work get compartmentalized, with the sole focus of achieving their own projects and goals. This does not foster a favorable environment for cooperation and good working relationships between teams. After all, individual departments can’t function by themselves. 

A survey from PwC’s global operations found that 61 percent of companies believed that cross-functional collaboration was the solution to reaching strategic goals. Through cross-learning and creating solid work relationships, data, and business users gain a better understanding of how their own job fits into the “big picture”. 

They also get to build cohesiveness, a better understanding of what they are up against and learn how to navigate recurrent problems that have plagued other departments in the past.

Why Should Business & Data Teams Eliminate Data Silos?

Having gone through a listing of why data silos are problematic, here is a quick summary of the reasons why your teams should eliminate data silos:

  1. A centralized repository will help provide comprehensive data analysis.
  2. Your teams gain more visibility into organization-wide data.
  3. Eliminating data silos speeds up decision-making, and makes your organization more competitive.
  4. You enable the free exchange of information and collaboration within teams.
  5. Your data and business teams become agile and responsive to find new ways to grow both the top and bottom lines.

Breaking Down Data Silos

Uncovering rich information and insights require organizations to overcome barriers that keep data segregated. They must adopt a systematic approach to data integration, data management, and data governance.

At the very core, breaking down data silos shouldn’t be from the highest level, but rather a collaborative effort that requires a fundamental shift to a mindset that encourages borderless sharing of information.

Changes in organizational culture, encouragement of communication and collaboration among different business teams, and reducing needless interdepartmental competition are all helpful, but we think these are secondary solutions that are bound to be dropped in a competitive and dynamic environment, if not implemented successfully.  

Just think about these remedies for a moment – You can always address cultural and political issues later, in parallel to or after your efforts of building a concrete solution.

A real solution to the silo problem comes when companies sought out to plan and develop a data analytics strategy that implements data governance policies, evaluates their technical requirements and technology capabilities, and establishes a solid groundwork for data integration. 

To achieve this, they need to invest in end-to-end data analytics solutions that can handle a variety of requirements: including seamless integration of data, transforming data into desired formats, providing secure storage, and supplying actionable insights at the fingertips of customer-facing business and data teams.

Building a Solid Data Foundation

One way to resolve data silos is to streamline your applications and build manual program integrations between different applications. This requires writing scripts in SQL, Python, or other languages to move data from siloed data systems into a centralized source of truth. If you ask us, this activity is not enjoyable at all.

The reality is that most companies don’t have the time and luxury to hire data engineers and integrate their data from different sources. And the problem of data integration doesn’t end there – your data engineers have to build connectors for individual applications, keep them up-to-date, account for scalability, and continuous monitoring for any errors. 

In the age of digital transformation and constant data accumulation by innumerous systems, you need sustainable, scalable, and cost-effective solutions that can extract data from your heterogeneous systems like databases, SaaS applications, cloud-storage services, streaming services, etc., and replicate it into a single source of truth like a data warehouse, or a data lake to help you uncover value-rich information that is readily accessible for all data and business teams.

Since most organizations nowadays use cloud-based applications, it is more than ever possible to streamline the process of gathering data from discrete data systems and applications into a common repository in minutes or hours, instead of days or months. Cloud-based ETL (extract, transform, load) tools help you transfer your siloed data into a common repository easily, providing quick data integration using ready-to-use connectors and the creation of efficient data pipelines with lossless data replication.

Such efficient ways of integrating data help improve data search, discovery, and data governance. Having an all-embracing single source of truth gives all your data and business users the same access to the same data all defined in the same way, hence opening lines of communication and data sharing.

Centralize Your Data With Ease Using Hevo Data Pipelines

Data teams might have an easier time breaking down silos if you can show the value of company-wide reporting and insights. After a while, it will be easier to make an argument that shared data is like any other shared resource. The whole will be better than the sum of all its parts.

With increasing accessibility to automated no-code ETL tools like Hevo Data, companies can readily integrate their data from frequently used SaaS applications and databases to their chosen data warehouses like Google BigQuery, Amazon Redshift, Firebolt, or Snowflake for analysis within minutes. 

Hevo Data: Why are Data Silos Problematic

Hevo empowers data and business users across all departments to seamlessly integrate data from a variety of sources – 150+ data connectors, which includes databases, SaaS applications, file storage solutions, streaming services, and SDKs. 

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Having a beneficial tool like Hevo that establishes a single source of truth for all of your company’s data will undoubtedly speed up your data analysis and integration process and give a boost to your organization.

Hevo also offers reverse ETL capabilities, through Hevo Activate, using which you can sync your actionable data residing in data warehouses into operational systems so that your teams can make fast decisions, scale customer-centered decisions, and design contextual customer engagements across their journey.

This way you create an end-to-end data analytics stack, from ingestion, transformation, and storage to visualization and operationalization.

Brand Case Study: How Scale Media, a Tech-driven Direct-to-Consumer Platform Achieved Greater Control Over Data Flow

Image Source: Forbes

Scale Media is a tech-driven direct-to-consumer platform that develops and deploys cutting-edge consumer brands from concept to scale. The company has achieved success in health, nutrition, entertainment, advertising, creativity, and media buying and has sold over 5.9 million products to 1.6 million customers.

Prior to upgrading their data & analytics stack with Hevo, Scale Media had to spend close to 60 hours per month building and maintaining API connectors. Since their data was stored in multiple disconnected systems, timely data availability and quick report generation were increasingly difficult. 

Seeing these inefficiencies, Scale Media decided to build a centralized repository, and for that, they required a modern ETL solution that would pull in and transform the data from their siloed sources – MySQL, Google Analytics, Criteo, Outbrain, Google Ads, Facebook Ads, etc. into Redshift. Hevo simplified their data integration process with its ready-to-use connectors and provided them with a hassle-free data movement experience.

Switching to this modern data stack was easy, quick, and very impactful. We are all benefiting from these specialized tools with Hevo doing all the heavy lifting, it’s taking care of the heavy SQL, and reducing the build times means quicker load times for everyone and less downtime for the dashboards. Hevo is a power-packed ETL tool that provides a wide range of integrations, and a powerful transformation layer, and their support team is fantastic. 

– David Goodman, Manager, Data & Analytics, Scale
Scale Media's Data Analytics Stack: Why are Data Silos Problematic?

Final Thoughts

Breaking down data silos and unifying all of their operational and experience data is a top priority for many companies. Through this piece, we looked at multiple reasons why are data silos problematic. Making data more portable and accessible empowers better business decision-making and more proactive use of data across different departments. 

To remove the barriers of silos, a progressive, pragmatic approach is most effective. Cultural and technological shifts are keys to bridging data silos and generating the true value of data. Consolidating data into a centralized repository like a data warehouse aggregates and organizes company data for comprehensive analysis, knowledge discovery, and sound decision making. 

Using automated, scalable, cost-effective, and zero-maintenance ETL solutions like Hevo, companies can integrate data seamlessly over the borders of silos (tools, files, databases). Hevo solves the complexities of modern data environments by breaking down the data silos to unleash the true potential of integrated data management and paving a way for a more proactive approach to data analytics.

Break Data Silos by Seamless Data Integration