The Impact of Data Silos (and How to Prevent Them)

Data silos often develop unintentionally within businesses, catching leaders by surprise. They hinder cross-departmental collaboration while giving rise to inconsistent data quality, communication gaps, reduced visibility, and increased expenses. The gravity of impact can be gauged from a report by Forrester research, which finds that knowledge workers spend an average of 12 hours a week “chasing data.” This is precious time that could be spent on value-added work and decision-making.

The emergence of data silos in businesses can be attributed to various factors, including organizational, cultural, and technical aspects. Regardless of the cause, these silos pose a severe threat to productivity and business.

Overcoming data silos is challenging and takes solid leadership commitment and disciplined Data Management practices to resolve fully. Leading-edge businesses today recognize the hazards of data silos and are actively working to dismantle them.

Read on to better understand data silos, their formation, and the risks they present to businesses, as well as how to prevent them from forming in the first place.

What Are Data Silos, and Why Do They Occur?

When data is inaccessible by certain groups of people within an organization, it is said to be a data silo. Simply put, a data silo is data that is separated and isolated within a company. 

But why do data silos occur? Here are some factors that cause data silos:

• Lack of collaboration: Data silos are formed when departments or processes work in isolation. In most organizations, departments work in compartments, developing their own unique systems, processes, and data. Limited collaboration between departments and teams gradually widens, and data silos are born. 

• Varying toolchains: Teams might be using different toolchains for their work. Take an example of a software company where multiple development teams use different requirements gathering tools. One team uses Azure DevOps, another uses Jira, and a third uses Notion. Even though the tools are used for similar purposes, they remain disjointed.
• Acquisitions and mergers: Whenever a company takes over another or merges, the rise of data silos is inevitable. This is because the two companies have different management styles, systems, processes, and technology stacks. For instance, two social media companies coming together might be using two different cloud providers, Azure and GCP. Management will need to decide which cloud services provider to consolidate to. Otherwise, the data will continue to remain split, giving rise to a form of data silo.

How Do Data Silos Negatively Impact Businesses?

Siloed data diminishes visibility, leading to data inconsistency and poor quality. According to Gartner, bad data annually costs companies a staggering $12.9 million. Leaders face a dilemma, as accurate and high-quality data is the backbone of all data-driven decisions. 

Here are just a few ways businesses are impacted by data silos:

• Incomplete picture owing to fragmented and incomplete data: When data exists across systems, it tends to be fragmented, with bits and portions distributed in different databases. Data analysts can find it hard to piece together the data to form a complete scenario. The incomplete data gives rise to biased perspectives, ultimately making it difficult for leaders to arrive at the right conclusions.

• Duplicate data: As data is ingested and stored across different systems, there is no knowing if the same data is being stored redundantly. Team leaders seldom collaborate to synergize their needs and use the same data stream. This can be extremely expensive for companies, as storing and maintaining duplicate datasets across systems can cost hundreds and thousands of dollars.

• Data integrity and security: As data silos start to build up, the chances of data going out of sync increase as well. Think of energy data for a user base being stored across different systems. Some are stored in local drives, and some by unvetted third-party storage providers. Data integrity becomes a huge risk as data discrepancies can arise. Teams often spend valuable time trying to make sense of the discrepancies and root causes.

• Security risks: IBM provides insights that companies lose around $4.35 million on average per data breach. These businesses may lack data security practices. With data silos, security becomes even more difficult as data is present in different places. It makes the information more vulnerable and prone to attacks. Inconsistent data security strategies are often implemented across all departments.

How to Avoid Data Silos

Leadership can employ several best practices to prevent data silos from forming. The effort requires a mix of behavioral, process, and tool changes. Below are some of these methods:

• Culture shift: Management consultant and author Peter Drucker famously once said, “Culture eats strategy for breakfast.” These words emphasize that no matter what strategy and tools you deploy, all investments will fail if a culture shift is not made. Hence, it is important to first start with a mindset change where collaboration and teamwork need to be given paramount importance. Department leaders should start by exchanging notes on the data they hold and publish the details like granularity, coverage, retention, etc., throughout their company. This will help identify opportunities and areas of improvement.

• Introduce data lakes: Data lakes are capable of holding vast amounts of structured and unstructured data. Companies can leverage data lakes to centralize their data and make them available throughout their company. Data lakes make it easier for companies to synchronize data delivery pipelines and implement data governance at scale.

• Make use of data catalogs: A data catalog is an inventory that publishes metadata for a company’s data. Data catalogs are an efficient means of learning specifics about the data set and its structure. They democratize data by providing vital insights regarding the availability, governance requirements, and granularity of the data.

• Automate ETL processes: Automated scripts can be employed to collect data from various silos and flow it into a central repository. This saves tremendous amounts of time. The only caveat is to seek consultation from a data architect in case the data needs to be structured for further use.

Conclusion

Data silos cost companies millions of dollars in productivity and bad decision-making. No one benefits from their existence, and it’s in the best interests of all stakeholders to prevent or eliminate them. 

The world is evolving rapidly, and data sharing has become more important than ever. To remain competitive, teams must collaborate, synergize toolchains, and formulate processes that help overcome data silos.