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How Do Data Silos Impact Your Organization?

Posted by Charlotte Foglia

How Do Data Silos Impact Your Organization?

Data is more critical–and prevalent–than ever. Over one trillion megabytes of data are generated every day. It’s estimated that by the end of 2025, cloud storage data will amount to 200+ zettabytes.

When data is clear and accessible, it can give you a competitive edge, inform your business decisions, and even boost revenue. But when information remains hidden and convoluted in data silos, it does more harm than good for your business.

Data silos, or information silos, are the culprit behind lost productivity, inefficiencies, missed insights, and even risks to security. But for many organizations, they are the status quo.

In the following article, we’ll look at what data silos are, why they were created, and how they damage your organization. Most importantly, we’ll look at how to remove data silos so that you can maximize success by speeding up workflows, searching data quickly, and gaining data-driven insight.

Data Silos: Definition and Origins

By definition, a “silo” is a structure used to house mass quantities of material such as wheat or grain. Silos typically stand side by side, storing goods separately. The material within the silo is abundant and free-flowing, but it’s trapped.

Likewise, a data silo is a collection of data within an organization that stands alone. Data silos “trap” information such as word documents, media, metrics, feedback, financial reports, customer records, and contact information. As a result of data silos, employees have difficulty accessing, searching, and analyzing data across departments.

The origins of data silos are complex. There can be several responsible factors, including company culture, infrastructure, and a lack of technology adoption.

Company culture can be responsible for creating and sustaining information silos. If competitiveness wins over collaboration, then departments are unlikely to want to share data.

Or, the root might be less malicious. In many cases, departments are unaccustomed to sharing information, and until recently, having a unified view of data was not a common objective. As a result, employee behavior hasn’t reflected a need for communicating, sharing, and analyzing data across departments.

Company infrastructure may be another common cause of data silos. Before the advent of cloud technology, accessing information often required a chain of requests and attachments. Now, technology has made it possible–and even standard–to share information automatically. However, many organizations weren’t designed to support an open, transparent flow of information between different teams and departments.

Likewise, technology adoption (or a lack of technology adoption) can reinforce data silos. With technology such as machine learning and NLP (natural language processing), organizations can search, access, filter, and transform data into actionable insights.

But many organizations have fallen behind in new technology. Though it has been proven to improve operational efficiency, three out of 10 companies don’t have a strategy for digital transformation. In many cases, organizations haven’t adopted technologies to help them search and filter data.

Finally, limited software integration can produce data silos. Teams often use different software to handle various needs, storing data in tools inaccessible to other groups.

Why Do Data Silos Impact Your Productivity?

Take a look at your organization. Is your information siloed?

If so, your employee productivity is suffering.

As mentioned above, data silos prevent information flow. That can have damaging long-term consequences on your company–reducing productivity, wasting time and resources, and even limiting growth and revenue.

First, data silos slow down your rate of productivity.

Manual data collection can be tedious and time-consuming for your employees, who must make requests of other employees via email or Slack to access what they need. That can result in significant delays and interrupt the flow of work for both parties. A report from Panopto says that employees waste 5.3 hours every week waiting for data from their colleagues–or recreating information that already exists.

Next, data silos cause organizations to consume unnecessary storage space. Information silos often contain repeat data or stale and trivial data. Personal information created by former employees and outdated surveys are two examples of irrelevant data.

Without a way to organize, filter, and search data, organizations continue to pile up this kind of data at the expense of storage space.

Data silos can be costly. Lost productivity is estimated to cost U.S. businesses $1.8 trillion every single year (Hubspot). Moreover, data silos are responsible for a portion of this lost productivity.

When employees spend part of each day retrieving information, they’re not making the best use of their time or energy. Their work also suffers from a lack of insight.

When employees don’t have access to valuable data, they’re unable to perform as well. For example, a marketing employee who doesn’t have easy access to data involving MRR can’t make wise, informed decisions about marketing spend.

Storage space can also represent an additional cost for organizations that pay for on-site servers to house information or for subscription costs for cloud technology that they may not need.

How Do You Identify Data Silos and Break Them Down?

Before breaking down information silos in your organization, you need to identify them.

Several signs point to the existence of data silos in your organization. Some of these symptoms are cultural; others are more structural.

If your teams aren’t collaborating or setting common goals, data silos may be to blame. When teams aren’t able to access the same information, they can’t get a complete and accurate picture of what’s going on.

This can make it challenging to collaborate and set goals. It can also feed an unhealthy culture of blame-shifting within an organization. When one team is underperforming, a lack of transparency can make it easy to blame another team for inefficiency.

If you find that many of your teams are initiating similar projects or using similar software systems, that can be evidence of a lack of integration. Being able to share data keeps teams on the same page–and keeps them as efficient as possible when making improvements, solving problems, or adopting new systems.

Breaking down data silos in your organization is not a simple task. You’ll need a multi-pronged approach that addresses your company culture, infrastructure, and most importantly, your technology.

To change company culture around data flow, you’ll want to make a case against data silos. The best way to do that? Present the benefits of building a data approach that’s collaborative, transparent, and efficient.

When employees can get a clear, comprehensive view of available data, they’re able to make better, more informed decisions. Part of this is the ability to glean insight from structured data (such as customer metrics) and unstructured data (such as media and text). They’re also able to work more quickly and efficiently, reduce errors, and accomplish their goals with fewer headaches.

To make company infrastructure more integrated, you’ll want to collect your data into a data warehouse or data lake, using a tool like Sinequa. Bringing your data together into a single hub makes it easier to search, filter, and evaluate. Creating a data lake also produces a greater level of transparency into your own data.

Ultimately, you’ll need enterprise-level data management to not only break down your silos but to create a strategy for searching and analyzing your data.

Save Time, Reduce Costs, and Search Data with Sinequa

Sinequa is an enterprise search platform that can help you break down your information silos, perform data analysis, and get data-driven insights by bringing your data into a single libra. Sinequa not only provides you with a powerful search tool for quickly scanning and searching your data; it also gives you the tools to evaluate the data and glean powerful insight through artificial intelligence and machine learning.

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