What are Data Silos?

Data silos are isolated collections of data that are accessible only to specific departments, teams, or systems within an organization and are not easily shared or integrated across other parts of the business. They arise from using different software systems, organizational structures, or workflows that limit cross-functional collaboration.

Data silos hinder efficiency and decision-making because they create barriers to accessing and leveraging your company’s information. For example, marketing, sales, and customer service teams might each maintain separate databases, which results in duplicate efforts, inconsistent data, and missed opportunities for better targeting and customer experience improvements.

While the exact number of data silos varies by company size and industry, research suggests your average company has more than 2,000. To overcome them (and prevent them in the future), you have to centralize your data management process, use tools that each integrate with your central data hubs, and encourage cross-functional collaboration.

Synonyms

• Information silos
• Data islands
• Siloed data

Examples of Data Silos

Data silos are fundamentally the same, regardless of your industry. They normally exist in one of three areas of your business:

• Separate software systems for different departments
• Incompatible data formats
• Lack of integration between data systems

Let’s dive into each.

Separate software systems

When you have data stuck in one department, that means the others can’t access it. That’s a huge problem, because seamless collaboration across departments is a baseline requirement for nearly all business functions.

• Sales and marketing teams have to share data to understand your potential customers.
• Finance needs access to customer purchase history and payment information to issue invoices, track revenue, and create financial forecasts.
• Your product team needs to know what customers tell your support and success teams so they can prioritize feature requests and bug fixes.

And that’s just three examples of data all teams need access to. For large enterprises, the number of different departments and data sources reaches into the hundreds. That’s how information silos form.

Incompatible data formats

How your data is formatted can also be problematic. When you’re trying to integrate and analyze data from multiple systems, even if you have a central data repository, it can make things impossible for the computer to read.

A few examples of what we mean:

• Date formats: Different departments may use varying date formats, such as MM/DD/YYYY, DD/MM/YYYY, or YYYY-MM-DD.
• Measurement units: Disparities in units of measurement, like using kilograms (kg) versus pounds (lbs).
• Text encoding: Variations in text encoding standards, such as ASCII versus UTF-8, can result in misinterpretation of characters, especially in multilingual datasets.
• File formats: Departments might store documents in different file formats, like .docx, .pdf, or .odt.
• Data structures: Incompatible data structures, like different database schemas or the use of different data models (e.g., relational databases versus NoSQL).
• Naming conventions: Inconsistent naming conventions for data fields across systems can lead to confusion and errors during data merging processes.

Data processors aren’t calculators that can automatically notice and fix these discrepancies. They’re only as smart as the instructions and data they receive, which is why it’s crucial to establish a standardized format across your entire company. If you don’t, your numbers will be extremely inaccurate.

Lack of integration

You may already have all your systems for data collection, storage, and sharing set up. But if they aren’t integrated (or can’t integrate), it’ll be impossible to make sense of your data as a whole in any meaningful way. You’ll end up with multiple datasets that don’t communicate effectively and can’t provide insight into your business as a whole.

Let’s say you have some of your sales data in your CPQ and the rest of it, plus your customer data, in your CRM. Since CPQ knows how much your customers spend, where they are in the deal process, and which products interest them, it can update their customer profile and pipeline stage in CRM — if they’re connected. If not, you won’t have any accurate information on your pipeline reports.

Causes of Data Silos

Besides the reasons above that data silos exist, there are a few organizational issues that lend themselves to disconnected systems and siloed data sources.

Organizational structure

The way your company is structured can contribute to the formation of data silos. Every department has its own daily operations and therefore, its own set of data. Especially if they have limited collaboration with one another, they probably don’t have a reason to share their data with other departments on a day-to-day basis, which results in isolated (and, thus, incomplete) datasets.

This issue is amplified if you have separate business units or offices in different locations. Each will have its own systems and processes, making it even more challenging to integrate data.

Legacy systems

A legacy system is one that has been in use for a long time and may be outdated or difficult to integrate with newer systems. The estimated nuber of companies with this problem varies by industry, but it’s always higher than you’d expect. In manufacturing, for instance, 74% of companies are still relying on legacy systems and spreadsheets for getting tasks done.

Examples of this include:

• Mainframe computers older than the employees
• COBOL-based software applications
• Outdated ERP (enterprise resource planning) systems

The problem with these is their data is trapped in legacy formats. They contain important historical data, but that data is impossible to extract automatically and use with the rest of your insights. Systems like these also have issues with scalability, and they can’t keep up with the growing volumes of data companies have.

Data security concerns

A lot of businesses have overly restrictive data access policies that prevent sharing data between departments, and between team members who need to use it. The main reason for these policies is data security. Companies don’t want to risk exposing sensitive information to the wrong people, so they restrict access.

Problem is, overrestricteve policies can lead to segregated data that can’t be integrated. This creates two main issues:

• Valuable insights are lost.
• Data siloing lead to duplicate work and errors.

You have to find a balance between data security and accessibility by implementing proper data governance policies and utilizing secure platforms for data sharing.

Lack of data literacy

Of course, data professionals will be able to manage these things. But the trap a lot of organizations fall into is not hiring enough data professionals, or underestimating the value and potential of data within their organization.

At every company, the time to start hiring data professionals and integrating more advanced tools will be different. And the amount of team members required to run your data operation will also vary. Pay close attention (and get feedback from your team) on the increase in data volume/complexity, decision-making challenges, and operational inefficiencies.

The Negative Impact of Data Silos

When you have data silos, the problems that come soon after are significant.

Lack of knowledge access hinders decision-making.

Let’s say your company’s sales team and marketing team use separate databases to access the same customer data. In this scenario, there is no single source of truth, meaning that if one record is changed in one database, that change will not be reflected in the other.

And in many retail companies, point-of-sale data is stored separately from ecom store data because transactions are processed through completely different tools. But, you won’t have complete sales insights unless you’re able to combine the two.

When your data is incomplete or access is limited for certain team members, it’s a lot harder to make high-level decisions about sales, marketing, pricing, product dev, budgeting, and just about every other important aspect of your business and its future.

Operational-level issues start to crop up.

Data silos also create operational-level issues that can have a negative impact on your business. For example, data may need to be manually transferred between systems or databases, which is not only time-consuming but also increases the chance of human error. And if storage and processing are redundant, you might have duplicate data in multiple systems.

Collaboration takes a big hit, here. According to Panopto’s 2024 Workforce Training Report, approximately 51% of organizations report that employees spend an average of three hours per week searching for necessary information, with an additional 26% spending six or more hours weekly on such tasks. That’s time your team isn’t able to get tasks done together.

Costs start to increase across the board.

When you start having issues that affect your operational efficiency, it gets expensive. Companies lose 20-30% of revenue annually due to inefficiencies stemming from data silos, while outdated data costs small-to-mid-sized businesses over $15M per year.

Businesses need clean, reliable data to set goals and make decisions. Scattered information leads to inaccuracies, bottlenecks workflows, and slows decision-making, all of which ultimately impact the bottom line.

Errors begin to impact the customer.

When data needed for your customer-facing departments isn’t accessible, it can impact the customer experience. For example, when you’re customer success team doesn’t have access to all the sales information for each customer, they can’t present them with personalized upsells and cross-sells, remind them when it’s time to renew, or respond adequately to service inquiries.

This is an even more serious consideration in the healthcare space. When patient data is stored in disparate systems across various departments, it can become inconsistent over time. For instance, if medical data on the same patient is stored in different systems, this data can become out of sync, leading to potential issues in patient care.

Innovation capacity takes a hit.

When you don’t have the insights to know where the market’s heading, product innovation suffers. All modern businesses have to adopt a customer-led growth strategy, at least to an extent, because people’s needs change, and your product needs to change with it. And that means having continuous access to:

• Sales insights per product, region, and other criteria
• Customer feedback and sentiment analysis
• Support tickets and inquiries by product
• Renewal rate and churn rate analysis per product
• Upsell/cross-sell opportunities and performance
• Product usage metrics and adoption rates
• Competitor and market data
• Qualitative customer insights, like feature requests and product reviews

To get all of these things, your sales, marketing, and customer success data has to be centralized. Otherwise, you won’t have a complete picture of who your customers are, what their pain points are, and what they’re looking for out of your product(s).

How to Break Down Data Silos

Digital transformation is a major undertaking, but it’s the only way to break down data silos and operate at your company’s true capacity. Updating your systems, making sure they integrate with one another, and educating your team on best practices are the necessary steps to eliminating silos within your organization.

Here’s a look at the tools and strategies you need to get that job done:

Data governance

Data governance is essentially an umbrella term for “how you manage data,” and it encompasses everything from setting data standards to protecting customer information. It establishes the rules and processes for data management to ensure its availability, usability, integrity, and security.

To create a governance framework:

• Define your company’s data policies and procedures.
• Assign data stewardship roles ensure accountability for data management.
• Develop data standards and guidelines for creating, collecting, storing, sharing, and using data.
• Implement tools for data quality control.
• Establish procedures for access control, data sharing, security, and compliance.
• Create a disaster recovery plan for data loss and backup strategies.

You’ll also need to have a plan for regular data audits. That way, you can identify areas where governance falls short and establish a continuous improvement process.

Data integration

Data integration tools like Airbyte, Informatica, and Talend facilitate the extraction, transformation, and loading (ETL) of data from various sources into a unified repository. They streamline data consolidation, ensuring consistency and accessibility across the organization.

You can use them to build data pipelines that automated the flow of data from one system (the database or API) to the next (the data warehouse).

Data warehousing and data lakes

A data warehouse is a centralized repository that stores data from different sources in one place. It allows you to analyze and report on your business data seamlessly.

Data lakes are a storage facility for big data. Unlike data warehouses, they store structured, semi-structured, and unstructured data from multiple sources in its raw format. It best supports machine learning, real-time analytics, and data exploration.

Both play a crucial role in organizing and consolidating data. While data warehouses provide a structured view of the data for reporting and analytics, data lakes offer more flexibility in storing and analyzing large volumes of diverse data.

Data visualization

Data visualization tools like Tableau, Power BI, and Qlik allow you to create interactive charts, graphs, and dashboards to present your data visually. They help your data team communicate insights to non-technical stakeholders in a polished, easy-to-understand way.

Visualizing your data can also help you spot patterns and trends in your data that might not be apparent from simply looking at the raw numbers.

Cloud-based solutions

You need to have a centralized data platform that can scale with your business. That means your data has to be hosted on the cloud. Cloud-based solutions like AWS, Google Cloud Platform, and Microsoft Azure offer scalable storage and computing resources for data processing.

Additionally, cloud services provide various tools and services that make it easier to extract insights from your data. For example, AWS has Amazon Redshift for data warehousing and Amazon Athena for querying data directly from your cloud-based data lake.

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