The Problems with Data Silos (and How to Fix Them)

Editor’s Note: This post was originally published in June 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

If you had to wager a guess, how many systems and software applications does your organization use? A lot, right? According to a survey by Forrester and Airtable, the answer is, on average, 367. 

That’s a lot of apps and systems. But it’s no surprise, really. Systems and applications designed for a specific purpose or department are ubiquitous in modern businesses. But as the number of systems your organization implements increases, so, too, does the number of data silos. 

What Is a Data Silo? 

Data silos are isolated systems or repositories of data that trap information, making it difficult to access or share across departments. In many cases, these silos represent the organization of the company: marketing, product, sales, operations, or R&D teams. In software development, we call this Conway’s Law, an engineering principle that says the software products a company develops reflect the structure of the organization that wrote them. In the data world, Tamr’s CEO, Anthony Deighton, calls it Deighton’s Law: Data reflects the organizational and systems structure of the company that generates it.

So what does that mean in data terms? To put it simply, Deighton’s Law means that the source systems reflect the structure and organization of the business. For example, if an insurance company has five lines of business (LOBs), then they likely have at least five (if not more!) different source systems, each supporting a specific LOB. So when a decision-maker creates a dashboard in their self-service analytics tool, they create a dashboard for a single LOB because that is what the data source reflects. 

The Problem with Data Silos

Data silos are problematic for organizations. Not only do they cause endless reconciliation, but they also amplify duplicate data, hamper collaboration, and create significant barriers to effective analytics and reporting. In fact, according to IBM, “82% of enterprises report that data silos disrupt their critical workflows, and 68% of enterprise data remains unanalyzed.”

In addition, data that resides in data silos is often incomplete, inconsistent, and out-of-date, making it difficult to resolve entities and integrate the information across the business. We experience the result of this dirty, inconsistent data as consumers. To continue our insurance example, you may have multiple insurance policies with a single provider, but each policy may live in a different system and have a slightly different name or address. The insurance provider is unable to recognize that you are the same person, and therefore they communicate with you multiple times as though you were totally separate individuals. 

To complicate matters even further, the best version of an organization’s data likely lives outside its firewall. For example, if you are a manufacturer looking for the cleanest data about your supplier, it’s unlikely that the data you pull from your enterprise resource planning (ERP) system is the most accurate. Instead, the best copy of this known entity would come from the supplier’s website or a third-party data provider like D&B, for example. 

How to Break Down Data Silos

Eliminating data silos is not an easy task. Many organizations implement a data warehouse, a data lake, or a data lakehouse in an attempt to consolidate data and make it more accessible for decision-makers. And while these solutions have advantages, failing to address the underlying issues with your data will prevent you from realizing the results you expect.

Instead, organizations should develop an enterprise-wide MDM strategy to integrate data seamlessly across the business. An MDM strategy brings structure to the ownership, processes, and technology needed to ensure the organization has clean, curated, continuously-updated data for downstream consumption. It defines key objectives and metrics, such as increasing competitiveness by improving the customer experience or creating product differentiation. And it delivers value by enabling companies to drive growth, save money, and reduce risks. 

At the core of an effective MDM strategy is AI-native MDM, a solution designed to deliver accurate, complete, and reliable golden records in real time. Golden records represent a single, authoritative version of your key business entities, consolidated from multiple sources and datasets across the business. By combining the efficiency and scalability of AI with the intuition and expertise of human oversight, AI-native MDM breaks down data silos, simplifies data management, and empowers organizations with more accurate insights, better decision-making, and stronger overall performance.

Eliminate Siloed Data With Tamr’s AI-Native MDM

Tamr’s AI-native approach is fundamentally changing the MDM category. Instead of relying on legacy, rules-based solutions or ones that simply add AI on top of an existing architecture, Tamr is purpose-built with AI at the core. That means every aspect of the solution—from architecture to workflows to user interfaces—all tap into the full power of AI. Using this approach, Tamr eliminates data silos and makes data mastering faster, easier, and more efficient so that companies can produce better outcomes at much lower cost.