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

If you had to wager a guess, how many systems and software applications does your organization use? A lot, right? According to a recent 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 rising in popularity. But as the number of systems your organization implements increases, so, too, does the number of data silos.

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, our Data Products General Manager, 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, it 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. In many cases, the data that lives in these silos is incomplete, inconsistent, and out-of-date. And that makes it difficult to integrate the data across the business. We experience the result of this dirty, inconsistent data as consumers. Think about your insurance policies. You may have multiple insurance policies with a single provider, but each policy may have a slightly different name or address. The 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 the data likely lives outside your firewall. Think about it. 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.

Break Down Data Silos with a Data Product Strategy

Eliminating data silos is not an easy task. Many organizations implement a data warehouse or a data lake in an attempt to consolidate data and make it more accessible for decision-makers. And while these solutions have advantages, if you don’t fix the underlying issues with your data, you will struggle to realize the results you expect.

Instead, organizations must create an enterprise-wide strategy for integrating data across their business. And that strategy must include treating data as a strategic asset and managing it like a product.

Managing data like a product means implementing a data product strategy that brings structure to the ownership, processes, and technology needed to ensure the organization has clean, curated, continuously-updated data for downstream consumption. Data product strategies define key objectives and metrics, such as increasing competitiveness by improving the customer experience or creating product differentiation. They deliver value by enabling companies to drive growth, save money, and reduce risks.

A data product strategy, delivered through the design and use of data products, is a surefire way for organizations to drive greater value from their data. Data products elevate the value of data as an asset by allowing everyone in the organization to discover it and consume it. And, they help to break down data silos by integrating data across multiple systems and sources, including third-party data, in order to provide a holistic view that everyone across the business can use for decision-making.

The Right Technology to Support Your Data Product Strategy

Having the right technology to support your data product strategy is critical as well. Tamr provides integrated, turn-key data product templates that combine machine learning, a low-code/no-code environment, and integrated data enrichment to streamline operations. With Tamr’s data product templates, you can deliver a consumption-ready set of comprehensive, clean, curated, and continuously-updated data sets for key business entities.

Because Tamr is the data product platform, it provides all the capabilities organizations need to deliver data products. Designed for consumption and ready for use, Tamr data product templates deliver high-quality, trustworthy, and accessible data that people across an organization can use to solve business challenges. Using data products, organizations can eliminate data silos and deliver data sets that humans and machines can consume broadly and securely across an enterprise.

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