Using data to make decisions is actually really hard. Yet nearly every tech company, business, and team touts “data-driven” as the de-facto way that they operate. In practice, this intention to be data-led is often just aspirational—only about a quarter of organizations report that they are actually data-driven, according to Harvard Business Review.
It’s easy to see why organizations strive to more effectively use their data. Better data leads to better insights, which can increase efficiency, productivity, customer satisfaction, competitive advantage, and ultimately, earnings. In fact, organizations with a data-driven B2B sales approach report above-market growth and EBITDA increases around 15% to 20%, according to research from McKinsey.
So with all the benefits of connected data, why do so many organizations struggle to overcome data silos? Let’s explore the meaning of data silos, why they occur, and best practices for eliminating them.
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The term “data silo” is used to describe information that’s isolated in databases, applications, or departments within an organization. It references the silos used in the agriculture industry— just as physical grain silos separate and store different types of harvested crops, data silos isolate different sets of data. But without good airflow, the grain in silos can mold and become useless. That’s exactly like your data. When it’s separated in different systems and applications without open connectivity, it becomes a hindrance to your organization that ultimately decreases its value.
Originating in the early 1990s, the term is now applied more broadly to also describe situations where data isn’t being shared effectively across an organization.
Data silos have become more prevalent as organizations continue to rely on more technology and collect more data. As you work to improve your data architecture and pave the way for success with automation, it’s important to have a plan for addressing them.
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Siloed data is often the result of disparate systems and technologies, a lack of data governance policies, and cultural barriers to sharing data. This has been fueled by the rapid adoption of cloud computing and SaaS applications, which can create isolated data sources that are difficult to integrate. Additionally, the rise of big data and the Internet of Things (IoT) has led to an increasing volume, variety, and velocity of data, making it more challenging to manage and share.
Data that’s siloed in disconnected systems makes continuous improvement incredibly difficult, resulting in processes and experiences that aren’t being optimized to their full potential.
With the rapid adoption of technologies like IoT and AI and the demand for more tailored user experiences, it’s more important than ever before to have good data management practices. That’s because the better and more connected your data is, the better the output from the technology that it feeds.
Companies that will realize the most value from these capabilities are those that already have good data practices in place. Consider a technology like AI: McKinsey found that companies who attribute 20% of earnings to AI are also much more likely to have data practices in place to support it.
Organizations with a strong data strategy have a range of opportunities ahead and often follow some of these best practices for eliminating data silos:
The first step to eliminating data silos is to identify them. Data discovery tools with pre-built connectors can vastly improve this process, helping you quickly uncover and connect data sources.
A data governance framework sets out policies, procedures, and standards that guide how data is managed within an organization. Implementing a data governance framework ensures consistency across the organization and establishes best practices for how data is collected and shared.
Data management solutions can help organizations break down data silos by connecting disparate data sources and enabling data to be shared across systems. Popular data management technologies include data warehouse, data lake, data mesh, and data fabric.
[ How does data fabric differ from older technologies and what benefits does it deliver? Read also Data Fabric vs. Data Mesh vs. Data Lake. ]
Eliminating data silos requires a culture of collaboration, where teams work together to share data and insights. You can encourage this by incentivizing cross-departmental communication and training employees on the importance of using data in their work.
Establishing roles and responsibilities for managing data—including defining who is responsible for data quality, security, and sharing—is a key component of good data management that promotes effective data sharing across an organization.
Even with the best data management practices in place, you’ll still need the support of tools and technology to connect your data and turn it into insights that help you take action.
A data fabric is the best data management option for organizations that want to build real-time applications with a 360-degree view of their operations. Data fabric takes the unique approach of keeping data where it is. Rather than migrating or trying to get your data in one place, a data fabric sits on top of your systems and stitches them together with a virtualized data layer.
This power to connect disparate data sets means that data is no longer hidden in silos, giving you a complete view of your data that allows your organization to truly become data-driven.
Data fabric also plays a key role as your enterprise works to scale success with hyperautomation technologies. You can’t automate complex business operations without the support of a strong data architecture. That’s why data fabric is a must-have in a modern platform for process automation, which applies a wide range of technologies, including robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, and artificial intelligence (AI) to improve business processes end to end.
Learn how to solve your data silo problems and speed up innovation. Get the eBook: The Data Fabric Advantage.