Data silos are where insights go to die. Everyone knows this, but the retail sector experiences unique challenges when it comes to bridging those gaps. Customers demand more personalization and better interactions with brands, making overcoming data silos one of the most essential pieces for retailers, consumer packaged goods (CPG) companies, and eCommerce brands.
Silos prevent companies from achieving the elusive "customer 360" model, so much so that whispers are growing to abandon it altogether. Gartner itself cites growing regulatory and privacy concerns as well as outdated data collection methods to suggest companies will soon abandon the notion of Customer 360. Instead, we think solving data silos and achieving true Customer 360 requires a new framework for operations: a data operating system.
What are data silos?
Data silos are isolated islands of data accessible to one department of an organization but not to all. Overcoming data silos seems simple — connect data sources to data tools and enable the data to flow freely between the silos. But when companies pull back the curtain on this particular strategy, they're met with significant complexity.
Silos cause teams to lose the context of their data. This is a critical loss. Even when teams do have access to other data silos, the process of connecting them and moving data between them takes too much time and too many system resources to make it feasible for all but the most important analytics. As companies grapple with growing complexity and fragmentation, they'll need to think of their data solutions in a new way. In retail, this proves especially important.
Types of silos matter
Most advice centers around internal silos, i.e., departments not sharing their data with each other. For retail and CPG, the equation also includes external silos.
For CPG, missing siloed data is often housed in second-party data, which comes from retail stores selling their goods. Second or third-party data must first be acquired and then scrubbed to ensure quality and compatibility with existing data systems. This adds difficulty to simply "connecting data sources to data tools."
Team expertise matters
Creating complex workflows requires expertise. If companies are struggling to find the talent to build these complex systems, it puts strain on existing teams and delays solving silos. And even when teams do possess the expertise, the abundance of manual processes involved in ensuring data is secure, consistent, and up to date makes mistakes more likely.
Organizational structure and future growth matter
Silos don't happen by accident. The organizational structure of a company can make it challenging for them to even know what's missing. For example, many CPG companies may not even realize the wealth of data that could be available to them from their partners. In addition, organizations with many subcomponents face a new problem: who decides governance and permissions? There may not be a single governing entity organization-wide.
Challenges with current data solutions
Companies address silos in a variety of ways, but none fully address the short and long-term consequences of restricting information flow.
- Adopting and offloading tools: Small, new companies can afford to scrap every existing tool in favor of something completely different. Much larger, established organizations and enterprises can't offload legacy systems or invest in retraining teams every time a new tool comes out.
- Signing on with SaaS: The opposite approach to building an ecosystem like above doesn't provide the company with complete control over its data. It's risky because security relies on a third party, growth and expansion can be expensive, and SaaS companies sometimes go under.
- Freeing all data and hoping for the best: In no version of reality would this work. Companies have an obligation to ensure data remains secure, but that means different things based on the team member's position in the company and data's projected use.
Choosing a data operating system addresses retail's unique challenges with maintaining data flow, handling second and third-party data, and scale.
The benefits of a data operating system
Companies don't need to replace their current data tools and systems. A data operating system creates an operational layer that brings everything into one system. Users across the enterprise can examine what data is available for queries, where that data came from, and what other pipelines are using that data.
Observability
An operational layer allows an organization to see where data comes from and how it's being used. It reduces duplicate instances of data and helps companies understand the impact that architecture decisions will have.
Governance
Companies can lose track of governance as teams make copies of data. Attribute-based governance controls provide a single view of who accesses what, and those controls deploy across the entire data ecosystem. Data frees up while maintaining a consistent governance protocol that scales with the company.
Agility
One of the biggest frustrations with current Customer 360 solutions is system rigidity. Right now, constructing a 360 view requires tremendous technical commitment, not to mention financial investments. A data operating system doesn't introduce third-party vendors, however. It ties together everything in the company's current stack and adjusts as that stack evolves.
Don't abandon Customer 360 just yet
A data operating system offers a path toward customer 360 by linking different data sources and tools into one central dashboard. It provides visibility into the quality of data, where data came from, and who is using it.
The Modern Data Company's DataOS is such an operating system. Instead of farming out data capability to a third party and suffering from vendor lock-in, DataOS truly frees up data. It allows companies a complete view and takes just hours to deploy. It's time to discover how to achieve the fabled Customer 360 with one future-proof solution.
Discover how DataOS can maximize the value of data and enable full digital transformation by downloading our white paper “A Modern Data Strategy for Enterprises.”
