Organizations are collecting information at an unprecedented rate. Website analytics, CRM data, mobile app data, social media engagement metrics, marketing automation data, and customer feedback – the list goes on and on. But stockpiling books doesn’t make you a scholar, and hoarding data doesn’t guarantee insights. The true value of data lies not just in its volume but in its usability.
For many businesses, extracting insights from data can be a complex and time-consuming process. Data silos, inconsistencies, ambiguous definitions, and overly-technical or inconsistent language can create all kinds of roadblocks and bottlenecks, hindering practical data analysis and utilization. Semantic layering offers a powerful solution. It creates a unified and consistent view and vernacular that is applied to source data from different platforms with different definitions. This creates a consistent data language, making it easier to find, understand, and apply data for better decision-making.
What is semantic layering?
Picture a cool carbonated beverage. Yes, it’s a delicious treat, but it’s also a linguistic minefield. In the South, it might be universally called a “Coke,” regardless of whether it’s actually Pepsi or Coca-Cola. Head to the Midwest and it’s a “pop,” while those on the coasts tend to opt for “soda.”
Now imagine your internal teams talking about a new business prospect, which happens to be a new business unit with a huge, global company where you’ve previously worked with other business units. Your marketing team might describe them as a “prospect.” But they may be a “client” to your sales team or even a “counterparty” to your finance team, with all those different terms appearing in different systems to refer to the same company. Sound familiar?
This vocabulary confusion is just like the chaos you might encounter in data without a semantic layer. Different data products and systems, like different regions, use their own definitions for what may be similar or related data elements. A semantic layer maps data with diverse definitions from a variety of sources into familiar business terms to create a single, unified, and usable view of data across an organization. This breaks down silos and allows everyone to speak the same language, fostering a more collaborative and insightful approach to analysis.
For example, a Google Analytics report output may use the metric “avg_session_duration_seconds,” but a HubSpot report of the same landing page engagement may refer to that metric as “time on page.” Semantic layering would recognize that this is the same metric with different labeling and deem both metrics “Average time spent on the site,” for example, to make it consistent for individual users.
Here’s what semantic layering aims to establish:
- Standardization: Semantic layers apply easy-to-understand terms to complex and inconsistent metric nomenclature. This ensures everyone within the organization speaks the same “data language” no matter the source system, eliminating confusion and wasted time deciphering cryptic codes.
- Business-friendly representation: Semantic layers translate technical data structures into business-friendly language. This allows users without a deep technical background to easily understand and interact with the data, which is especially important for data literacy across your organization.
- Contextualization: Because semantic layering speaks to individuals in a common, understandable language, it enables individuals to draw connections across data and see the bigger picture in order to form a cohesive narrative across the data and unlock deeper insights from their analysis. It also helps to reduce the risk of confusion or misinterpretation of data, for more sound decision making.
Semantic layering takes complex data from your systems and translates it into clear business terms everyone in the organization can understand. This standardized data language makes it easier to analyze and gain valuable insights.
Why is semantic layering important?
Your business relies on data to drive informed decision-making and fuel growth so being able to access, interpret, and leverage that data in a meaningful language is key. Semantic layering is a cornerstone of your data strategy for several key reasons:
Data jargon made familiar
A semantic layer removes technical barriers by translating complex jargon into relatable terms, allowing everyone from marketing specialists to financial analysts to recognize and act on the data they need, regardless of their technical experience.
Enhanced data governance
Semantic layering promotes consistency and quality with data nomenclature. By establishing clear definitions and rules it minimizes the risk of errors and inconsistencies that can plague traditional data analysis. It removes ambiguity and the need for individual interpretation of metrics as they are defined from an original source. This further reduces errors and increases the accuracy of the findings and their usability.
Efficient data onboarding
With a standardized language in place, businesses can apply semantic layers to new data sources, ensuring consistency from the beginning. This allows you to adopt and take action on new data quickly.
Deeper insights unlocked
Semantic layering ensures consistency and understanding by establishing a common language, which empowers us to more efficiently uncover meaningful patterns and trends within the data. This facilitates data-driven decision-making across all levels of the organization.
Learn more about Tallwave’s data strategy and analytics services.
Semantic layering offers a strategic investment in data understanding
Semantic layering isn’t just a technical solution – it’s a strategic investment in the future of your business. By making your data terminology consistent and understandable, you empower your team, improve decision-making, and position yourself for long-term success in the data-driven world.
