How a Semantic Layer Helps Your Data Teams
The semantic layer may be the key to making the self-service data revolution a reality for everyone.
The self-service BI revolution has never really lived up to its billing. Although user-friendly BI tools have, indeed, made visualizing data easier, they still require a minimum level of database expertise to make them work. This leaves business users struggling to become SQL jockeys, data wranglers, and data warehousing experts -- all before they can create a basic chart. In fact, I would argue that the self-service data revolution is more like a coup that benefits only the database elites.
The universal semantic layer bridges this gap. By inserting a business-friendly interface on top of messy, complicated data, anyone can become an analytics guru. The semantic layer truly brings data to the masses and may be the key to making the self-service data revolution a reality for everyone.
What is a Semantic Layer?
A semantic layer is a tool that provides a unified view of data across multiple sources. It creates a business-friendly, consistent, and understandable representation of complex data, making it easily accessible to any BI or AI consumer. By creating a semantic model on top of their data, data teams can intuitively manage their data, improve data quality, drive trust in data and create reusable data models that can be leveraged across multiple BI tools and applications. A semantic layer empowers data teams to build data-rich applications while enabling easy access to the data.
3 Reasons to Invest in a Semantic Layer
Reason #1: A semantic layer helps you scale data teams
In most organizations, data teams are composed of both business intelligence (BI) developers and data engineers. BI developers focus on creating data products that include multidimensional cubes, dashboards, and reports to help the business answer their questions. Data engineers typically work on building data pipelines, designing and managing ETL jobs, and defining database schemas. In many organizations, these roles may be combined into a single role called an “analytics engineer.”
Regardless of how teams are organized, building these data pipelines and data products is time-consuming and requires a high level of skill in writing queries, working with databases, and modeling data. As a result, managing an increasingly complex data landscape with demanding, data-hungry users is a major challenge for these data teams.
One of the most important advantages of a semantic layer is its ability to substantially automate, simplify, and eliminate repetitive tasks, allowing data teams to scale their data operations. A semantic layer provides leverage for data teams building data products by:
- Centralizing business definitions. A semantic layer serves as the single source of truth for business information, making it easier to manage data across the organization. By moving business definitions out of the consumption tools (e.g. BI tools, Microsoft Excel) and into a central location (the semantic model), a semantic layer enforces consistency of business rules and definitions and eliminates the need for consumers to re-model data in their tools. For example, if a business definition or metric changes, the updated calculation can be changed once in the semantic layer instead of changing dozens of reports in dozens of tools.
- Drastically reducing or eliminating manual ETL/ELT tasks. By defining data transformation rules and business calculations in a semantic model instead of in data pipelines, data teams can significantly reduce or even eliminate manual ETL/ELT tasks required to create a usable reporting and analytics database schema. For example, a semantic layer can eliminate the need to create new reporting tables because they can be defined virtually in the semantic model. By reducing manual work, data teams can introduce new data and analytics to their users more quickly and with higher quality without creating or modifying physical data pipelines.
- Decentralizing data product creation. As organizations look to move beyond monolithic, centralized data teams to a more decentralized approach to data product creation, a semantic layer can play a critical role in enabling these new structures. Decentralized data organizations can take on different forms, from completely autonomous data-mesh-style teams to hub-and-spoke arrangements, where distributed domain teams still own their own data products but conform to the standards of a center of excellence or a central data team.
Regardless of the organizational style, a semantic layer provides a common language for these distributed, business-oriented data teams. By using a semantic model and its sharable components (i.e., calculations, conformed dimensions), these distributed data teams can collaborate, share, and drive consistency of business definitions across the organization.
For example, the marketing team may manage the conformed “campaigns” dimension, which can be used by the finance team in their semantic models to derive new insights (e.g., ROI by marketing campaign). By sharing common business definitions and metrics, teams can avoid duplication of efforts and assure consistency across business domains.
In other words, a semantic model can enable a more decentralized approach to analytics teams by serving as the common mechanism for teams to document, encode, and share their business domain knowledge with each other.
Reason #2: A semantic layer facilitates data-driven applications
Embedding analytics within a broader set of applications is another area where the semantic layer can provide significant value. However, delivering trustworthy, fast and easy to use analytics can be time-consuming and complex. A semantic layer can accelerate data-driven application development by:
- Encapsulating business logic. A semantic layer’s data model frees application developers from creating database tables and writing complicated queries, which can be time-consuming and prone to errors. By encapsulating business logic into the semantic layer, developers save valuable time.
- Providing application-friendly interfaces. A semantic layer provides several application-friendly interfaces to access data, making it easier for developers to build data-driven apps. For example, a good semantic layer includes REST interfaces, SQL compatibility using JDBC or ODBC, and OLAP-friendly protocols such as MDX and DAX. This allows developers to choose the best protocol for the job and gives developers a wide range of programming language options.
- Ensuring consistency in data products. A semantic layer eliminates inconsistency in data products between custom applications and BI/AI tools. Because the same semantic layer delivers analytics to both commercial and open source BI tools and custom applications, users are guaranteed to get the same answers regardless of what tools they use to ask their questions.
Reason #3: A semantic layer supports AI/ML and augmented analytics
A semantic layer can serve as the critical foundation for business definitions, driving the proliferation of AI/ML by:
- Unifying the four styles of analytics -- descriptive, diagnostic, predictive and prescriptive -- in one place. By combining historical data and augmented data in a single semantic data model, users can seamlessly combine analytics to both explain the past and predict the future. New features and predictions that are the output of machine learning can be written back to the semantic layer for use by anyone in the organization.
- Reducing data preparation time for data scientists and ensuring that machine learning models use consistent, certified, historical training data. By taking data modeling off a data scientist’s plate, a semantic layer can substantially reduce the manual data wrangling that can dominate that data scientist’s time. According to Steve Lohr of The New York Times, "Data scientists, according to interviews and expert estimates, spend 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets."
- In addition to serving as a metric store, a semantic layer can also serve as a feature store for data science. By tracking and storing all business metrics in one place, the semantic layer simplifies the sharing of machine learning data with others in the organization beyond just the data scientist.
The Semantic Layer is the Glue
A semantic layer provides a powerful solution for data teams looking to scale BI, embed analytics within a broader set of applications, and capture the value of AI/ML within augmented analytics. By creating a unified view of the data, the semantic layer simplifies data management, improves data quality, and enables data teams to scale their efforts across multiple BI tools and applications. With a semantic layer, organizations can turn everyone into a data-driven decision-maker.