According to a 2023 Atlan survey, business leaders spend less on self-service than governance and data quality. For 60% of data leaders, information governance and quality are key focus areas.
Despite the industry hype about user autonomy, data integrity has always been at the top of enterprises’ ask list. It’s even more critical, with embedded analytics being the norm. This article discusses the next best thing in application integration, the semantic layer, and what to consider when opting for one.
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What This Article Covers
- What Is a Semantic Layer?
- Pain Points
- Benefits
- Decentralized Analytics
- How To Build A Semantic Layer Architecture
- Key Considerations
- Next Steps
What Is a Semantic Layer?
A semantic layer is a representation of corporate information in business terms. It gives independent information access for application integration, product development and analytics. If you can access your organization’s information with standard business terms, you’re using a semantic model.
Modern business intelligence (BI) tools have metadata management capabilities, enabling users to create semantic models that address big data integration through virtualization. It’s less draining on the system.
Besides, channeling siloed information through the semantic model provides a single data governance and security control point.
A semantic layer rests over the data warehouse or lakehouse, serving metrics, hierarchies and complex calculations on demand. It abstracts the database schema, so you don’t need to know the data structure unless necessary.
The rules, structure and query languages for creating its data elements are called semantics. This layer doesn’t contain any data, only metadata. As an end user, you don’t see tables and relationships but lists with hierarchies and relationships.
Having a common standard to name business entities makes enterprise reporting and big data analytics easier. It’s a win-win for self-service BI.
A semantic model can be specific to your BI tool, so you might need to tweak it or build a new one if you switch platforms. On the other hand, a universal semantic model has an open architecture to connect many applications on demand.
A semantic data model solves information access and quality issues common with unregulated information use. Self-service exploration and analysis can generate ambiguous business terms open to interpretation, compromising consistency.
A semantic layer solves it by providing a single source of truth — a transparent medium of information access so there’s no duplication.
- It serves as the sole source of truth by aggregating information.
- It processes queries at lightning speed.
- It’s highly performant with autotuning capabilities.
- It has information security and governance baked in.
Pain Points
With the changing market landscape, industry expectations have changed. Businesses expect new BI systems to integrate external data and provide direct access, hence the surge in embedded analytics tools.
Visualizations are prime examples of the need for application integration. Getting the complete picture entails aggregating information from all related domains. CRM, ERP, inventory and sales systems, or the finance department might want sales information without going into the nitty-gritty details.
Different users may use disparate BI tools, a common scenario with overlapping tech stacks.
Your requirements are twofold — you want self-service analysis and a common medium to integrate this information and present it to you. And it’s business-critical.
Without centralized analytics that support self-service, enterprises struggle with performance issues and low confidence in data.
- A Glut of BI Tools: The larger the organization, the more likely you’ll have a mishmash of analytics systems. Regular product upgrades make enforcing a single source of truth an uphill task. Many people working with the same information in silos can lead to inconsistency and duplication.
- Processing Demands: Moving information across systems places processing demands on your infrastructure. Getting it into your BI tools and writing it back to the repository is effort-intensive.
- Performance Issues: Obtaining the desired insights can be a time-suck if you don’t know where to look. Gaps between user understanding and underlying information can slow down downstream processes like decision support.
- Low Confidence in Data: Multiple copies of the same information is a governance nightmare. It can hurt your reputation and bottom line. Quality suffers, and customers may lose confidence in your data.
Using data catalogs boosts performance. But can they solve the issue of information ambiguity? Not exactly.
The field Location Code from two or more domains can have two meanings. It might indicate the city zip code for your store or its assigned identifier.
An information catalog will treat these as two data entities in two locations and point you to them. It won’t tell you they are the same — it doesn’t have the information.
Data warehouses haven’t exactly solved the issue of redundancy. They allow some table duplication to avoid extra joins that consume processing power. Consider two tables — EMPLOYEE_BY_DEPT and EMPLOYEE DETAILS. Both include the EMP_NAM field to avoid additional joins when giving query responses.
A semantic layer removes such redundancies with built-in business logic.
Primary Benefits
This additional layer over your data warehouse makes your information centrally accessible, high-quality, and securely exposed to many BI tools.
- Maximize ROI: Collaborating is more manageable when everyone understands business information similarly. Workflows function as planned, and using the same business terms makes reporting and analysis a breeze.
- Integrate Any Tool: A universal semantic layer is flexible, and its open architecture enables connecting to the BI and analytics system of your choice. You could design it in a tool like Intellicus or AtScale, populate it with information in TIBCO Data Virtualization Studio and connect it to Power BI, Tableau and Excel. More on it ahead.
- Boost Performance: Do everything quicker — generate reports, shoot off queries at the moment and get instant responses within your business systems. Virtualizing business information enhances performance by not burdening source systems with constant data demands.
- Analyze Independently: Semantic modeling frees users from IT dependence by giving standard business terms for data entities. It supports user autonomy with high information quality, thanks to governance rules coded in the business logic.
Decentralized Analytics
Information warehouses centralize information access but can be challenging to maintain. Translating enterprise requirements into technical specifications requires a business analyst. And IT needs to build the data structures when new information becomes available.
It makes scaling the warehouse a challenge. For users, the difficult part is navigating the numerous warehouse tables.
Pivoting to the other end of the spectrum — total independence from IT — doesn’t work great either. You risk losing transparency with everyone doing their own thing. Users might create different versions of the same information, eroding employee and client trust in your business.
It’s why enterprises deprioritize self-service – they don’t see value in it. According to the Atlan survey, only 34% of business leaders consider self-service a priority.
A universal semantic layer offers a middle ground by decoupling domain semantics from the standard business logic. Consider the hub-and-spoke model, with the semantic model at the center and the various domains forming the spokes. The semantic code includes the structure and language matching with every domain in your business.
Decoupling allows you to plug and play into any BI platform without hard-wiring the business logic.
If it’s Power BI, your semantic model will converse with it using DAX (Data Analysis Expressions). The business logic will use MDX (Multidimensional Expressions) for Excel and SQL (Structured Query Language) for Tableau.
An effective hub-and-spoke model removes the need to know everything about other domains. Finance can combine information from sales using the standard business logic without in-depth knowledge or assistance.
Watch this video to learn more.
How To Build a Semantic Layer Architecture
Connecting to files, websites and applications over the Semantic Web entails following Semantic Web rules.
- Define Business Requirements: List the necessary use cases, keeping the current pain points in mind. Factor in the solution’s business value and decide upon a definition of success.
- Map Data: Build logical models for mapping information from the source to target systems.
- Include Interoperability: Use semantic web standards to link information across files, applications and websites.
- Define taxonomies, metadata and information catalogs to add business context to your information. Decide which terms will represent the data entities, and lay down the rules for making the information machine and human-readable.
- Establish relationships and hierarchies using mappings and schemas.
- Determine which query language will communicate with various systems and file formats.
- Incorporate Smart Storage: Mature semantic implementations may include graph databases for faster information access, so consider incorporating them into your model. Assess your use cases to decide whether you need data cataloging and a text analytics tool.
- Connect to Analytics Applications: Establish connectivity with your internal domains and preferred BI and analytics tools.
Key Considerations
Flexibility is key to support developers, analysts and data experts. It should connect to your preferred BI and analytics platforms using compatible query languages and data structures.
What else should you look for?
1. A universal semantic layer must have a virtual layer with the base data objects mapped to your KPIs and complying with business rules. A decoupled semantic model is scalable and doesn’t tie you to one BI tool.
2. It must support predictions and anomaly analysis with time-based calculations.
3. It should adhere to field-specific formats, like the dollar ($) sign, for monetary values but not for text fields. The same goes for data-time formats and numeric entities.
4. Version control is necessary for period-over-period analysis and ensures you don’t lose the previous business logic. Business rules are dynamic, and comparing previous and current information for an apples-to-apples comparison may require changing them temporarily.
5. A semantic model must cover the business logic with multidimensional relationship views and data aggregation.
6. Data virtualization is a must and removes the need for data migration every time a new storage trend emerges.
7. It should support visual data modeling to make the business logic easy to understand.
Visual modeling enables intuitive data exploration and analysis. Source
8. It should be highly performant with autotuning capabilities to handle fluctuating workloads and user concurrencies.
9. Information security is necessary with authentication and authorization protocols like Windows Active Directory, LDAP (Lightweight directory access protocol) and OAuth.
Restricting sensitive and PII (personally identifiable information) access to select users is business-critical. The semantic code must consistently log access by any BI tool, program or API (application programming interface.)
Next Steps
Accurate data is the North Star for enterprises, guiding them to long-term success and financial gains. And they can have it without sacrificing user autonomy, thanks to the semantic layer.
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How did a semantic layer work out for you? Which capabilities came through? Let us know in the comments section below.