Pratik Jain explains how a universal semantic layer gives an extra slice of data efficiency.
Organisations have been stockpiling huge amounts of data, but only a few have developed ecosystems which use it effectively for analytical insights that drive business decisions and strategy. The logjams spring from various quarters, from data residing in silos or having inconsistent definitions to slow processing speeds – all resulting in poor accuracy and trust in the distilled intelligence.
The case for a universal semantic layer
Legacy systems cannot be replaced overnight with smart new data ecosystems, so data engineers are tasked to develop a hybrid architecture. This only adds to the complexities.
A universal semantic layer (USL) is seen as a panacea to several of these challenges. It becomes a standardised framework that ensures data access, consistency, readiness and interoperability across multiple data sources.
Acting at the critical juncture of data storage, processing and visualisation, the USL transforms the underlying data sprawl into a unified and coherent view. At the same time, it transposes raw data definitions and business semantics, making the data view and business analytics highly user friendly.
Instead of working with cryptic and technical field names like prod_id or txn_dt, business users work with product names and transaction dates, making it easier for them to view data in a meaningful way. It provides the necessary business-ready data for analysis. It also integrates with data transformation tools and ETL (extract, transform, load) processes, ensuring that data is consistently modelled and prepared.
Key functions of a semantic layer
Abstraction of the technicalities of the underlying data is vital to making interactions with analytics tools effortless for business users. They can now query, “What was the gross margin for product X over last year?”
The USL must ensure that the derived results are contextual and actionable from their perspective. For this, it must perform some critical functions, including the following:
Data unification
A semantic layer resolves discrepancies between different naming conventions, formats and structures used in different storage locations including cloud warehouses, data lakes or on-prem RDBMS. It harmonises the data, creating a single unified dataset eliminating errors that may occur due to duplication or misinterpretation.
Metric standardisation
Business metrics are often defined differently across teams and applications, leading to inconsistent reports, resulting in lowered trust in using data insights for making critical decisions. A metric such as gross margin could be worked out as total sale value minus cost of goods by the sales team, while the finance team may also discount for inventory carrying costs.
When a semantic layer is used, it eliminates these discrepancies by being a common central repository of all business metrics definitions. Complex formulas used to arrive at the metrics are all pre-defined.
Data governance and access
Access control is key to maintaining compliance with industry regulations and organisational policies. With a USL, data ecosystems can implement centralised control over permissions for view and modify rights based on roles. Sensitive information can be protected as per organisational policies while techniques such as masking and anonymisation can be applied. Moreover, a central audit trail can be maintained for review and risk analysis for potential data breaches.
Self-service analysis
USL enables business users to create custom views as per their roles and context. It offers a highly flexible and agile data ecosystem that supports ad-hoc analysis independently, reducing the need for technical support from data engineering teams. Users may apply new business rules or define calculated fields dynamically enabling drilling down for deeper data insights from the dashboards.
For example, a sales manager may use self-service analytics to explore all the possible factors that are contributing to slowdown of sales in a region or find the impact of a discount scheme on various product lines.
Benefits of a semantic layer
By decoupling the data consumption layer from its underlying storage and processing complexities, a USL allows organisations to embrace new hybrid and multi-cloud environments without disrupting analytical workflows.
Even as businesses evolve, USLs can remain a single repository of key metrics and KPIs, centrally enforce changed governance practices and ensure that reports and insights remain consistent, reliable and readily accessible. By enabling all this, USLs deliver significant advantages, including:
Faster time to insights
A USL accelerates decisions by allowing business users to access and analyse data without requiring deep technical expertise or relying on data teams.
Improved collaboration
When insights are accessible and trusted across departments, organisations can build a culture of data-driven decisions.
Multidimensional insights
Complex strategic decisions need analysis using multiple dimensions, allowing leadership teams to explore insights across various perspectives.
A USL is a perfect platform to power such multidimensional analysis without significant performance degradations. It can be further optimized with AI and ML tools that can design, query and maintain structured data models that map both raw and aggregated data.
A future-ready data ecosystem
With exponential growth in data and the need to leverage it for competitive edge, data ecosystems should be designed for scale, speed and adaptability. A universal semantic layer is the right platform that embodies these features with flexibility to integrate with new data sources as well as different BI/analytical tools.
By Pratik Jain
Pratik Jain is a senior technical architect at Kyvos Insights, a data analytics and business intelligence company. He has been part of the company since its inception and has nearly 20 years of experience in building highly secured and scalable distributed business intelligence products.
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