5 Common Data Challenges & How a Semantic Layer Can Solve Them

In today’s fast-paced business environment, the ability to make quick, informed decisions is a critical differentiator. However, organizations often face significant data challenges when it comes to data-driven decision-making. These challenges typically stem from the absence of a semantic layer, which is pivotal in ensuring data is accessible, consistent, and trustworthy across the enterprise.

This blog explores five common data challenges organizations face without a semantic layer and how implementing such a layer can effectively address these data challenges.

1. Diverse Analytics Tool Preferences Across Business Units

In large organizations, it’s common for business units to prefer various analytics tools. This diversity can arise from factors such as acquisitions, resistance to change, or the different needs of each department. While this flexibility allows teams to use tools they are comfortable with, it also creates silos, where each tool operates on its own data source, leading to multiple versions of the truth.

According to Dresner Advisory Services, many enterprises use three or more BI tools, each contributing to a fragmented data landscape. The risk here is clear: multiple tools can generate various, often conflicting, truths. This fragmentation is further exacerbated by the rapid pace of change in cloud data warehousing, BI, and AI/ML technologies, making it challenging for organizations to maintain consistency.

A semantic layer solves this problem by providing a unified data access framework. This allows business users and data scientists to work with their preferred tools while ensuring data governance and semantic consistency. As a result, data access is streamlined, and the accuracy and reliability of insights are significantly enhanced.

As shown in Fig 1 above, the semantic layer sits between the point of analytics consumption and the data warehouse and data lake. A semantic layer hides the physical complexity from end users and provides them with understandable business terms and user-friendly data instead of raw SQL and database schemas. This level of data virtualization makes data access possible for any analytics consumer.

2. The Challenge of Data Accessibility

While data is abundant in most organizations, accessing coherent and usable data is a different story. Business analysts and data scientists often need help to make sense of data scattered across various sources, such as log files, relational tables, and other data stores. Without a clear understanding of the metadata, these users spend excessive time interpreting data, leading to errors and inaccurate results that negatively impact business performance.

Gartner reports that 87% of organizations have low BI and analytics maturity, meaning that while data may be plentiful, it is not being utilized effectively. A semantic layer can alleviate this issue by enriching the data model with the necessary context, enabling data consumers to make quicker, more informed decisions.

3. The Slow Pace of Data Integration

In today’s business landscape, speed is essential. Waiting for a centralized data team to generate reports and dashboards for various departments is often impractical. There is a clear link between data-driven decision-making and business performance. MIT research indicates that top-performing companies leveraging data-driven decisions see a 5% increase in productivity and a 6% increase in profits compared to their peers.

The rise of big data and cloud computing has empowered business users to take control of reporting and data engineering. While this shift is mainly positive, it also leads to the proliferation of data marts and platforms, complicating data governance. This complexity underscores the need for a semantic layer to simplify and streamline data access and usage across the organization, ensuring that data-driven decisions are both timely and accurate.

4. Inconsistent BI Analytics Across Business Units and Low Data Confidence Among Executives

Using multiple BI tools often leads to inconsistent reports for similar queries. Each tool has its own modeling layer and custom calculations, which can produce varying results from the same data set. Errors in table joins time-based calculations, or formulas further compound these discrepancies, creating confusion and potentially misguided decisions.

These inconsistencies, coupled with unreliable data, can erode trust and hinder decision-making. A semantic layer standardizes definitions and calculations across all BI tools, providing a single source of truth. This ensures consistent, accurate reporting and fosters confidence in data, leading to more informed, reliable decisions across the organization.

5. Poor Query Performance Creates More Data Silos and Data Copies

Modern cloud data platforms have revolutionized enterprise analytics, making it possible to analyze massive data sets efficiently. However, simply having data available doesn’t guarantee fast, interactive dashboards or “speed of thought” analysis. The sheer scale of modern cloud data often results in slow query times, leading to workarounds that break semantic consistency.

AtScale was designed to deliver fast performance without the need for data extraction, unlike traditional OLAP approaches. In benchmark tests using the standard TPCDS benchmark v2.11.0, AtScale’s Acceleration Structures showed significant benefits in speeding up query performance and handling multiple concurrent users while reducing SQL complexity.

The table summarizes the TPC-DS 10TB Benchmark results. You can download the complete benchmark studies here.

Conclusion

The data challenges organizations face highlight the critical role of a semantic layer in modern data-driven organizations. By addressing data challenges related to tool diversity, data accessibility, integration speed, report consistency, and data confidence, a semantic layer ensures that organizations can make informed, accurate, and timely decisions. As businesses continue to navigate an increasingly complex data landscape, implementing a semantic layer will be vital to unlocking the full potential of their data assets.

Over the past several years, organizations have focused their analytics investments on embracing major cloud data platforms. While modern data platforms simplify operations and solve many data challenges, they must address the fundamental challenge of making actionable data available to consumers. Building a business case for investing in a semantic layer strategy should focus on three key value drivers:

1. Increase the value of data by making it easier to combine more data sets, large data sets, and broader data windows in a way that is accessible to more data consumers.
2. Reduce the cost of delivering analysis-ready data by optimizing cloud spend, simplifying data engineering and data ops, and making data consumers more productive.
3. Increase the number of data consumers by making it easier to interact with data assets. Improving data literacy within the organization opens up the flow of data-driven insights.

Implementing a semantic layer is not just a technical enhancement; it’s a strategic move that can transform how organizations leverage their data for competitive advantage.