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Three Best Practices for Optimizing the Benefits of Your Modern Data Stack

Here are three ways your enterprise can get the most from your data stack.

• By Elif Tutuk
• January 22, 2024

In the ever-evolving landscape of data analytics, staying ahead of the curve is essential. As the vice president of product at AtScale, I've witnessed the transformative power of data analytics and the emergence of game-changing trends that are shaping the industry. One of the most critical elements for success is having a robust and adaptable data infrastructure. This infrastructure includes data models, semantic layers, and tools that enable businesses to harness the power of their data effectively.

For Further Reading: Data Democratization’s Impact on Users and Governance Five Value-Killing Traps to Avoid When Implementing a Semantic Layer How To Get the Upper Hand on Cloud Cost Management

The universal semantic layer is emerging as a pivotal component within the modern data stack, redefining how organizations can derive insights and value from their data. Here are three ways your enterprise can get the most from your data stack.

Best Practice #1: Empower business units

The days of data being solely in the hands of IT specialists or data experts are fading. Today, businesses are embracing a democratized approach to data. The universal semantic layer enables everyone to become a data product creator, meaning that enterprises are distributing the ability to create data products to the business. As a result, the role of IT is transforming from that of controlling all the data to that of creating and managing platforms that allow business units to create their own data products and ask their own questions about that data. IT is no longer a bottleneck but has become a data enabler for all business units.

The trend toward democratization has a profound impact on the way we work with data. Business units are no longer restricted to predefined reports and dashboards; they can create their own data products, ask their own questions, and derive insights that drive decision-making. This approach fosters innovation and ensures that data becomes a tool for all, rather than a privilege for a few.

Best Practice #2: Welcome all data and analytics roles

Another trend is the inclusivity of data and analytics roles. The modern data stack doesn't discriminate between data engineers, analytics engineers, or BI developers. It accommodates both code and no-code enthusiasts, making data accessible to everyone, regardless of technical background. This also means that anyone can access the data in their BI tool of choice, whether that be Power BI, Tableau, or Excel. The semantic layer is the key to truly enabling that business-friendly representation that works for every user, no matter their skill level or BI platform preference.

This inclusivity empowers organizations to tap into the full potential of their workforce. Data engineers can design the infrastructure, analytics engineers can build advanced models, and BI developers can create user-friendly dashboards, all working together in harmony. The result is a more agile and collaborative data environment where each role contributes to the overall success.

Best Practice #3: Optimize your cloud for efficiency and cost savings

Cloud computing has become the bedrock of modern data analytics. However, with the immense power of the cloud comes the need for efficiency and cost control. This has led to the development of strategies for optimizing cloud spending.

Most organizations have already migrated or are migrating their analytics infrastructure to the cloud. In doing so, enterprises are implicitly trading fixed capital expenditures (CAPEX) for variable operational expenditures (OPEX), making budgeting challenging and cloud computing costs unpredictable. Simultaneously, the cloud’s infinite elasticity enables more users and more queries, driving more analytics demand and even higher variable costs.

A semantic layer can reduce cloud computing costs substantially and make those costs more predictable with automated query optimization. By tracking end-user queries and using AI to cache data and optimize queries autonomously, a semantic layer reduces or eliminates redundant queries and eliminates unnecessary I/O, the primary driver for most cloud data platform computing costs.

Because the semantic layer “understands” the semantics of each query, it can rewrite queries to find the lowest-cost approach that answers end-users’ questions, thereby reducing costs by 3.1x.

A Final Word

As we navigate the data analytics landscape, empowering business units, embracing inclusivity, and optimizing the cloud will be crucial. These approaches empower businesses to harness the full potential of their data, drive innovation, and make informed decisions. They allow our enterprise to move closer to a future where data is not just collected but leveraged to drive innovation, improve operations, and stay competitive in an ever-changing business landscape. In the end, it's not just about collecting data; it's about what you do with it that truly matters.