What As a Data Mart? And What Challenges Does It Solve?

Business leaders are always under constant pressure to improve operational efficiency, maintain product quality, and optimize financial performance. Good data and, more importantly, good metrics are important for effectively addressing these challenges.

A critical yet often underutilized tool for achieving reliable business metrics is the data mart. In this article, we will explore what a data mart is and how it functions. We will also define what is a “good metric” and examine how data marts enable business leaders with metrics that enhance visibility, control, and predictability—ultimately improving business performance.

What Is a Data Mart?

A data mart is a specialized data repository typically stored in a SQL database designed to serve the specific needs of a particular department, business unit, or function. It contains data relevant to its intended audience, structured to provide streamlined access to insights.

Key characteristics of a data mart include:

• Focus: Designed to support specific business areas, such as top-line (orders, sales, backlog, standard cost & margins), purchasing (supplier performance, material productivity), inventory (aging, excess & obsolescence), and others.
• Efficiency: Simplifies access to key data, reducing the time needed to generate insights. This efficiency stems from having all the logic centralized in one place rather than spread across various reports and dashboards.
• Usability: Usability is high because standard connections allow users to utilize familiar reporting tools like PowerBI, Tableau, or Excel. This ensures accessibility, making it easy to query data and present it in user-friendly formats.

By organizing data into easily digestible segments, data marts bridge the gap between raw data and actionable metrics, enabling business leaders to make informed decisions faster.

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This makes it challenging to benchmark typical business-as-usual travel costs and determine how much can be cut without negatively impacting operations.

Not all metrics are created equal. A good metric stands out by its ability to provide meaningful, actionable insights that align with organizational goals. To evaluate the quality of your metrics, consider the following attributes:

1. Compliant: The metric adheres to industry standards and internal governance policies, ensuring accuracy and reliability. This is especially important for calculations affecting key financial figures such as profitability, working capital, and reserves, where adherence to internal policies ensures consistency and trustworthiness.
2. Usable: It is accessible, easy to interpret, and designed for practical application by its intended audience.
3. Granular: The metric provides detailed information, enabling deep drill-down analysis to uncover root causes and trends. Achieving granularity is challenging but essential, as it enhances the quality of insights and directly influences decision-making through accurate explanations and narratives.
4. Aligned: It supports strategic objectives and connects to critical financial or operational outcomes like the P&L, balance sheet, or product quality. All stakeholders agree on the metric’s calculation and meaning, ensuring organizational clarity and alignment.
5. Comparable: A good metric is consistent across contexts and systems, enabling meaningful comparisons between departments, time periods, or benchmarks. This involves tracking events like organizational changes, sales territory changes, standard cost updates, and currency fluctuations. When changes occur, metrics should be easily reinstated, and different versions of the same metric should remain accessible for comparison across changes.

To illustrate the impact of missing metric attributes on business performance, let’s consider a case study. Imagine a scenario where a CEO asks the CFO to temporarily reduce travel expenses to meet profitability targets. Now, suppose the CFO has to work with a Travel Expense metric lacking the five key attributes. Here’s what happens:

1. Non-Compliant: Travel expenses are incorrectly booked in cost categories, making it difficult to calculate the cost savings that could be generated.
2. Unusable: Details like hotel, flight, and car rental bookings are locked in the travel application, requiring weeks for analysts to compile raw data for a detailed analysis.
3. Lacking Granularity: Without granular insights, such as travel by department or business function, it is impossible to quickly identify cost cuts without risking business operations.
4. Misaligned: With travel expenses not booked in the correct cost categories, business leaders across functions will disagree on the actual figures, wasting time aligning on a baseline.
5. Non-Comparable: Because travel expenses are not easily available, year-over-year analysis becomes difficult. This makes it challenging to benchmark typical business-as-usual travel costs and determine how much can be cut without negatively impacting operations.

Metrics that embody these attributes not only enhance decision-making but also build trust among stakeholders by providing a clear, auditable trail from raw data to actionable insights.

How Data Marts Enable Business Leaders with Good Metrics

Manufacturing companies operate in complex environments where good metrics are critical for visibility, control, and success. Data marts make good metrics possible through five essential activities:

1. Centralized flat tables stored in SQL databases: Data marts are ready to use and provide a simplified format for querying data. These tables streamline access to key metrics by organizing raw data into structured and understandable formats, ensuring faster and more reliable insights for decision-makers.
2. Centralized Metric Calculation Logic: Data marts centralize the logic behind metric calculations, ensuring consistency and reducing errors.
3. Automated Processing: Data pipelines powering data marts fully automate the end-to-end transformation process, turning raw data into trusted and actionable metrics.
4. Integration of Data Sources: Data marts consolidate data from multiple sources, both legacy and modern, into single, auditable metrics.
5. Monitored Ingestions and Transformations: Data pipelines powering data marts are fully monitored to ensure that the metrics business leaders rely on are always available. When issues arise, effective communication is promptly initiated, and resolutions are implemented quickly to minimize disruptions.

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These capabilities make data marts indispensable for driving performance improvements and informed decision-making in manufacturing operations.

Common Pitfalls in Achieving Reliable Metrics

Data has become popular in business, placing significant pressure on data marts and the traditional business intelligence profession. Many so-called data professionals promise quick ROI through complex data science, AI, and ML projects or by promoting revolutionary new technologies often portrayed as miraculous solutions.

To avoid costly missteps and wasted resources on data initiatives that fail to deliver, it is important to recognize how the temptation of quick fixes and over-promised results can mislead companies. Below are common pitfalls to steer clear of when seeking the insights and metrics your business needs:

Excessive Focus on Tools and Technology: While focusing on tools and technology is not inherently bad, it becomes problematic when attention shifts to unattainable promises tied to “magical” solutions. Good data and insights come from strong processes and robust stakeholder alignment—essential foundational work. Moreover, tools are increasingly standardized, so as long as the fundamentals are solid, selecting one tool over another usually won’t yield vastly different results (unless you’re Google).

The One ERP Strategy: This strategy, often proposed by IT leaders in manufacturing with extensive ERP experience, is no longer effective. A single ERP system does not guarantee centralized or unified metrics. Non-ERP data, such as CRM data, has become equally critical and must be included. Furthermore, mergers and acquisitions often result in multiple ERP systems, meaning “one ERP” rarely remains as singular as promised.

Putting Complex Data Modeling in the ERP: Often proposed by IT leaders, this strategy is not always a straightforward decision. In some cases, performing data modeling in the ERP makes sense, but in others, it does not. Customizing and updating data marts is now faster and more cost-effective than modifying ERPs. If your organization operates multiple ERPs, it might be more practical to move certain calculations, including key ones such as accounting reserve calculations, to the data marts layer.

Expecting ROI from All Data Initiatives: This expectation often stems from a misunderstanding of the diverse aspects of data work. Not all data projects are about driving revolutionary business changes; many are essential for day-to-day operations. Without solid data marts, business functions may create their own versions of metrics, often leading to bad practices and inefficiencies, and this is also a good way for numbers to never match properly.

As we have seen, avoiding these pitfalls is important for building a reliable data and metrics ecosystem that supports business leaders. Recognizing these challenges allows organizations to focus on sustainable strategies that drive long-term success.

Conclusion

Data marts provide powerful technology and best practices for delivering “good metrics” that are compliant, usable, granular, aligned, and comparable. These metrics not only improve visibility and control but also enhance predictability—key drivers of business performance.

Integrating data marts into your business performance analytics strategy accelerates access to insights, enabling faster decision-making, more efficient operations, and improved business performance.