Trust But Verify: Why Data Quality Is the Key to a Data-Driven Culture

Summary

In this edition, we will be covering the following items:

1. The importance of building robust data quality checks with Power BI

2. What well-built data quality infrastructure can do for FP&A

3. Options for creating enterprise-scale data quality monitoring tools

Worth Its Weight in Gold: Why Data Quality Is So Important

Data quality is your first step to a data-driven culture. Without this foundation, finance transformation can be extremely difficult. With it, your team can unlock new capabilities and stand out with your stakeholders and business partners.

1. Maintaining Trust: The leadership team has good reason to be skeptical. Data quality is a common issue in organizations big and small, and there is a lot of pressure on the executive team to make smart, informed decisions. Once trust is lost, it becomes very difficult to rebuild. So, it’s absolutely critical for the FP&A team to be supported by tooling to get data quality right.

2. Empowering Decision-Making: Once your leadership team and business partners have trust in the data they are looking at, you’ve unlocked the potential for truly data-driven decision making.  

3. Time-to-Insight: When your team can rely on a centralized source of verified data, they can deliver insights quickly and repeatedly. Finance professionals often spend more than half their data sifting through data. Providing them with high-quality, validated data can help your team dive right into the analysis and deliver results for their business partners.

Empowering FP&A with High-Quality Data

FP&A sits at the heart of the organization’s drive to understand the numbers and use them to make informed decisions. That is why data quality is so critical to the FP&A workstream. But FP&A teams also stand to benefit by unlocking new capabilities:

1. Reusable Infrastructure: Once your team has done the hard work of creating a trusted source of truth, you now have the flexibility to use it for additional reporting, analytics, and even traditional Excel Pivot tables. This means that you get to build the infrastructure once but reuse it as many times (for as many use cases) as you like. This also gives your team a seamless transition from building ad hoc reports (for day-to-day analysis) to formalizing those reports with Power BI (when needed), all using the same underlying, verified, datasets.

2. Single Source of Truth: When everyone in your organization knows where to find trustworthy data, you now have the infrastructure in place to build a truly data-driven culture. While virtually every organization strives to create a data-driven culture, that is incredibly hard to do when users don’t have trust in the underlying data and don’t know where to find high-quality information.

Building Your Data Quality Infrastructure with Power BI (and Fabric)

Power BI and the Microsoft ecosystem comes with a growing suite of data quality tooling that can help your team build enterprise-scale solutions to support reporting, analytics, planning, and much more.

1. Configurable DQ Monitoring at Every Interval: Data is always on the move in your organization. From systems-of-record to data warehouses and beyond. The first step to building your data quality monitoring is to implement standardized checks at each interval (each “hop” the data takes). Using Power BI (and Microsoft’s growing analytics engineering platform, Fabric), your team can implement automated checks on data at each point in the pipeline. This means that when data arrives at its endpoint for analysis, it has been thoroughly checked multiple times to ensure consistency and accuracy.

2. Real-Time Alerting & Automated Actions: Similarly, your team can use the same set of tools (Power BI, Fabric) to build real-time alerting. For example, you can have an alert configured to automatically email the right stakeholders when particular data is missing, like a vendor.

3. Centralized Monitoring Across Systems: The value of building this infrastructure is that it can be centralized and reused across different data sources to create a “pool” of trustworthy, validated datasets across the enterprise. Now, your FP&A team can work with business partners across the organization (HR, sales, operations) to produce analysis and support decision making that simply couldn’t be done without this investment.

   1. Not Just FP&A: Your IT teams can also become proactive about data quality, using data quality monitoring dashboards. This “metadata” (data about your data) can be a pivotal support for your organization’s goals to modernize, giving confidence to stakeholders.

4. Dataset Certification: Trust-building is incredibly important, as we’ve already discussed. One of the valuable tools your team can use is dataset certification, where you can establish pre-defined criteria that a dataset must pass before becoming certified. Establishing these stringent criteria can give your users the highest confidence in the data they are using and the numbers they are looking at.

Exercise: What Can Your Data Do for You?

What would your team be able to accomplish if they had access to formatted, reliable, accurate data for reporting, analytics, and much more?

1. What tools could you empower your leadership team with?

2. How could you serve your business partners and stakeholders?

3. What questions could you answer? What capabilities could your team unlock?

In the Next Newsletter

We will learn about best practices for requirements gathering with Power BI for FP&A.