Understanding the Data Integrity Management Capabilities of Business Intelligence Tools

Imagine that you are the Chief Financial Officer (CFO) of a multinational corporation. It’s the end of the fiscal quarter and your team is working tirelessly to finalize the quarterly financial reports. Just when you think you are ready to present the numbers to the board, an internal audit unfortunately reveals discrepancies in the financial data reported! The consequences are distressing as incorrect financial statements could mislead investors, result in regulatory penalties, and severely damage your company’s reputation. In today’s data-driven business landscape, scenarios like this underline the importance of ensuring the quality and integrity of financial data for decision-making and compliance. Poor data quality can indeed lead to erroneous financial statements, misinformed business decisions, and regulatory violations, among other costly issues. This is where the role...

Technical requirements

Before we start, we must have the following ready:

• Power BI Desktop, April 2023 or later (no licenses required)
• Tableau Desktop, 2023.1 or later (free trial version)
• Tableau Prep Builder, 2023.1.0 or later (free trial version)
• Tableau Cloud (free trial version)
• Alteryx Designer, 2022.3 version or later (free trial version)

It’s okay if you have not installed these applications yet—we will cover the installation when we discuss these tools in the Reviewing the most popular BI tools and how to get started with them section.

Note

This chapter will serve as the foundation for the hands-on examples that we will be working on in Chapter 5, Using Business Intelligence Tools to Fix Data Integrity Issues, and Chapter 6, Implementing Best Practices When Using Business Intelligence Tools.

Recognizing the importance of BI tools

In the past, finance professionals relied heavily on manual processes to collect, analyze, and validate data. For a long time, spreadsheets were the go-to tools for financial modeling. In addition to this, internal audits were conducted through labor-intensive, manual reviews of financial statements and records. As you can see, this approach was not only time-consuming but also prone to human error. Ensuring data integrity and quality was a constant challenge, as even a single oversight could lead to inaccurate financial reporting and compliance issues. If data inconsistencies were identified late in the process, it became difficult to correct them and increased the risk of delaying critical business decisions.

The advent of BI tools has revolutionized the way finance professionals manage data quality and integrity. These tools employ various algorithms and even machine learning techniques to automatically identify data inconsistencies, validate...

Exploring common data quality management capabilities of BI tools

In the previous section, we discussed how important BI tools are to finance professionals and organizations. Now, in this section, we will focus on various data quality management capabilities available in most BI tools. Understanding these features in depth will help us choose the right tool when dealing with finance data integrity issues.

Data profiling

Data profiling involves examining, analyzing, and producing insightful summaries of data. We covered the general steps for data profiling in Chapter 3, Measuring the Impact of Data Integrity Issues, in the Data profiling using a data quality framework section.

This typically involves the following steps:

1. Defining the criteria for data quality
2. Gathering and evaluating the data
3. Analyzing data quality
4. Identifying and prioritizing data quality issues
5. Creating a remediation plan
6. Tracking and gauging data quality

Note

We will...

Reviewing the most popular BI tools and how to get started with them

In the previous section, we explored the common data quality management capabilities of BI tools. In this section, we will discuss some of the most popular BI tools at the moment and how to set them up on your local machine or personal computer/laptop. We will also cover the relevant features of these tools to improve data integrity.

Microsoft Power BI

Microsoft Power BI is a powerful BI tool that allows users to visualize, analyze, and share data insights across an organization. The tool offers a broad spectrum of features, ranging from basic data visualizations to advanced analytics capabilities.

Figure 4.1 – Sample visualization in Microsoft Power BI

Figure 4.1 shows a sample visualization in Power BI Desktop. Here, we make use of a scatter chart to visualize the data to more easily identify any potential outliers.

Given that we will use Power BI in the following chapter...

Summary

In this chapter, we started with a quick discussion on how BI tools could help finance professionals and organizations in today’s data-driven landscape. We then focused on several data integrity management capabilities of these tools, including relevant features such as data profiling, data cleansing, data validation, data lineage, and data governance. After that, we introduced some of the most popular BI tools used globally—including Microsoft Power BI, Alteryx, and Tableau.

With what we have covered in this chapter, you have learned about why BI tools are helpful for professionals as well as the capabilities and features these tools have to enhance and improve the integrity of the data. In the next chapter, we will continue our discussion on BI tools! We will build on top of what we learned here in this chapter, and dive deeper into how to use various BI tools to solve common data integrity issues for finance.

Further reading

For additional information on the topics covered in this chapter, you may find the following resources helpful:

• 2023 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms:

  https://www.gartner.com/doc/reprints?id=1-2D773G95&ct=230411&st=sb

• Microsoft named a Leader in the 2023 Gartner Magic Quadrant for Analytics and BI Platforms:

  https://powerbi.microsoft.com/en-in/blog/microsoft-named-a-leader-in-the-2023-gartner-magic-quadrant-for-analytics-and-bi-platforms

• Salesforce Tableau named a Leader in the 2023 Gartner Magic Quadrant for Analytics and BI Platforms:

  https://www.tableau.com/blog/tableau-leader-gartner-magic-quadrant-analytics-business

• Alteryx—Data Profiling:

  https://www.alteryx.com/glossary/data-profiling

• Tableau Prep—Use Data Roles to Validate your Data:

  https://help.tableau.com/current/prep/en-us/prep_validate_data.htm

• Tableau—The Benefits and Challenges of Data Governance:

  https:/...

The rest of the chapter is locked