Some might list their fears as the following: big spiders, big bills, and big data. Data tends to be an intimidating subject for many. Adding “big” in front of it makes it sound even more complex. Let’s strip away the scary “big” and focus on the main element—data. To understand and reap the rewards of data, one should learn and practice data literacy.
Data literacy skills can vary wildly due to generational differences, varied educational backgrounds, or diverse interests. The authors of this article are a great example. Caroline learned about data in college, on a laptop. Jeremy studied computer science and was fortunate if he saw a laptop in college. He also had to carry data around on a 1.44 MB floppy disk, a laughably small amount of data today. Even people of the same generation will have differing educational experiences relating to data and applications. As leaders, it is important to make sure your organization has the needed data literacy skills to leverage your data and help your business thrive.
Data literacy involves effectively working with data in the following ways:
- Understand data and its context
- Analyze data to extract valuable insight
- Communicate data findings clearly
In this two-part series, we’ll define and explore these key elements of data literacy. This first article will focus on understanding data for your business and strategies to consider.
How to Understand & Summarize Operational Data
Understanding your organization’s data can feel like drinking from a firehose, but take solace in the fact that you live and breathe data every day. You generate data through operations, and you interact with data through various applications, generally in very small, very targeted ways. Working with data simply means working with the same information you already know, just at a larger scale. To begin to understand it, you first need to see the data in front of you.
To do this, you must first identify how to retrieve this data en masse. You can typically export data from your enterprise resource planning (ERP) system in several common formats, including Excel, delimited files, and PDFs.
Methods for Exporting Data
How you export your data depends on the size of the data set you’re exporting and for what purpose:
- Excel has a record limit of 1,048,576 rows. If the data set you export has more records than this, you risk losing necessary data in the export. In addition, the data type functionality of Excel can alter the display of the values, which can produce data analysis.
- Delimited files utilize punctuation to denote where a new column begins. This delimiter is commonly a comma, tab, or pipe symbol. Important note: A comma-separated values (CSV) file can be opened in Excel, but a CSV file is not an Excel file. Unlike Excel files, CSV files can contain a virtually unlimited amount of data. CSV files have the rawest, unaltered version of the data. This is often the most reliable export type.
- PDFs are commonly used to export readable information from a system but are usually a poor choice for extracting data from that system. Data can be difficult and sometimes impossible to extract from a PDF.
- More advanced data extraction techniques may suit your needs, and your organization may have data professionals or even a center of excellence to help you extract data using these methods. Some examples include reading data directly from a relational database; using an application programming interface (API); or more advanced/capable file formats, such as files that include location data, images, or other binary data.
The Significance of Validating Data
Next, you’ll want to validate the data exported from your ERP system. Check that the total number of records in the export matches what is in your system and confirm that totals (sums of numerical data) match the expected total for that category, e.g., total payments. It is critically important that the data fed into any reporting or analysis is accurate and complete.
Real-life example: During COVID-19, a business responsible for reporting contact tracing in England transferred data from a CSV file into an Excel file. As a result, 16,000 reported cases of COVID-19 were lost due to oversight of the record limit. Former Shadow Secretary of State for Health and Social Care of the United Kingdom Jonathan Ashworth noted, “Thousands of people [were] blissfully unaware they’ve been exposed to Covid, potentially spreading this deadly virus at a time when hospital admissions are increasing.”1
Understanding Data Privacy Rules
Another important aspect of understanding your organization’s data is data privacy. According to a data breach report,2 flight charts, navigational materials, crew’s personally identifiable information, and software source code of Pegasus Airlines were all left unprotected on the internet in 2022. Pegasus Airlines is a Turkish company, and this breach violated Turkish law, with a maximum fine of $183,000, but the reputational costs and potential litigation exposure could’ve been far higher. The cause: employee negligence and human error. A mistake was made when configuring the entity’s cloud environment, leaving sensitive data unsecured and without password protection. Any time data is exported, transferred, moved, or altered, it is at risk unless proper controls are in place. In this example, data wasn’t moved or altered; it was just kept in the wrong spot. This highlights the importance of understanding data privacy rules, as oversights like this can potentially lead to significant financial, legal, and reputational consequences.
Getting Comfortable With Data
The logistical consequences of poor data literacy can be significant. A lack of drive to improve data literacy can hinder your company’s growth. According to mathematician Clive Humby, “Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity. So, data must be broken down, analyzed for it to have value.”3
Some are afraid of change due to the unknown. Getting comfortable with your data and exploring data-driven decision making is an excellent start to becoming data literate. Familiarize yourself with what information is available from your system, how it is exported, and account for its sensitivity. Even if you feel behind in becoming a data-driven business, being open to and pursuing data literacy can help you put your best foot forward.
How Forvis Mazars Can Help
Our Analytics team at Forvis Mazars offers tailored technology solutions focused on transforming data into relevant, consumable, and actionable information. Our experienced data analysts use predictive analytics, machine learning, and AI techniques along with powerful data visualizations that help enable users to understand and analyze complex data sets. For more information, see our in-depth analytics services and connect with a professional at Forvis Mazars today.
- 1“Excel: Why using Microsoft’s tool caused Covid-19 results to be lost,” bbc.com, October 5, 2020.
- 2“7 Examples of Real-Life Data Breaches Caused by Insider Threats,” syteca.com, February 28, 2024.
- 3“Data as The New Oil Is Not Enough: Four Principles For Avoiding Data Fires,” forbes.com, March 4, 2022.