Introduction

Data validation refers to the process in which one verifies both data quality and data accuracy;[1] data scientists implement this practice by creating checks or “guideposts” into a report or system so that input and data maintain their consistency within a database.[2] According to Yale University, data validation matters “for ensuring regular monitoring of your data and assuring all stakeholders that your data is of a high quality that reliably meets research integrity standards.”[3] This is true for most any organization that uses data; given the modern times we live in, that is nearly all organizations. Within the context of corporate data, data validation serves to assess whether the data in question meet the established requirements of a given business.[4] Another organization, Geeks For Geeks, corroborates the importance of valid corporate data with its explanation that data validation is the process of verifying data’s accuracy, structure, and integrity before data analysts use it to inform business decisions and operations.[5]

One data validation service, Gepard, even conjectures that data validation assists businesses with avoiding critical data errors, advancing the flow of product information management, and keeping a watchful eye over the quality of corporate data.[6] Needless to say, the validation of the data within a corporate database is imperative and failure to do so will likely have significant consequences for the business that does not take the time to perform data validation measures. Such important measures, thankfully, are widely available and organizations can choose from a wide variety.

How to Use Non-SQL Data Validation Techniques

Given their indispensable nature, we have enumerated a list of the 10 most highly recommended non-SQL data validation techniques for corporate database management. While SQL comes in handy for data validation, we know that not everyone is familiar with or comfortable using SQL. As such, we have provided a list of techniques for analysts who wish to use resources other than SQL. This list is not in any particular order and its contents is informed by a variety of sources.

1. Spreadsheet Formulas: Our first recommended technique is most likely to be serviceable to companies that are small to medium in size. Spreadsheet Formulas, such as those found in Microsoft Excel, can help with data management prior to its import into its ultimate destination: a corporate database. This technique facilitates the maintenance of preliminary assessments of data format and integrity.

2. Data Validation Software: Data Validation Software such as Talend, a subsidiary of Qlik, is well-suited for any corporation in need of strong, scalable solutions that will not only clean and validate data but also integrate data from multiple sources. Indeed, this tool prepares data for use in reports and/or analytics that a corporation requires.

3. Automated Data Cleansing Services: Informatica, and other Automated Data Cleansing Services, is an invaluable tool for corporations that have immense amounts of data that require consistent cleansing in order to maximize the accuracy of the corporate database. This technique will, in turn, reduce the amount of work required of both database administrators and data analysts.

4. Manual Data Review: Sometimes corporate database management needs human oversight offered by Manual Data Review tools such as Dataedo. This is likely when the data is particularly sensitive or complex. For example, manual data review may be especially handy when conducting data prep for important financial reports or when running compliance checks.

5. Integration with Business Applications (e.g., Salesforce and HubSpot): For professionals who wish for a smooth and reliable data flow between business systems such as CRM (Customer Relationship Management) and ERP (Enterprise Resource Planning), business application integration may be just the right data validation tool. This automated process of data validation is likely to increase operational efficiency within corporations who utilize it.

6. Regular Data Quality Reports: Regular Data Quality Reports, using software such as Tableau, are used for monitoring corporate data quality and reporting on the findings. These reports are helpful in identifying both trends in the data and data issues; both processes are critical for consistence maintenance of the data standards established in a given corporate environment.

7. Use of Web Forms with Validation (e.g., Google Forms and Typeform): This technique provides a “front end” form of data validation. Data validation features on tools like Google Forms and Typeform help to ensure that data collected by means of corporate websites and customer portals are validated prior to entering the corporate database. For example, a data validation feature may catch common errors such as typos in customer (or potential customer) email addresses.

8. Batch Data Validation Tools (e.g. Experian and Clearbit): As the term suggests, Batch Data Validation Tools like Experian and Clearbit are useful when companies need to process large amounts of data. This high-volume corporate data may include important information such as customer details, thus requiring the accurate and consistent nature of batch data validation tools.

9. Workflow Automation Platforms (e.g., Zapier and Make): Zapier, Make (formerly Integromat), and other Workflow Automatic Platforms serve to integrate the data validation into automated workflows within corporations. These tools help ensure that data quality is maximized and maintained during corporate business processes.
10. Cloud-Based Data Validation Services (e.g., Amazon’s AWS, Google Cloud, and Microsoft Azure): Our final recommendation for data validation techniques in the context of corporate database management is Cloud-Based Data Validation Services. With various options to choose from, these services are ideal for corporations embracing the infrastructure of the cloud. In doing so, these corporations are leveraging data validation solutions that are not only flexible and scalable but also conveniently integrate with other services that the cloud provides.

Conclusion

The diverse suite of the above non-SQL data validation techniques for corporate database management provides crucial tools that cater to a variety of business needs. These methods are instrumental in maintaining high data quality and integrity; they are essential for informed decision-making and operational efficiency and provide these benefits without the necessity of SQL expertise.

The advantages of these techniques are manifold. They offer accessibility, allowing team members with varying levels of technical skills to engage in data validation processes effectively. For instance, spreadsheet formulas enable preliminary data checks that are straightforward for anyone familiar with basic office software, while automated tools like data cleansing services manage large datasets with minimal human intervention.

Integration capabilities of these techniques with existing business systems also add a layer of efficiency. Tools such as CRM and ERP integrations streamline data flows, reducing errors and ensuring consistency across different platforms. This seamless flow is particularly beneficial in complex corporate environments where data synchronization is critical for day-to-day operations. Furthermore, scalability is a significant advantage. Cloud-based services and batch data validation tools are designed to handle vast amounts of information, accommodating growth and increasing data demands without compromising performance.

By adopting these non-SQL data validation techniques, companies not only enhance their data management practices but also foster an environment that supports continuous improvement and compliance with data governance standards. These tools enable businesses to leverage their data confidently, driving innovation and maintaining a competitive edge in their respective industries. This strategic flexibility and adaptability are crucial for companies aiming to thrive in the digital age.

Take Away

“Data validation ensures regular data monitoring and assures stakeholders of high-quality data that meets research integrity standards,” emphasizes Yale University, underscoring the importance of data integrity in today’s data-driven world. By incorporating non-SQL validation techniques, companies can exceed these standards, supporting robust decision-making and sustainable growth. This approach enhances accuracy and fosters a trustworthy, dynamic data ecosystem.

[1] Taylor, S. Data Validation. Corporate Finance Institute. https://corporatefinanceinstitute.com/resources/data-science/data-validation/

[2] Taylor, S. Data Validation. Corporate Finance Institute. https://corporatefinanceinstitute.com/resources/data-science/data-validation/

[3] Yale Library. Research Data Management: Validate Data. Yale University. https://guides.library.yale.edu/datamanagement/validate

[4] IBM. Business validation rules. https://www.ibm.com/docs/en/datacap/9.1.9?topic=requirements-business-validation-rules

[5] Geeks for Geeks. What is Data Validation? https://www.geeksforgeeks.org/what-is-data-validation/

[6] Gepard. Data Validation – Product Content Compliance for Your Business. https://gepard.io/for-brands/data-validation