Data validation is a critical segment aspect of any data consumption initiative. Not only do you need to have verified and validated information present for stakeholders, but you need to have data that is universally standardized across all departments based on the parameters defined by your organization. This helps with consistency and alignment in cross functional data consumption programs, for data elements to be accurately interpreted across variance reports, dashboards, or other management systems.
The process of data validation has increased in priority as data is manipulated across multiple automation tools and software development innovations. There are data integration platforms that easily incorporate validation processes so that your team will not feel bogged down by this essential step. It is crucial for any organization relying on data for everything from simple record keeping to deep analysis for decision-making to learn more about data validation.
What is the Data Validation Process?
The exact process used for data validation may differ for each unique business based on the needs and challenges of that industry or company. In general, you can expect steps for data validation that cover the basics, similar to:
Creating a roadmap for data validation that deals with the overall expectation if there are any issues related to the source information and then how to resolve these problems. This is when you might determine or define the type of controls you need to design to help with quality and consistency at the data source.
Here you will need to determine the data attributes such as number of records, data size, comparison of source and the target based on a data field, and pretty much anything else that ensures all applicable data is present from source to report.
Validate Data Formatting
This is self-explanatory, where the data collected is put into the correct formats for the end-user to clearly understand, regardless of whether or not it meets business expectations.
Data harmonization is a critical factor in data validation. This is because frequent data discrepancies can appear when different sources of information use other terms or words for the same core metrics. When you harmonize this information, you are aligning all of the data from sources into a standard format that can be validated. Two other factors important to this process include:
- Data Catalogs - a tool to index and classify different data assets, usually through metadata or combined with data management and search tools.
- Data Dictionaries - communicate the structure and type of content of the data by providing meaningful descriptions that IT and engineers use to create a database structure.
These tools are used to ensure consistent validation efforts and format standards so that the appropriate source data is being utilized in the best possible data model for searching, reporting, and analysis.
Design elements for Data Reliability
The more your internal and external users can rely on the data being presented and validated, the more accurate reports, decision-making, and integrations can be. The goal is to work with higher-quality data because of the implemented validation tools that breed trust with end-users. Some of the use cases involved in this process include:
By organizing the data stored and collected from various sources in a unifying structure and format, you ensure data that is more accessible and ready for use. In addition, this helps to avoid errors from arising and inconsistencies causing problems downstream.
There are different models of data cleansing, but the idea is to detect and correct any incorrect data records. Then, whenever a correction is unavailable, that data or records may be removed so it cannot corrupt the rest of the dataset.
Making specific data units easier to find involves drawing profiles based on the source information’s structure, type, and relationship. This helps with quality assessment and classification so that every piece of data is compatible with the dataflow in place.
When the data meets the standards, rules, and basic structures of data quality as defined by the enterprise, it becomes more efficient through the rest of the dataflow and creates value for the end-users.
Why do these use cases matter in data reliability? They help to avoid the downstream multiplier effect of bad data quality. A single piece of bad data at the beginning of the dataflow (from a source) can result in significant bad decision-making or insights that are based on information not relevant to the company, not accurate, or missing datasets that sway statistics.
When a single point of bad data is collected, it begins to multiply in potential harm with every stage along the process until it flows all the way downstream to the end-user. This compounding factor can result in negative impacts.
Consider Automation to help Improve Data Quality and Reliability
Integrating a modern script or automated program to ensure you have appropriate data validation systems in place drastically reduces the chance of errors. This is especially true for organizations working with massive amounts of data that cannot conceptually be validated through manual processes without dedicating significant resources that would be contradictory to business growth.
It is far more efficient to use automated tools that benefit data validation through:
- Handling larger amounts of data volume
- Ensures data quality through accuracy, validation, completeness, and uniform controls
- Eliminates mundane and repetitive tasks from employees
- Enhances data dependability by lowering the potential risk of bias, variation, and fatigue
- Saves time for the business with minimal effort required
The Importance of Data Quality in Decision Making
The more you can boost confidence in the accuracy of the data being used for analysis and reporting, the better your decision-making outcomes. Considering how many decisions are made related to customer outreach, productivity, process management, and more, the stakeholders of any organization need reliable and trusted data to move forward.
If data is not properly validated, a business runs the risk of tarnishing results, which can cause significant damage. This can result in missed opportunities, lost revenue, and reduced operational efficiency. That, in turn, can harm partnerships and customer relationships that will lead to the downfall of an organization.
A simple way to ensure that your business is leveraging proper data validation that overcomes the challenges of bad data is to use modern tools that combine different technologies.
At NextPhase, we have years of experience implementing such validation tools and systems to ensure your decision-making is based on accurate and reliable data. We can provide the necessary tools your business needs to make valuable decisions based on data quality confirmed insights for a brighter and more profitable future. Contact us today, and let’s discuss building a solution for your data validation needs.