How to ensure you can trust your data
Journal
Before we can understand our data, we need to be able to trust it. With the ever-increasing volume, scale and scope of data, the rapid adoption of AI and Machine learning, getting the right insights from data is getting more and more challenging.
Your organisation may be collecting large volumes of raw data, but it is only useful if it is of high quality. Can you imagine the consequences of investing millions of dollars to train your AI and ML models, but your data was unreliable?
Building trust in the quality of your data allows for optimal service outcomes and provides benefits that extend far beyond increased data quality. However, the first step in this process is investing in automating your data validation.
"Trust takes a lifetime to build and a moment to lose, but automating your data assurance addresses that"
Data validation is the process of regularly checking all data for accuracy and completeness to ensure that it can it be used effectively.
It is a critical step within an organisations data quality management framework as it ensures that the data that is ingested and stored within disparate systems is accurate, complete, consistent, and conforms to the desired format and constraints.
Ongoing data validation is not only crucial for maintaining the integrity and accuracy of the data, but also, to ensure that data driven analytics and decision making is based on reliable data can be effectively used for analysis and decision-making.
Failing to properly validate data can have significant consequences for both the organisation and its stakeholders.
Some of the costs of not validating data effectively include:
1. Loss of trust and credibility:
If an organisation is found to have inaccurate or unreliable data, it can damage its reputation and lead to a loss of trust from customers, partners, and other stakeholders.
2. Inefficiency and poor decision-making:
Bad data can lead to poor decisions, as well as inefficiency in business operations. This also can lead to financial losses, such as lost revenue or increased expenses.
3. Legal and regulatory compliance issues:
Organisations that handle sensitive data may be subject to legal and regulatory compliance requirements. Failing to properly validate data can lead to non-compliance, which can result in fines and penalties.
Using automation tools to validate your data
By validating data, organisations can avoid potential costs and ensure that their data is accurate, reliable and useful. One key way to validate data is through automation.
Automated data validation can be achieved using scripting or programming that can check the data against a set of predefined rules and constraints. This can include checks for data types, accuracy, completeness, and validations against multiple data sets.
Data quality software tools can perform a wide range of data validation checks automatically to identify errors in real time. These tools can be used to identify patterns or discrepancies in the data that may indicate errors or inconsistencies and avoid human error.
Software such as Investigate DQ can help organisations to easily monitor, validate and reconcile customer and organisational data across any number of different technology platforms or data sources.
Investigate DQ will automatically execute routine data quality checks across critical day-to-day functions. For example, Investigate DQ could automatically monitor customer data to ensure you find any critical errors and of course have clean data that is usable.
Best of all Investigate DQ is system agnostic, which means it connects directly to your data sources, avoiding costly and prolonged ETL (extract, transform and load) processes. No matter what technology systems you run, Investigate DQ can plug in and be ready on day one.
You can learn more about Investigate DQ by clicking below.
