how to validate the reliability and accuracy of information

As a result, they had multiple teams working with multiple different tools and product roadmaps – and there was little visibility between departments as to what data was being collected. Adevinta has 25 digital brands, which operate across 11 countries. However, what all these possibilities have in common is their ability to generate more revenue, whether by mirroring customer expectations, finding the right product-market fit, or not wasting time and resources on channels and touchpoints that generate little returns.Ī prime example of this is with Adevinta, a leading online classifieds provider in Europe. Increased revenueĭata can unlock key insights for your business: how to streamline operations, what should be prioritized in your product roadmap, which acquisition channels have the highest ROI - the list goes on. Conversely, prioritizing reliable data allows you to reap the benefits that come with it – happier customers, boosted revenue, and more efficient operations. Reliable data is crucial to a business’s ability to make data-driven decisions (like understanding which initiatives will yield the biggest ROI) and avoid missteps (like sending marketing emails to invalid email addresses).īad data isn’t just virtually useless, it can also cause legal problems, erode customer trust, and hinder business growth. For example, you may have a customer’s correct address in your database, but if it’s not formatted correctly (e.g., the apartment number is placed where the zip code should go) it’s not valid nor reliable. Data reliability is knowing that data is accurate and can be trusted for analyzing and making decisions, while data validity refers to the consistency of data formatting.ĭata validity is an essential part of data reliability: you can’t have reliable data without valid data. data validityĭata reliability and validity are both important aspects of data health, but they’re not the same.

This is why it’s important to understand the entire data lifecycle to ensure reliability at every stage (e.g., collecting, cleaning, consolidation, usage, and eventual deletion). Reliability is an important facet of data integrity, and points to how much confidence and trust an organization has in using their data to inform processes and decision making.Įrrors or inconsistencies in your data can threaten business operations, insights, and the ability to reach your objectives. Reliable data refers to the accuracy and completeness of the data you collect.

How to validate the reliability and accuracy of information