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Have you ever struggled with duplicate records, inconsistent formats, or redundant data in your ETL workflows? If so, the root cause may be a lack of data normalization. Poorly structured data leads to data quality issues, inefficient storage, and slow query performance. In ETL processes, normalizing data ensures accuracy, consistency, and streamlined processing, making it easier to integrate and analyze.

With the rise of online ordering and the growth of delivery, consumers and businesses alike order more packages, expect them faster, and want rapid updates on progress, delays, and changes. Companies providing transportation and logistics services, then, have a compelling reason to add realtime update features to their apps. In other articles, we’ve talked about the broad challenges transport and logistics companies face when providing realtime updates, including scalability and low latency.

Can you put a number to how much bad data is costing your business? This includes everything from marketing losing 21 cents of every advertising dollar due to misinformed business decisions to data engineers wasting more than 3h daily resolving data quality issues. Luckily, you can guarantee high-quality data with 9 simple checks. In this article, we’ll look at.

Poor-quality data costs the U.S. economy around $3 trillion a year and impacts the bottom line of 88% of American companies. That's why data reliability is so critical. It prevents inaccurate, inconsistent, or just plain bad data from moving through your pipelines.

The quality of your data determines how well it supports your business goals within a given context, be it in operations, planning, or decision-making. Low-quality data cannot effectively serve your purpose. Usually, decision-makers rely on data to support their decisions; however, much evidence suggests that poor or uncertain data quality can contribute to ineffective decision-making in practice.

There is no question about the usefulness of big data these days. However, if you want the best data, you need it to be as accurate as possible. That means that your data has to be up-to-date, correct, and clean. Using one of these top data cleansing tools can help you be sure of this.

Part of our vision at Qlik is to simplify analysis for everyone – meaning that Qlik Sense is not just a tool for IT and the data analyst but something that can be put in the hands of everyone.

Your organization’s data is the source of opportunity for your innovation. However, 25 percent of executives surveyed by KPMG either distrust or have limited trust in their data. Without integrity, the information is essentially useless. So exactly what is data integrity? Let’s take a dive deeper into this topic and discuss why it is important for your organization.

According to Forbes , 95% of companies need to manage unstructured data and use it to gain actionable insights. Data processing is the way to do just that. Unfortunately, poor data management occurs when companies don't make full use of the information they collect.

Data cleansing is a fundamental aspect of knowing what data you retain and using it most effectively. The process of cleansing reduces the potential for error and enhances data reliability. Here's how to get started.