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.
ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts. Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed.
In 1979, Teradata began life as a collaboration between Caltech and Citibank. Today, this enterprise software group is all about redefining business intelligence tools and data management. The Teradata Database is now the Teradata Vantage Advanced SQL Engine, The name not only highlights the evolution of the company but also recognizes that tech consumers now expect more from their tools.
These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.
Read Part 1 here > Data analytics is a complex process that demands time and effort from data scientists. From cleaning and prepping data to performing data analysis, data scientists go through an extensive procedure to uncover hidden patterns, identify trends, and find correlations in data to make informed business decisions. The task of integrating, cleaning, and organizing data assets often take up the bulk of the data scientist’s time.
