Data Quality (DQ) describes the degree of business and consumer confidence in data’s usefulness based on agreed-upon business requirements. These expectations evolve based on changing contexts in the marketplace. As people get new information and experience different interactions, business requirements face updating, redefining Data Quality needs within the data’s lifespan. Since DQ represents a moving […]
Transforming Data Quality with Machine Learning
Machine learning makes improving Data Quality easier. Data Quality refers to the accuracy of the data: High-quality data is more accurate, while low-quality data is less accurate. Accurate data/information supports good decision-making. Inaccurate data/information results in bad decision-making. So, intelligent decision-making can be supported by supplying accurate information through the use of machine learning. Machine […]
Data Governance Frameworks
A Data Governance framework can be described as a collection of processes, rules, and responsibilities used to structure an organization’s Data Governance program. A solid framework will cover data standards, data privacy, business strategies, and the responsibilities of key individuals. Well-designed Data Governance frameworks support effective Data Governance programs and should standardize rules and processes across the […]
What Is Data-Centric AI All About?
Data-centric AI (DCAI) is a new class of AI technology that focuses on understanding, utilizing, and making decisions based on data. Before data-centric AI, AI was largely reliant on rules and heuristics. While these could be useful in some cases, they often led to suboptimal results or even errors when applied to new data sets. […]
Data Quality Management 101
Data Quality Management is necessary for dealing with the real challenge of low-quality data. Data Quality Management can stop the waste of time and energy required to deal with inaccurate data by manually reprocessing it. Low-quality data can hide problems in operations and make regulatory compliance a challenge. Good Data Quality Management is essential for […]
16 Internal Data Management Best Practices
In today’s digital world, data is undoubtedly a valuable resource that has the power to transform businesses and industries. As the saying goes, “data is the new oil.” However, in order for data to be truly useful, it needs to be managed effectively. This is where the following 16 internal Data Management best practices come […]
Understanding Data Management Tools
Data Management tools are used to develop and monitor practices, as well as organize, process, and analyze an organization’s data. These tools are designed to arrange and harmonize data, and should provide a high degree of efficiency and effectiveness. Data Management tools also support privacy, security, and the elimination of data redundancy. Effective Data Management […]
6 Data Cleaning Strategies Your Company Needs Right Now
Data cleaning (or data cleansing) is the process of checking your data for correctness, validity, and consistency and fixing it when necessary. No matter what type of data you are handling, its quality is crucial. So it’s better to implement this process into your regular workflow as soon as possible. What are the specifics of data […]
What Is Data Cleansing?
Data cleansing (aka data cleaning or data scrubbing) is the act of making system data ready for analysis by removing inaccuracies or errors. This process prevents questionable and costly business decisions based on messy data. Data volumes and sources have grown much bigger and are expected to scale up even quicker. Companies wish to access […]
Case Study: PrecisionProfile Advances Healthcare Analytics with Improved Data Preparation
There’s one phrase that people never want to hear from their doctor: “I’m sorry, but you have cancer.” According to the National Cancer Institute, an estimated 1,735,350 new cases of cancer will be diagnosed in the United States this year and 609,640 people will die from the disease. Fortunately, and despite these statistics, many types […]