Companies need to collect huge volumes of data produced to extract valuable insights via data analysis to survive, let alone thrive in the competitive marketplace. It is the lifeblood of most business decisions, functions, and processes. But the process of data collection can be highly challenging for many organizations.
Data Lifecycle Management
Data Lifecycle Management focuses on data governance, data cleansing and quality, and data stewardship. The rubric applies to articles that focus primarily on the high-level preparation, flow, and use of data through an organization, rather than with one single facet such as storage or analysis.
Trustable data can be defined as data that comes from specific and trusted sources and is used according to its intended use. It is delivered in the appropriate format and time frames for specific users.
With the world’s data multiplying in leaps and bounds, every organization is trying to make better business decisions in marketing, product development, and finance using insights from the data they hold. The value of businesses today can be measured by the quality of the data they hold.
Data scientists spend 80% of their time on data cleaning and exploratory analysis. What if you could automate most of this? What if data scientists… Read More »How to Automate Data Cleaning, in a Nutshell
Before embarking on the data profiling exercise, an analyst must prepare by going through a data profiling analysis.
One of the biggest challenges that businesses face with their datasets is duplication. Teams encounter thousands of rows in the customer dataset, knowing that their… Read More »The art of removing duplicates from your organizational data
What is data transformation? Simply put, data standardization is the process of transforming data values from an incorrect format to a correct one.
Data accuracy is the biggest challenge many businesses encounter in their quest to cleanse data. Having accurate data is the foundation of the usefulness of data in all its stages of use.
Data profiling focuses on examining and analyzing data, followed by creating a useful summary of that data.
Businesses, whether big or small, know that understanding data is essential to making informed decisions that impact the organization’s bottom line.