Dark data is data that is collected, processed and stored as part of daily business activities. However, despite the large amount of storage space it occupies and the resources that must be spent on collecting and maintaining it, it cannot be used for analytics.
In addition to these attributes, the quality of a brand’s data can also be measured by other criteria:
Pick-up time, with calculations and testing of the equipment.
The cost of storage. Because if the amount of information collected nepal phone number list remains stable and storage does not suffer, the quality has increased.
The bounce rate of the email in the newsletter or in marketing campaigns. If errors occur when sending mailings, the data of some users is probably out of date.
One of the most common problems facing companies today is the lack of quality of the data they manage in their systems. Inaccurate or erroneous data makes decision-making difficult and, in the worst case, threatens the development of the company. As the end of cookies draws ever closer, maintaining data quality has become one of the most important goals for brands. How can this be achieved?
Data collection, an important step to ensure data quality
Our data science specialists divide data science projects into three phases:
Data collection: Collecting raw data is essential to maintain data quality. During this phase, we verify that the data from each online and offline channel is processed correctly.
Segmentation or processing: This is where the data is transformed to extract value from it. Work is done on integrating different sources and implementing data lakes that allow for in-depth analysis of the data.
Activation: This is about using the knowledge gained from the data to make decisions. Quality data is a beacon that controls, for example, media purchasing or the development and optimization of target groups.
Data collection: Collecting raw data is essential to maintain data quality. During this phase, we verify that the data from each online and offline channel is processed correctly.
Segmentation or processing: This is where the data is transformed to extract value from it. Work is done on integrating different sources and implementing data lakes that allow for in-depth analysis of the data.
Activation: This is about using the knowledge gained from the data to make decisions. Quality data is a beacon that controls, for example, media purchasing or the development and optimization of target groups.