Hyper personalization is a marketing strategy that involves using advanced technologies, such as artificial intelligence and machine learning, to tailor messages and experiences to individual customers. Deloitte defines hyper-personalization as “the most advanced way companies can target a single consumer”. Hyper-personalization essentially treats each user as a unique customer segment. This trend is heavily driven …
Data
TinyML is a technique that shrinks deep learning networks to fit onto small hardware. This process takes edge computing to the extreme by allowing small devices to quickly process data without experiencing latency issues Approximately 15.2 million units of TinyML chipsets were shipped two years ago. This number is forecasted to grow to 1.2 billion …
Synthetic data is information that is created artificially via computer simulations rather than gathered from real-world events. Synthetic data has been traditionally used to validate mathematical models and as a stand-in for operational or production data. However, synthetic data is becoming more prevalent in AI training because it can be used without privacy restrictions, can …
DAO analytics aggregates information on various decentralized autonomous organizations. In addition to surface-level data like the number of token holders, active members, proposals and votes, these platforms also provide more detailed data like: Top stakeholders Treasury activity (currency mix, treasury level over time, etc.) Members part of multiple DAOs One trending DAO analytics startup is …
As the amount of data businesses generate continues to grow exponentially, maintaining data security, control, and customization has become increasingly important. On-premise data quality platforms provide an effective solution for businesses that require complete control over their data while ensuring security and customization. On-premise data quality platforms offer businesses unparalleled security, control, and customization over …
Discover how open-source data quality platforms can help businesses improve data quality while being cost-effective. Learn more about the benefits of using open-source data quality platforms. Introduction In today’s digital age, businesses are generating massive amounts of data every day. However, the quality of this data can have a significant impact on the success of …
Learn how cloud-based data quality platforms can help businesses improve data quality, efficiency, and reduce costs. Read on to discover the key benefits and considerations when implementing a cloud-based data quality solution. Introduction As businesses continue to rely on data for decision-making, ensuring high-quality data has become increasingly important. Poor data quality can lead to …
Data quality is a crucial factor for any business that relies on data to make decisions, optimize processes, and improve customer satisfaction. Data quality refers to the ability of a set of data to serve an intended purpose, and it can be assessed by various dimensions, such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. …
Learn how data governance and data quality platforms can help businesses ensure data accuracy, compliance, and trustworthiness while avoiding penalties and risks. Introduction Data has become one of the most valuable assets for businesses of all sizes and industries. However, the increasing volume, variety, and velocity of data pose significant challenges for organizations in terms …
Learn the best practices for assessing data quality using data quality platforms and how it can help businesses make informed decisions in this comprehensive guide. Introduction Data quality is a crucial factor for any business that relies on data to make decisions, optimize processes, improve customer satisfaction, and gain a competitive edge. However, data quality …