Data-hungry

Describing a system, process, or individual that requires, consumes, and is highly dependent on large quantities of data to function effectively or achieve its objectives. This craving for data extends beyond mere collection; it implies active processing, analysis, and utilization of the information to generate insights, make decisions, or improve performance. data-hungry entities often prioritize access to comprehensive and up-to-date datasets, sometimes at the expense of other considerations. They are driven by the belief that more data translates to better results and are constantly seeking ways to expand their data intake and analytical capabilities. This characteristic is increasingly prevalent in fields like artificial intelligence, machine learning, and advanced analytics.

Data-hungry meaning with examples

• The new AI algorithm is incredibly data-hungry, requiring terabytes of labelled information to accurately identify patterns and predict outcomes in the financial market. Without such a massive dataset, its performance plummets, rendering it practically useless. Developers spent months sourcing and cleaning suitable financial data to feed the algorithm, reflecting the challenges of working with such systems.
• A data-hungry marketing campaign relies on tracking every click, impression, and conversion across multiple platforms. Sophisticated analytics tools are employed to analyze consumer behavior and tailor advertisements to individuals. This intensive approach provides insights that are then used to continuously refine the marketing message and media buy, ultimately optimising it for the highest return on investment.
• Modern self-driving cars are undeniably data-hungry, generating and processing colossal amounts of sensor information from cameras, radar, and lidar. This continuous stream of data is used to build a model of the vehicle’s surroundings, providing vital information and supporting decision making for safe and efficient navigation. Without a continuous data stream, the system becomes blind.
• Scientific research in genomics is frequently data-hungry. Researchers require access to vast datasets of genetic information to identify correlations. Specialized data-analysis tools are used to extract important data from sequencing results, allowing scientists to test hypotheses and push the boundaries of knowledge within fields of medicine and genetics.
• Many data-hungry streaming services, like Netflix and Spotify, utilize customer viewing habits to provide recommendations. The use of personalized recommendations, made possible through advanced machine learning techniques, is the key factor which drives subscriber engagement. They also constantly monitor and analyse user interactions with the platform, using these insights to refine their offerings and algorithms.