Helping world-class firms make the most of their data
Imagine collecting large amounts of customer feedback but struggling to make sense of it due to the unstructured nature of the data.
Without a reliable way to extract and analyze relevant information, you miss out on valuable insights that could inform product development and marketing efforts. This is where named entity recognition comes in.
By using NER in conjunction with ontologies to analyze relationships in data, your business can make more informed decisions and gain a competitive edge. The insights gained from NER can inform product and service development, improve customer experiences, and increase customer loyalty.
Datavid Rover's NER tools use ontologies to revolutionize how you analyze data—leading to more efficient, effective, and impactful operations.
Save time sifting through data
Datavid Rover helps you leverage NER and streamline data processing and analysis, reducing the need for manual effort, and improving efficiency.
Get the insights you are looking for
Datavid Rover supports you in leveraging unstructured data to improve your products, services, customer experiences, and overall business operations.
Find critical relationships in your data
When NER works alongside ontologies, it can help your business identify critical relationships between real-world concepts, like how a drug causes side effects, or how a financial operation is connected to fraud.
Automate tedious data processes
NER helps your business save costs associated with manual data processing and analysis.
By automating the identification and extraction of named entities, you can reduce the need for manual labor, increasing efficiency and productivity.
Datavid Rover helps your company automate processes, freeing up time and resources for other important tasks such as strategic planning, customer service, and product development.
Improve your data analysis
NER helps extract relevant information from large volumes of unstructured data, making it easier to perform analysis and gain insights.
This is particularly important in industries where large amounts of data are generated daily. Datavid Rover takes charge of your data's path to actionable knowledge, analyzing it quickly and accurately using NER technique, and returning organized and curated results.
Improve your customers' experience
NER can help you enhance your customer experience by extracting and identifying relevant named entities such as product names, feature requests, and sentiment from customer feedback, reviews, and social media posts. This can help you gain a better understanding of customer needs and preferences, which can inform product development and marketing efforts
Datavid Rover uses NER to analyze your customers’ feedback, helping you respond more quickly and effectively to issues and concerns and improving their satisfaction and loyalty.
Boosting customer satisfaction and revenues
BSI faced the challenge of building a more powerful search system within their Compliance Navigator, allowing for contextual results and increasing customer satisfaction.
Datavid proposed a solution based on the use of the knowledge graph, built on ontologies, capable of tracing the relationships between concepts and the BSl standards database.
The solution included a UI/UX technology upgrade, resulting in greater customer satisfaction and higher revenues.
The new semantic search functionality shows results for related standards, even when search terms are not precise. Additional features include advanced search and creating templates.
Working with the consultants at Datavid was a great experience, they always guided us in the right direction and delivered significant value in short span of 3 months.
PRODUCT MANAGER AT BSI
Your questions. Answered.
Named Entity Recognition (NER) is a natural language processing (NLP) technique that identifies and classifies named entities, such as names of people, places, organisations, dates, and more, in text. It's used to extract structured information from unstructured text data.
Named Entity Recognition (NER) identifies and classifies specific entities, such as names of people, places, organisations, dates, and more, in text, making it easier to extract structured information from unstructured text data.
An entity is a general concept referring to something with a distinct and independent existence, while a named entity is a specific type of entity that is a proper noun, such as a person's name, a location, or an organisation's name. Named entities are a subset of entities and are often the focus of Named Entity Recognition (NER) in natural language processing.