Helping world-class firms make the most of their data
Given the volume of data your organization deals with, being thoughtful about each step of the data journey is important: data collection, data preparation, and data discovery. Datavid Rover's data discovery tools uncover hidden gems in your data, visualizing relationships between data sets to help you make strategic business decisions without compromising operations.
Power up your data ecosystem
Datavid Rover’s data discovery tools provide a big-picture view of your data streams thanks to flexible API integrations with a wide range of systems—including CRM, data warehouses, and more. This enables you to automate and modernize workflows while broadening use cases with third-party applications.
Bring enterprise knowledge to everyone
Data discovery isn’t just about deriving business insights—it’s also about making those insights available to your teams with self-service, intuitive interfaces. Datavid Rover’s tools all come with this experience out of the box, leveraging machine learning and other AI techniques to bring additional value when needed.
Strengthen your analytics capabilities
An effective data discovery process lays the foundation for business growth. Thanks to Datavid Rover's discovery tools, your team members get to apply their expertise without the headache of preparing the data for use beforehand. With charts and graphs that represent their exact needs, all they have to do is use their domain experience to craft a narrative—and then implement it.
Search less and discover more
Imagine the possibility of uncovering deeper insights into your business data with minimal effort and in a significantly shorter time. Datavid Rover’s discovery tools help you do just that.
Using entity extraction techniques powered by ontologies and knowledge graphs, you can get relevant information for your specific task in seconds. This results in productivity gains and time-saving to help you focus on innovation and business development.
Turn data into knowledge
To remain relevant in the marketplace, you need forward-thinking business strategies that can withstand future challenges. Having access to knowledge helps you get there.
Datavid Rover uses advanced analytics and data modeling techniques to extract additional value from your data, enabling insights that enhance the quality and reliability of decisions leading to business growth and operational excellence.
Bring inefficiencies to a halt
Enterprises often lose a significant amount of value due to inefficiencies such as employee turnover, process bottlenecks, sunken costs, and other factors. Datavid Rover can help you extract 99% of the value that your data carries and assist in mitigating these inefficiencies—enabling you to maintain control over your enterprise's most valuable knowledge.
Extracting knowledge from decades of data
With almost 50 thousand team members spread across the globe, Syngenta is a powerhouse in farming and agrochemicals—a dominant leader in many of its applications worldwide.
Syngenta came to Datavid with a huge challenge to solve: indexing more than 16 million files spread across dozens of data sources which contained decades worth of siloed knowledge.
Datavid developed a semantic data platform for Syngenta, with the capability to index and retrieve specific agrochemical concepts extracted from "foam tree" ontologies.
The result was a drastic reduction in research time from several weeks to just a few hours, enabling research across a vast body of knowledge previously unavailable to the R&D team.
Datavid is helping Syngenta's scientists identify concepts that are relevant to their research more quickly, ultimately saving time and effort on a weekly basis.
HEAD OF CONTENT DIGITISATION, SYNGENTA
Your questions. Answered.
The steps of data discovery typically involve:
- Data Identification: Identify the sources and types of data available.
- Data Collection: Gather data from various sources, which can include databases, files, and external datasets.
- Data Exploration: Analyse the data to understand its structure, quality, and potential insights.
- Data Visualization: Create visual representations of the data to aid in understanding and identifying patterns.
- Data Cleaning: Clean and preprocess the data to address missing values and inconsistencies.
- Data Modeling: Apply statistical or machine learning techniques to extract meaningful information.
- Data Validation: Ensure that the results are accurate and validate them against the original data.
- Reporting and Communication: Present the findings to stakeholders through reports or dashboards.
Tableau, QlikView, and Power BI are examples of data discovery tools used for visualising and exploring data to gain insights and make data-driven decisions.
The principles of data discovery include:
- Accessibility: Data should be easily accessible and available to those who need it.
- Understanding: Users should comprehend the data's meaning and context.
- Collaboration: Encourage collaboration and knowledge sharing among data users.
- Self-Service: Empower users to explore and analyse data independently.
- Visualization: Use visual representations to aid understanding and insight.
- Agility: Be flexible and responsive to changing data needs and requirements.
- Data Quality: Ensure data is accurate, complete, and reliable.
- Governance: Implement rules and policies for data management and security.