Glossary
Data Intelligence
Faced with vast data volumes, data intelligence is crucial to identifying the most relevant information.
Before creating value through data, selecting the most pertinent data and deriving appropriate insights is essential. This is the challenge for most modern organizations dealing with increasingly massive datasets. Fortunately, they can utilize data intelligence to pinpoint the most useful information for their operations. Let’s unravel the concept.
What is Data Intelligence?
Data intelligence encompasses all the processes that extract value through data, from its exploration to data visualization and analysis. It transforms vast amounts of data into insights capable of supporting organizational growth. Data intelligence is crucial to selecting the most relevant data to drive better decision-making, whether to mitigate risks, identify new opportunities, understand target audiences more deeply or enhance competitive advantages for example.
Note: Given exponential data volumes, the term “Big Data intelligence” is also used.
How does it works?
The goal of data intelligence is to highlight the value of information to an organization. In order to convert raw data into a valuable resource, data intelligence experts employ a range of methods and tools, including:
- Machine learning/AI algorithms: These technologies quickly process and analyze massive amounts of data, which is often impractical with traditional analytics.
- More traditional business intelligence (BI) tools: It’s important to distinguish between business intelligence and data intelligence. Business intelligence focuses on organizing data to make it understandable and useful, while data intelligence concentrates on the added value of each analyzed piece of information.
- Other methods: These include steps that facilitate data processing and analysis, such as data mining for data collection and data visualization for making insights accessible and understandable.
Benefits
Data intelligence assists organizations in making better decisions, providing a significant competitive advantage. Although all organizations today possess vast amounts of data (from Internet of Things (IoT) sensors, the web, mobile applications, geolocation tools, etc.), not all can extract value from it. Those that succeed are the ones that master data intelligence. The capability to derive relevant insights from large volumes of raw data enables organizations to better understand consumer habits, identify trends, reduce costs, optimize offerings, and improve internal processes.
Data intelligence is applicable across different industries and roles: marketing, to understand the ideal customer; finance and cybersecurity, to identify risks; and healthcare, to enhance agility and responsiveness to crises.
Ultimately, data intelligence is at the core of organizations developing a data-driven culture.
Optimizing Data Intelligence
While many companies use data intelligence today, to create value through data key best practices must be followed. These include:
- Understanding data: Successful digital transformation requires data to be translated into formats and insights that are understandable and accessible to all, regardless of their data knowledge or technical skills.
- Data visualization: Making data understandable for everyone typically involves using data visualization tools. These transform information into easily understandable graphs, curves, maps, or stories.
- Data sharing: This can be internal, with data accessible to all relevant staff, and externally with data available to clients, partners, consumers, citizens, the media, and other stakeholders. Data portals facilitate widespread data sharing while making it easily understandable to all.
Learn more
Product
Opendatasoft integrates Mistral AI’s LLM models to provide a multi-model AI approach tailored to client needs
To give customers choice when it comes to AI, the Opendatasoft data portal solution now includes Mistral AI's generative AI, alongside its existing deployment of OpenAI's model. As we explain in this blog, this multi-model approach delivers significant advantages for clients, their users, our R&D teams and future innovation.