Today, data intelligence is a key driver for companies that want to optimise their decision-making processes and achieve sustainable growth. It describes the targeted processing and intelligent utilisation of data in order to gain valuable insights. Many decision-makers are faced with the challenge of utilising large amounts of data sensibly and differentiating between big data and smart data. Data intelligence helps to structure this process and utilise the right tools to gain real competitive advantages from raw data.
Big data and smart data: what's the difference?
Big data refers to huge amounts of data that are often unstructured and complex. It is generated, for example, by online transactions, social media or sensors. Smart data, on the other hand, is specifically processed, high-quality data that provides directly usable insights. Companies use smart data to make faster and more precise decisions.
A practical example: an online shop collects thousands of customer interactions every day. This raw data is big data. It is analysed, filtered and converted into smart data using data intelligence. This provides insights into purchasing behaviour, product preferences and individual needs.
Another example: Sensors are used in logistics to monitor the condition of goods. The data collected is big data. With data intelligence, it becomes smart data that provides information on possible delivery delays or quality problems.
Large amounts of data are also collected in the healthcare sector. Data intelligence helps to analyse this data and develop personalised treatment approaches. This results in smart data that provides doctors and carers with specific recommendations for action.
Data intelligence: advantages for companies
Data intelligence offers numerous advantages. It improves data quality, speeds up decision-making processes and reduces risks. Companies can utilise their resources more efficiently and tap into new business opportunities.
For example, a financial services provider uses data intelligence to better assess credit risks. By analysing smart data, it can decide more quickly and accurately which applications are approved.
Another example: an energy supplier uses data intelligence to analyse its customers' consumption. This enables it to make targeted offers and improve customer service.
Companies in the retail sector also benefit from data intelligence. They can increase sales by using smart data to better understand their customers' needs and develop targeted marketing measures.
Data intelligence in practice
The implementation of data intelligence requires a clear strategy and suitable tools. Companies should first analyse their data pools and define the goals they want to achieve.
For example, a manufacturer of industrial equipment uses data intelligence to optimise the maintenance intervals of its products. By analysing smart data, he can avoid breakdowns and extend the service life of the systems.
Another example: an insurance company uses data intelligence to recognise cases of fraud more quickly. By analysing smart data, it can identify suspicious patterns and take targeted action against fraud.
Smart data is also being used in the education sector. Schools and universities are analysing the performance of their pupils and students in order to develop individual support measures.
Data intelligence: challenges and solutions
The introduction of data intelligence brings challenges. Companies must ensure that their data is of high quality and that the right tools are available.
An example: A retail company has difficulties structuring its data. With data intelligence, it can improve data quality and promote collaboration between departments.
Another example: A service company wants to strengthen its customer loyalty. With data intelligence, it can better understand the needs of its customers and develop targeted measures.
Smart data is also being used in the public sector. Authorities are analysing data to improve their services and increase citizen satisfaction.
My analysis
Data intelligence is a decisive factor for the success of companies. It makes it possible to utilise large amounts of data in a meaningful way and gain valuable insights. Decision-makers should consider the opportunities and challenges of data intelligence in order to optimise their business processes and achieve sustainable growth.
Further links from the text above:
What is Data Intelligence? Advantages, application & best practices
Big data vs. smart data: is more always better?
What is Data Intelligence? | Definition and advantages
Big data: the utilisation of large amounts of data
What is data intelligence and what does it mean?
Smart data: definition, application and difference to big data
Data intelligence for intermediaries
Making decisions with smart data
What is Data Intelligence? Discover the power
Smart + Big Data | Artificial Intelligence
Data intelligence or the art of turning data into gold
How to turn big data into smart data
Unleashing data intelligence: how to master big and smart data
Big and smart data - from statistics to data analysis
Meaningful data intelligence | Digital KAIZEN™
Smart analytics: how decision-makers use big data and smart data
Why data intelligence is the key to your business success
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