Advancing digitalisation is increasingly changing how companies deal with the flood of data. Data intelligence is becoming increasingly important because it helps to gain valuable insights from large volumes of data. The conflicting priorities of big data and smart data show how important it is to utilise data in a targeted and quality-oriented manner in order to achieve competitive advantages.
Data intelligence: from raw material to valuable resource
Big data describes the sheer volume of diverse data that companies generate every day - such as transaction data in retail, machine data in industry or customer data in the financial sector. However, this raw data alone is rarely directly usable because it is often unstructured and complex.
Data intelligence means extracting the information from this mass that is truly relevant, structured and of high quality. This so-called smart data is created by intelligently analysing and filtering big data. In this way, companies transform data into a valuable raw material that supports strategic decisions.
For example, an online retailer uses purchasing behaviour with the help of data intelligence to create targeted individual offers. In turn, a logistics company uses it to dynamically optimise routes, conserve resources and shorten delivery times. And in the healthcare sector, intelligent data helps to better tailor treatment plans to individual patients.
The role of quality and context in data intelligence
The difference between big data and smart data lies primarily in the quality and relevance of the data. While big data stands for pure quantity, smart data means that data is filtered, cleansed and viewed in the right context. This is where data intelligence comes in, because this is the only way to generate truly meaningful and usable insights.
In practice, many companies report that unstructured or erroneous big data only provides partially helpful results. An insurer that utilises data intelligence, on the other hand, can evaluate claims more precisely by integrating relevant variables and filtering out incorrect information.
Manufacturers also benefit by being able to analyse machine data at the right time and thus predict maintenance, which reduces unplanned downtime. At the same time, the integration of governance and data protection guidelines protects sensitive data and promotes trust.
BEST PRACTICE with one customer (name hidden due to NDA contract)
BEST PRACTICE with one customer (name hidden due to NDA contract) The European mechanical engineering company used data intelligence to link extensive sensor data from production with external market and weather data. This enabled it to flexibly adapt production processes, minimise downtime and use energy more efficiently. The targeted use of smart data measurably reduced costs and noticeably increased output.
Technologies and methods for data-intelligent processes
Data intelligence is fuelled by modern technologies such as artificial intelligence (AI), machine learning and data mining. They make it possible to recognise patterns and correlations that would be difficult for humans to see on their own.
For example, a retail chain can use machine learning to analyse purchasing patterns and identify seasonal trends at an early stage. This optimises the ordering of goods and avoids overstocking. Automated prediction models also help the financial sector to assess credit risks more accurately and detect cases of fraud more quickly.
Logistics companies also rely on real-time data analyses to make fleet management efficient and avoid traffic disruptions. These data-supported processes reduce downtimes and create added value for customers and companies alike.
BEST PRACTICE with one customer (name hidden due to NDA contract)
BEST PRACTICE with one customer (name hidden due to NDA contract) A leading insurance company implemented an AI-supported platform to automatically check incoming forms for completeness and inconsistencies. This significantly reduced processing time and at the same time noticeably improved data quality, which enabled more in-depth risk analyses.
Practical tips for the use of data intelligence in the company
The following steps are helpful for companies that want to successfully unleash data intelligence:
- Develop a data strategy: Define clear objectives and decide which data is relevant.
- Integrate data sources: Linking different systems and external data in a meaningful way.
- Ensure data quality: Eliminate errors, remove duplicates and update data regularly.
- Use automated analysis tools: Use AI and machine learning to recognise patterns and trends.
- Note data protection: Implement and constantly revise compliance and security concepts.
For example, a retail company can react more quickly to changes in demand through a consistent data strategy. A production company can increase maintenance efficiency, while a service provider can improve its customer approach through personalised offers.
BEST PRACTICE with one customer (name hidden due to NDA contract)
BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized logistics company integrated IoT data from vehicles with weather and traffic information. With the help of data-intelligent algorithms, it was able to adjust routes in real time, forecast delivery times more precisely and significantly reduce fuel consumption.
Data intelligence as a success factor for modern companies
Data intelligence means utilising the right data in the right quality at the right time. It supports companies in making well-founded decisions, promoting innovation and making processes more agile.
Industries such as retail, manufacturing, finance and healthcare often report that data-intelligent approaches increase their efficiency, reduce risks and increase customer satisfaction. Data intelligence is thus becoming a decisive competitive advantage.
Specialised solutions and experts such as transruptions coaches support companies in all aspects of data intelligence by initiating projects and providing targeted impetus. The combination of technology, strategy and a trained approach ensures sustainable success.
My analysis
Data intelligence unleashes the value of big data by transforming large volumes of data into relevant, high-quality smart data. Companies benefit from more precise insights, faster decisions and better results. The targeted use of data-intelligent technologies and methods creates new opportunities in a wide range of industries. It is therefore worth continuously promoting and actively supporting data intelligence as a strategic resource.
Further links from the text above:
Smart data definition and differences to big data
Big data vs. smart data: quality instead of quantity
Data intelligence: big data and smart data for decision-makers
How to secure your lead with Big & Smart Data
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