Unleashing data intelligence: Big Data & Smart Data for Decision Makers

In the age of digitalisation, the term Data intelligence is becoming increasingly important. Companies are faced with the challenge of not only storing large amounts of information from the growing flood of data, but also using it to gain valuable insights for well-founded decisions. Big data and smart data form the basis for generating action-relevant insights and thus improving business processes in a targeted manner.

How data intelligence unleashes the value of data

Big data comprises large, heterogeneous and often unstructured volumes of data that originate from various sources such as IoT sensors, transactions or user interactions. However, the sheer mass of data alone offers little benefit, as it is difficult to interpret without context. This is where Data intelligence on: Intelligent filtering and processing creates smart data that provides precise and contextualised information.

Data intelligence transforms raw data into a valuable basis for decision-making. In industry, for example, companies analyse sensor data in order to control production processes with foresight. A retail company filters out the data from countless customer interactions that provides information about the purchasing behaviour of certain target groups in order to design more targeted marketing campaigns. Financial institutions also use transaction data to recognise fraud patterns at an early stage and take preventative action.

BEST PRACTICE with one customer (name hidden due to NDA contract) The company from the energy sector used data intelligence to generate smart data from extensive measurement data from its systems. As a result, the maintenance of devices could be planned more efficiently, unexpected failures decreased considerably and uptime increased significantly.

Applying data intelligence: From Big Data to Smart Data

The transition from big data to smart data is at the heart of data intelligence. It is not about the quantity, but above all about the quality, relevance and speed of data processing. This is the only way to generate meaningful information that enables quick decisions and personalised solutions in real time.

In logistics, for example, vehicle movement data is transformed into smart data in order to optimise routes and reduce delivery times. In the healthcare sector, intelligent data helps to monitor patients better and adapt individualised therapies in a targeted manner. Telecommunications companies also use smart data to analyse network loads and thus improve service quality for customers.

BEST PRACTICE with one customer (name hidden due to NDA contract) A leading mobile phone provider used data intelligence to automate the analysis of network data. This resulted in more precise network utilisation forecasts and improved troubleshooting measures, which significantly increased customer satisfaction.

Practical tips for getting started with data intelligence

In order to utilise the potential of data intelligence, companies should first clearly define their objectives. Which questions should be answered with data? Only then is it worth identifying suitable data sources and systematically checking the data quality.

A further step is the implementation of analysis tools that use AI-supported algorithms to recognise patterns and derive insights. It is advisable to involve interdisciplinary teams in order to combine technical and specialist expertise.

Data protection and data security should also be considered from the outset in order to fulfil legal requirements and strengthen stakeholder trust.

BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized manufacturer introduced an awareness of data quality and data protection in all departments through targeted training on the topic of data intelligence. This improved collaboration between IT and specialist departments and made the handling of data more efficient.

Data intelligence: the key to better decision-making

Data intelligence helps decision-makers to act not only on the basis of intuition, but also in a well-founded and objective manner. Fast and precise decisions are crucial, especially in dynamic markets. Thanks to smart data, it is possible to react to relevant signals and deploy resources in a more targeted manner.

In retail, for example, intelligent data enables a personalised customer approach, which often leads to higher sales rates. In manufacturing, they improve quality assurance by recognising deviations at an early stage. Data intelligence methods are also used in the public sector, for example to analyse traffic flows in order to reduce congestion and optimise public transport.

The combination of big data and smart data forms the basis on which sustainable, data-driven strategies are created. Decision-makers who actively shape this combination open up new potential for innovation and competitiveness for their company.

My analysis

Data intelligence is a decisive success factor in today's economy. It makes it possible not only to extract information from large amounts of data, but also to convert it into context-specific knowledge and action-oriented insights. The quality of the data is crucial, which is why the focus is on smart data as a refined product of big data.

In a wide range of industries, intelligent data helps to optimise processes, understand customers better and act more resource-efficiently. Companies that consider data intelligence to be an integral part of their strategy are better positioned for future challenges and opportunities.

Further links from the text above:

[1] What is smart data?
[2] Big data vs. smart data: is more always better?
[5] Unleashing data intelligence: Big Data & Smart Data for ...
[9] Data intelligence: Big data and smart data for decision-makers ...
[14] Unleashing data intelligence: Big Data & Smart Data for ...

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