The importance of data intelligence is growing rapidly in an increasingly digitalised world. Managers in particular are faced with the challenge of generating not just volumes of data but actual insights from the flood of information. Big data and smart data are two key components that provide companies with valuable impetus to make well-founded decisions and realise competitive advantages. Unleashing data intelligence therefore means using the right methods and tools to gain actionable insights from complex data streams.
Understanding the basis: A comparison of big data and smart data
Big data stands for an enormous amount of different data that is often unstructured and complex. For example, e-commerce companies collect huge amounts of data on customers, purchases and user behaviour. The situation is similar for manufacturers with sensor data from production or banks with transaction data. However, the sheer volume of data can hinder decision-making.
Smart data, on the other hand, filters out the really relevant and high-quality information from this mass. They create added value by processing data in a targeted manner for specific business requirements. For example, logistics service providers help to make supply chains more efficient by intelligently analysing GPS data and traffic flows. In marketing, smart data provides precise customer segments that can be used for personalised campaigns. The transformation from big data to smart data is an essential step in effectively unleashing data intelligence.
BEST PRACTICE at the customer (name hidden due to NDA contract) An international medium-sized company in the mechanical engineering sector has achieved a reduction in downtimes of 20 % thanks to a data-intelligent analysis of machine data. The sensor information was processed in real time so that customised maintenance measures could be initiated in good time.
How data intelligence helps managers make decisions
Decision-makers are under constant pressure to react quickly to market changes. Data intelligence supports them with faster and more reliable decision-making. Instead of relying on gut instinct, they rely on real-time data and sound analyses. One example: banks use data-intelligent fraud detection systems to recognise suspicious transactions much more quickly and reduce credit risks.
Retailers also benefit when they use data intelligence to analyse sales figures and customer preferences in order to optimally manage stock and customise offers. In the energy sector, utilities are using smart data to better forecast peak loads and avoid costly bottlenecks.
BEST PRACTICE at the customer (name hidden due to NDA contract) A logistics company was able to significantly reduce its delivery times by integrating traffic data and smart data, thereby increasing customer satisfaction.
Data intelligence in projects: Methodology and implementation
To successfully utilise data intelligence, it is advisable to first define clear project objectives. For example, a retail company can reduce its returns rate by analysing delivery and customer service data. At the same time, it can increase the efficiency of its marketing measures through data-based campaign management.
The use of AI-supported algorithms and machine learning is a proven practice for generating targeted smart data from big data. This increases the precision of analyses and helps managers to identify sources of risk at an early stage and plan countermeasures. Data governance and quality assurance are equally important to ensure that the data obtained is trustworthy and traceable.
BEST PRACTICE at the customer (name hidden due to NDA contract) An insurance company uses machine learning to recognise patterns from data on past claims and thus better calculate risks. This has enabled insurance premiums to be structured more fairly and fraudulent claims to be identified more quickly.
The added value of data intelligence in the everyday lives of managers
Data intelligence not only accompanies managers during analysis phases, but also supports them in managing their company on an ongoing basis. It enables greater flexibility and adaptability, as data streams are continuously monitored and automatic warnings are issued in the event of deviations. This allows decision-makers to remain capable of acting even in dynamic markets.
Data-intelligent applications also promote collaboration within teams: Standardised data platforms and transparent reports create trust and a common basis for decision-making. Industrial companies use this transparency to optimise production processes and sustainably reduce downtimes. In retail, sales and marketing departments benefit from a standardised view of customer data, which improves service.
My analysis
Unleashing data intelligence means making the transition from pure data collection to targeted analysis and selection of the information that is actually relevant. For leaders, this is the basis for making well-founded decisions, organising processes more efficiently and better assessing risks. In practice, there are a wide range of applications, from increasing production to personalised customer contact and risk detection in the financial sector. Linking big data with smart data is the key to establishing data intelligence in companies in the long term and securing competitive advantages.
Further links from the text above:
What is data intelligence and what does it mean?
Big data vs. smart data: is more always better?
Data intelligence: Big data and smart data for decision-makers ...
What is smart data?
What is data intelligence?
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