Big data and smart data - more than just data volumes
The topic of data is omnipresent in companies across all industries these days. Clients frequently report that they are faced with the challenge of managing vast amounts of information - often referred to as big data. It is not just about the sheer volume of data, but above all about the question of how this data can be used sensibly. Many are looking for support in gaining usable, precise insights from the huge amounts of data that really contribute to the implementation of projects.
Big data refers to extensive, often unstructured data volumes that pose a challenge due to their size alone. Without targeted processing, this data is often of little use for decision-making. This is where a well thought-out approach comes in: Smart data. This form of data is filtered, checked and tailored to specific issues so that it serves as the basis for active, efficient decisions.
Smart data as the key to targeted decisions
Smart data is created from big data when intelligent algorithms and filter processes are used to prioritise the quality and relevance of the data. Many industrial companies report that it is precisely this targeted data that enables them to respond more quickly and precisely to operational challenges - without being overwhelmed by the flood of data.
In the manufacturing industry, for example, smart data supports the monitoring and optimisation of machines using sensor data. By filtering out relevant key figures, technical faults can be recognised at an early stage and maintenance work can be planned in a targeted manner. This reduces downtimes and increases production efficiency.
In the logistics sector, smart data can be used to optimise transport routes in real time. For example, specialised algorithms help to filter out those factors from the flood of traffic data, weather information and delivery forecasts that are crucial for on-time delivery right now.
Smart data also enables the intelligent control of supply grids in the energy industry. Selected data from consumption measurements and weather forecasts can be used to adjust energy flows in line with demand and increase grid stability.
From data giants to data-based added value - practical examples
KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract) A manufacturing company was faced with the challenge of large amounts of data from various production lines. In KIROI coaching, we worked together to develop how relevant process data can be extracted using filter mechanisms and refined using machine learning methods. This resulted in smart data models that indicate production deviations at an early stage and efficiently support maintenance planning. This led to a noticeable improvement in plant efficiency.
KIROI BEST PRACTICE at ABC (name changed due to NDA contract) In the field of logistics, one customer used smart data to better manage its supply chains. The extensive GPS and weather data was consolidated and analysed in a targeted manner so that delivery times could be reliably predicted and alternative routes recommended. KIROI supported the step-by-step implementation of the algorithms in the operational process.
KIROI BEST PRACTICE at DEF (name changed due to NDA contract) As part of the KIROI coaching programme, an energy supplier worked on generating smart data from the large volumes of consumption data. Precise data analyses enabled consumption peaks to be better predicted and grid resources to be used in a more targeted manner. This led to improved grid stability and sustainable cost savings.
Assistance in dealing with smart data in the company
Many clients come to us with questions such as: How can I extract meaningful information from the wealth of available data? Which technologies are suitable for data preparation? How can AI methods be used in a targeted manner to create real added value? KIROI coaching expressly does not see itself as a panacea, but rather as an accompaniment, source of inspiration and support for projects that deal with artificial intelligence and data-based solutions. The focus is on developing customised solutions that are tailored to existing structures and objectives.
It often turns out that less data with higher quality is more effective than large, unmanageable quantities. The trick is to define the right questions and create the right database for them. Smart data can be used to optimise processes, explore new business models and realise competitive advantages.
My analysis
It is clear that the path from simply collecting large amounts of data to intelligently processed information is crucial for many companies. Smart data helps to utilise technological possibilities in a targeted and example-oriented manner - for example in industry, logistics or the energy sector. The role of experienced coaches is to accompany this transformation process pragmatically, at eye level and with a view to individual needs, thus providing sustainable impetus.
Further links from the text above:
[1] Big Data vs. Smart Data: Is More Always Better? - Netconomy
[2] Big data vs. smart data: is more always better? - Netconomy (en)
[3] Big Data to Smart Data | The evolution of data science and AI ...
[4] Smart data and artificial intelligence: technology, work, ...
[6] Smart Data: Definition, application and difference to Big ...
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