Data intelligence for sustainable success in practice
Data intelligence is becoming increasingly important in numerous industries, as it supports the targeted utilisation of large amounts of data. Today, companies are faced with the challenge of not only collecting large amounts of data, but also extracting the right benefits from this data. This requires a combination of analytical expertise and a strategic view of data quality.
Data intelligence: more than just collecting data
Many enquiries show that companies often do not know how to gain truly intelligent insights from large amounts of data. This offers the opportunity to develop data intelligence - a process that goes far beyond the mere collection of big data. Rather, it involves the targeted filtering, evaluation and processing of data so that it can be used effectively in line with the company's objectives.
An example from the production industry illustrates this: Raw data from sensors on machines is processed by intelligent data management in such a way that maintenance cycles can be predicted and downtimes minimised. This turns unfiltered big data into smart data - data-intelligent, precise and usable.
Data intelligence can also help retailers to better understand customer behaviour. Analysed data on purchasing habits and preferences can be used to develop personalised offers. This not only increases customer satisfaction, but also boosts sales.
Data intelligence: the key to decision support
The quality and relevance of data are at the centre of data intelligence. Many companies report that they are able to make much more informed decisions through the targeted processing and contextualisation of data. The challenge lies in filtering out the relevant data from the wealth of information and providing it in a form that enables action-oriented insights.
In the logistics sector, for example, data-intelligent control helps to make supply chains more efficient. Real-time data is analysed in order to optimise transport routes and identify bottlenecks at an early stage. This leads to greater delivery reliability and lower costs.
KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract)
A manufacturing company was able to improve its plant availability with the support of data-intelligent methods. By analysing sensor data in real time, an early warning system was developed that detects potential faults and recommends measures in good time. The company reports noticeable increases in efficiency and better resource planning.
KIROI BEST PRACTICE at ABC (name changed due to NDA contract)
A service provider in the financial sector used data-intelligent processes to segment customer profiles more precisely. This enabled a targeted approach with relevant offers, which increased the conversion rate. The support from KIROI coaches provided valuable impetus for the integration process of the new technologies into existing systems.
KIROI BEST PRACTICE at DEF (name changed due to NDA contract)
A retailer used data-intelligent analyses to optimise its stock management. With the help of the insights gained, order quantities could be better matched to actual demand. This reduced excess stock and improved the availability of goods for customers.
Data intelligence as ongoing support
Many of those who turn to us are not looking for a quick solution, but for continuous support in order to develop data-intelligent projects in a sustainable way. Especially when introducing new technologies or making the transition from big data to smart data, clients report how helpful it is to have experts at their side to support them with project planning, implementation and reflection.
KIROI sees itself as a partner that provides impetus and offers methodical support without making promises of perfect solutions. The result is a trusting collaboration focussing on customised processes and applications.
My analysis
Data intelligence is increasingly becoming the decisive factor in profitably utilising the potential of digital data. Targeted methods are needed to extract relevant information and translate it into concrete business decisions. Practical experience shows that companies can increase their efficiency and respond better to customer needs with data-intelligent solutions. Professional support that provides strategic and operational assistance makes a significant contribution to the successful implementation of projects involving data-intelligent processes.
Further links from the text above:
[2] Big Data vs. Smart Data: Key Insights for Operational Optimisation
[3] Big Data vs. Smart Data: Is More Always Better?
[4] Big Data vs. Smart Data: Valuable Insights to Optimise Operations
[5] Big Data Basics: Definition, Smart Data
For more information and if you have any questions, please contact Contact us on the topic or read more blog posts on the topic Artificial Intelligence Blog here.
Deutsch 
English (UK)