Unleashing Data Intelligence: KIROI's Step 3 to Big & Smart Data

Understanding Data Intelligence in Practice

Data intelligence is gaining increasing importance in many industries, precisely because companies today have to deal with an enormous volume of data. This raises many questions: Which data is really important? How can it be used efficiently? How can focused insights be gained from large, unmanageable amounts of data? This is exactly where the concept of data intelligence comes in and effectively supports projects involving artificial intelligence.

From Data Mountain to Targeted Decision Support

The challenge is not only to collect large volumes of data – so-called Big Data – from the flood of information, but to transform it meaningfully. Data intelligence means processing raw data in such a way that it becomes immediately usable. This is referred to as Smart Data: data that is filtered, cleaned, and contextualised. This allows companies to react faster and make more targeted decisions.

A manufacturing company, for instance, receives a large amount of machine data in real time. Thanks to intelligent processing, it is immediately apparent which machines require maintenance or which processes can be optimised. This not only saves costs but also increases operational reliability.

Data intelligence also helps in logistics: sensor data from vehicles is evaluated to dynamically adjust routes and optimise delivery times. Intelligent data processing thus ensures more efficient processes and better customer experiences.

In retail, customer data can be combined from various sources to plan marketing campaigns with precision. Only truly relevant information is utilised, which minimises wastage.

KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract)

As part of a digitalisation project, KIROI coaching supported a manufacturing company in creating a clear overview from complex sensor and machine data. Through targeted filtering and bundling of information, bottlenecks could be identified early and maintenance work precisely planned. The project teams reported how data intelligence significantly improved the understanding of processes and allowed resources to be utilised more efficiently.

Why raw data volumes aren't everything

A common misconception is that more data automatically leads to better decisions. However, the sheer volume – Big Data – can be overwhelming and confusing. Without proper filtering and processing, information floods can threaten, hindering actions rather than promoting them.

Data intelligence, on the other hand, focuses on quality, or "smart data": only relevant and reliable information is incorporated into the decision-making process. This improves the precision of analyses and often makes solutions more readily achievable.

In the energy sector, for example, large amounts of measured data are collected. Intelligent data processing makes it possible to predict peak loads or outages precisely and thus make power grids safer. Without this processing, the volume of data would remain unmanageable and useless.

In the financial sector, data intelligence is evident when analysing transactions. Targeted filtering is the only way to quickly recognise suspicious patterns and prevent fraud attempts.

Intelligent data solutions also support patient care in the healthcare sector. Sensors continuously supply vital data, which is analysed by intelligent systems and interpreted in the correct context – for better diagnoses and treatment plans.

KIROI BEST PRACTICE at ABC (name changed due to NDA contract)

An SME in the mechanical engineering sector requested support in implementing data-based quality control. The integration of smart data significantly reduced scrap rates. The coaching team provided impetus on how processes can be adapted in a data-oriented manner and how employees can confidently handle this new data intelligence.

Data intelligence as a companion for AI projects

Many companies come to us with the desire to harness the potential of AI, but they face the complex task of preparing data meaningfully. This is where data intelligence shows its role as an essential step towards successful AI integration.

KIROI-Coaching acts as a companion, providing impulses on how data streams can be analysed and transformed into customer-oriented solutions. The goal is to overcome not only technical but also organisational challenges in handling data.

When introducing new systems, clients often report how important it is for data intelligence to respond flexibly to different requirements while maintaining a focus on relevant information. This is how data becomes a sustainable competitive advantage.

In the manufacturing industry, for example, the combination of sensor data and AI analyses can increase efficiency and reduce downtime. At the same time, employees need comprehensible tools to correctly interpret and use smart data.

The coaching shows ways to achieve this balance – without unrealistic promises, but with practical steps towards empowerment.

KIROI BEST PRACTICE at DEF (name changed due to NDA contract)

A logistics company engaged KIROI coaching to discover data-driven optimisation potential. Targeted analyses of transport and warehouse data resulted in an intelligent control system. This enabled users to better substantiate operational decisions and react dynamically.

My analysis

Data intelligence forms a central foundation for today's use of data in companies. The goal is to extract useful and reliable insights from the volume of data to promote innovation and efficiency. KIROI-Coaching actively supports projects and provides impetus on how data strategies and AI processes can harmoniously come together.

What's important is the responsible handling of data, without claims of efficacy, but with well-founded support for companies of all sectors. This ensures a sustainable step from data overload to targeted intelligence.

Further links from the text above:

[1] What Is Smart Data? – DATAVERSITY

[2] Unleashing Data Intelligence: KIROI's Step 3 to Big & Smart Data

[3] The difference between Big Data and Smart Data can be summarised as follows: Big Data refers to extremely large and complex datasets that are difficult to process using traditional data processing applications. It is characterised by the "three Vs": Volume (amount of data), Velocity (speed at which data is generated and processed), and Variety (different types of data). Big Data often requires specialised tools and techniques to extract value. Smart Data, on the other hand, is refined, processed, and contextualised data that is ready for analysis and actionable insights. It's not necessarily about the sheer volume of data, but rather its quality, relevance, and usability. Smart Data is the result of filtering, transforming, and analysing Big Data to extract meaningful and pertinent information. It focuses on delivering specific, accurate, and timely insights that can drive decision-making. In essence, Big Data is the raw material, and Smart Data is the finished product derived from it.

For more information and if you have any questions, please contact Contact us on or read further blog posts on the topic Artificial Intelligence Blog here.