Many companies today are faced with the challenge of deriving real benefits from the flood of digital information. Data intelligence - the ability to collect and analyse data in a meaningful way and turn it into smart decisions - has become a decisive factor for success. Many of you come to me with specific questions: How do we develop sustainable data strategies? How do we find the really relevant data in the information jungle? And how do we get employees from all departments on board?
From big data to smart data: data intelligence in practice
Big data describes the huge amounts of structured and unstructured data that companies generate every day[9][13]. The challenge is rarely in the collection, but in the selection: Not every byte is valuable, and many companies get lost in data management because they store too much information that is of little relevance[6]. This is where data intelligence comes into play. The aim is to filter smart data from big data, i.e. data that is clean, up-to-date, meaningful and directly usable[1][3].
A company in the logistics sector used to collect all sensor and tracking data - and often had no overview. Only through data intelligence and targeted filtering was it possible to analyse only those parameters that are really important for real-time delivery forecasts and supplier evaluations. This made it possible to shorten delivery times and reduce costs.
BEST PRACTICE at the customer (name hidden due to NDA contract): A production company relied on the combination of maintenance data, machine running times and workshop reports. AI-supported analyses were used to recognise error patterns in conjunction with order data. This allowed the process to be streamlined and warehouse planning to be optimised, as spare parts could now be reordered earlier and in a more targeted manner.
Data intelligence can also be seen in the ability to link different sources in a meaningful way. For example, a medium-sized retailer uses sales data, customer feedback and weather data to plan targeted advertising campaigns. This increases the accuracy of marketing because only data that is clearly linked to business success is used.
Data intelligence as a key value driver
More and more companies are recognising this: Simply collecting data does not add value. What is crucial is the ability to utilise data for specific issues - in other words, to develop genuine data intelligence[2]. This means handling data in such a way that it enables well-founded decisions, improves processes and drives innovation.
A classic example: analysing customer journeys across different channels. It helps project management companies to understand the actual use of software or services and recognise needs before customers leave[3]. This increases customer satisfaction because adjustments can be made at an early stage.
Data intelligence also makes it possible to target sales and marketing measures. A B2B company used smart data applications to identify which customers were particularly interested in new solutions. The target group approach was then controlled in a targeted manner, which led to an increase in efficiency and sales[8].
BEST PRACTICE at the customer (name hidden due to NDA contract): A service provider in the healthcare sector used data intelligence to analyse the capacity utilisation of surgeries and the workload of staff. This enabled waiting times to be reduced, working hours to be optimised and patient satisfaction to be noticeably increased because the processes were adapted flexibly based on data.
Using data intelligence specifically for your own company
There is no patent remedy, but many companies benefit from introducing data intelligence in a structured way. Here are some practical tips:
1. Define goals clearly: Think about which questions you want to answer. This is the only way to decide which data is really relevant and how it needs to be prepared[1].
2. Promote data literacy: Invest in further training for your employees. Data intelligence depends on everyone in the company being able to handle data sensibly.
3. Use technology sensibly: Use AI and machine learning to recognise relevant patterns. This allows large amounts of data to be filtered and analysed automatically[6].
4. Use current data: Do not rely on outdated information. Smart data is up-to-date because it has already been processed at the time of collection[1].
5. Start with pilot projects: Test data intelligence in small units first. This allows you to gain experience and adapt the procedure for larger projects.
A company from the energy sector used data intelligence to optimise the consumption of systems. By analysing sensor and load data, load peaks could be identified and energy use controlled in a targeted manner. This not only saved costs, but also made production more sustainable.
BEST PRACTICE at the customer (name hidden due to NDA contract): A medium-sized industrial company integrated data intelligence directly into its production planning. Sensor data from machines was linked with order and warehouse data. This made it possible to identify bottlenecks at an early stage, minimise downtimes and meet delivery deadlines more reliably.
Transruption coaching: Your partner for data intelligence
Many of my customers start with uncertainties. They know that data is important, but lack the structure to make the best use of it. This is exactly where we come in with transruptions coaching: We accompany you step by step on the path to data intelligence - from defining goals and selecting the right tools to sustainable implementation.
We take a practical approach to coaching. Together, we analyse your initial situation, identify your most important data sources and develop individual strategies for your business success. We use agile methods so that you can quickly see initial results and react flexibly to changes.
You benefit from experienced sparring that helps you to actively shape change. Your company always takes centre stage - and your employees learn how to use data intelligence profitably in their day-to-day work.
My analysis
Data intelligence is not a short-term fad, but a key building block for the future. Companies that manage to extract smart, usable information from big data create clear competitive advantages for themselves. They optimise processes, increase customer satisfaction and discover new business opportunities[2][6].
Data intelligence requires courage, curiosity and a clear strategy. And it thrives on the cooperation of everyone involved. Those who take action here will help shape the digital future - and benefit from greater efficiency, innovation and flexibility.
If you want to make better use of your data, it's worth taking the path to data intelligence. You don't have to know everything yourself - get the right support. Ask yourself: Where are we today? What do we want to achieve? And how can we get started together?
Further links from the text above:
Netconomy: Big Data vs. Smart Data - Is More Always Better? [1]
HubSpot EN: What is smart data? Definition, application and advantages [2]
Dataversity: Big data vs. smart data [3]
Netconomy EN: Big data vs. smart data - is more always better? [6]
B2B Smart Data GmbH: What is Smart Data? [8]
Google Cloud: What Is Big Data? [9]
Oracle: What Is Big Data? [13]
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