Data intelligence as the key to data-driven companies
In an increasingly digitalised world, data intelligence determines the competitiveness, innovation and future-proofing of companies of all sizes. Decision-makers quickly realise that huge amounts of data alone rarely bring the hoped-for added value because raw data is hardly usable without the right structure and analysis. Only when large and complex information is specifically filtered, processed and interpreted does data intelligence realise its full potential - and become a key success factor for data-based decisions[3].
Many companies have been collecting product, customer and process data for years. However, there is often a lack of access to the relevant core information because departments work in isolation or because the technology for analysing data is inadequate. This is precisely where data intelligence comes in: it helps to transform big data into smart data - in other words, to filter out what is really helpful from the sheer mass of information[1][6].
Decision-makers who make consistent use of data intelligence gain time advantages, recognise trends earlier and are better able to assess risks. They boost the efficiency of their processes and increase customer satisfaction - and thus benefit sustainably from data-driven impulses for their business model[3].
Data intelligence live: How big data becomes smart data
Big data describes huge, often unstructured amounts of data that flow in daily from various sources - such as sensors, machines, customer systems or web applications[9][13]. The key difference to smart data is that these masses first have to be processed in order to be truly usable. Only filtering, cleansing, contextualisation and intelligent analysis turn raw data into high-quality, practically relevant information[1][6].
The transformation from big data to smart data is achieved through the targeted use of algorithms, artificial intelligence, machine learning and modern analysis tools - enabling companies to gain precise, contextualised and quickly available insights[5][6]. These can be directly translated into concrete measures because they are tailored to the company's individual requirements.
Examples of the smart use of data intelligence
A look at the industry shows: Data intelligence is already common practice in many areas. In customer service, for example, a filter customer analyses historical support requests to filter out the topics that generate a particularly high number of queries and develops new self-service functions. This significantly reduces the workload of the service team while increasing customer satisfaction.
In logistics, another customer relies on sensor data from the fleet to predict maintenance intervals and thus minimise unplanned downtime. Millions of measuring points form the basis of the data, but only intelligent evaluation turns it into real smart data - with a measurable effect on process optimisation[7].
In marketing, a third user utilises algorithms to tailor advertising messages to the interests of individual target groups. The data analysis provides information on which customers react most strongly to which offers - and thus significantly increases the conversion rate without increasing wastage[8].
These examples show: Data intelligence is not an end in itself, but is always created where data is read, understood and translated into concrete steps. It is this transformation that makes the difference between a flood of information and a genuine basis for decision-making.
Accompanying data intelligence: The path from Big Data to Smart Data
More and more decision-makers are specifically looking for support in order to realise data intelligence within their own company. The transformation rarely succeeds on its own because there are many hurdles to overcome - from technical issues and data protection requirements to organisational change processes.
Transruption coaching accompanies companies on this path and helps to systematically develop access to data intelligence. Experience shows: We usually start by taking stock of the existing data sources and their linking potential. In the next step, we work together to analyse which issues are particularly relevant for the company - and how smart data can be obtained from big data in a targeted manner[6].
A clear focus on the quality of the data is important: Only clean, consistent and correctly interpreted information provides a sound basis for decision-making[2]. We are jointly developing initial pilot projects, for example for predictive maintenance, the optimisation of marketing campaigns or the management of supply chains[8].
Continuous dialogue is essential during implementation: Workshops, data reviews and the involvement of specialist departments ensure that data intelligence does not remain an IT project, but becomes a reality in day-to-day operations. This results in sustainable success that can be measured - and the company becomes fit for the data-driven future.
Extract from practice: How data intelligence makes an impact
BEST PRACTICE at the customer (name hidden due to NDA contract)A leading logistics service provider was faced with the challenge of increasing the efficiency of its own depots, but was unable to make progress on the basis of individual key figures. As part of a structured data intelligence project, all relevant machine data, delivery times and weather information were brought together in a new data model. Artificial intelligence helped to identify patterns and optimisation potential that had previously remained hidden. The result: throughput times fell by 15 per cent, machine availability increased and employees were targeted at the process steps where they had the most influence on success.
Other customers with a similar approach report noticeable effectsA manufacturer of consumer goods used data from e-commerce to reduce returns. By specifically analysing order samples and accompanying information, it became clear which products were returned more frequently - and how the returns rate could be demonstrably reduced through targeted adjustments in the online shop. The data intelligence not only provided insights here, but also concrete options for action that were transferred directly into the process.
Data intelligence has also become indispensable in retailLarge chain stores are increasingly relying on intelligent warehouse analyses to optimise stock levels and avoid overstocking. By combining warehouse data, weather forecasts and online sales figures, it is possible to predict which items will be needed where - and how the supply chain can be optimised. This reduces costs and increases customer satisfaction because important products are always available.
Tips for getting started with data intelligence
The start of data intelligence begins with a clear question: Which decisions should be made based on data? What information is missing? This allows relevant data sources to be identified and tapped in a targeted manner.
The second step is about data quality: only clean, consistent and complete data sets provide reliable results. Here, it is worth the effort to systematically cleanse and enrich the database, as this significantly improves subsequent analyses.
It is also important to choose the right analysis tools: Modern business intelligence software, AI applications and machine learning algorithms help to transform big data into smart data. Initial pilot projects are often enough to make the added value of data intelligence tangible.
Make sure to involve specialists from the relevant departments at an early stage. Solutions that are practical and sustainable can only be developed through dialogue between IT, specialist departments and management. Change management often accompanies the introduction of data-driven decision-making processes.
And don't forget data protection and IT security: processing and analysing large volumes of data is subject to strict legal requirements. Therefore, make sure you have a data protection-compliant infrastructure and transparent processes from the outset[6].
My analysis
Today, data intelligence is not an add-on, but a key lever for innovation, efficiency and customer satisfaction in companies. The targeted use of smart data makes it possible to gain specific insights from the flood of information that are relevant to current and future challenges[3]. Data intelligence helps companies to understand and utilise data as a valuable resource - and is therefore the core of a modern, data-driven business model.
Further links from the text above:
Data intelligence: How decision-makers use big & smart data [3]
Smart data: How intelligent data is shaping our future [1]
Big data vs. smart data: is more always better? [2]
From big data to smart data with data intelligence [13]
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