In the digital age, data is a key success factor for companies. But not all data is equally valuable. In particular, the use of Smart Data supports decision-makers in transforming complex big data volumes into profitable insights in a targeted manner. This intelligently processed data provides well-founded impetus for strategic decisions and helps to organise business processes effectively.
Smart data as the key to better decision-making
Many companies have a large amount of data, but it is often unstructured and not very meaningful. Smart Data refers to data records that have been carefully checked and filtered to ensure high data quality and are therefore immediately usable. Such data enables managers to act faster and more precisely because relevant information is directly available.
An example from the manufacturing industry shows how smart data can be used to optimise production planning: Sensors record machine data in real time, which is then filtered and analysed. This allows maintenance work to be controlled in advance (predictive maintenance), which reduces unplanned downtime and increases productivity. Similar approaches can be found in the logistics sector, where real-time data is used to control supply chains, and in retail, where customer analyses are used to make marketing campaigns more targeted.
BEST PRACTICE at the customer (name hidden due to NDA contract) By using smart data, a company in the consumer goods industry was able to analyse customer behaviour data in such a way that personalised offers were created automatically. This led to a significant increase in conversion rates and higher customer satisfaction.
From big data to smart data: quality over quantity
The term big data refers to the huge amounts of data that companies generate. This contains valuable information, but is often unstructured and difficult to manage. Smart Data is created when this raw data is filtered, cleansed and contextualised. This results in high-quality data sets that are specifically tailored to the respective industry and company objectives.
In the healthcare sector, for example, patient data from various sources is brought together so that doctors and nursing staff can make informed diagnoses more quickly. In the energy industry, smart data analyses help to accurately predict energy consumption and thus use resources more sustainably. In the banking sector, too, credit decisions benefit from cleanly processed data that allows risks to be better assessed.
Typical challenges such as data overload or quality deficiencies are reduced so that employees can work with clear, usable information. This is important because clients often report that simply having access to large amounts of data without an intelligent filter leads to excessive demands.
Practical tips for the successful use of smart data
So that companies Smart Data effectively, it is advisable to observe a few basic principles:
- Ensure data quality: Regular checks and cleansing reduce sources of error and increase the reliability of data.
- Create context: Data is only valuable in relation to the respective question. A clear target definition helps to filter out relevant information.
- Use technologies: AI and machine learning are ideal for recognising patterns in large amounts of data and generating smart data.
- Networking departments: Data silos prevent a holistic view. Close collaboration between IT, specialist departments and management promotes success.
A supplier from the automotive industry used smart data to integrate the vehicle management process with telemetry data. This led to improved maintenance cycles and higher vehicle availability.
In the mail order business, smart data supports customer service by deriving optimisation potential from ratings and returns data. This increases customer satisfaction in a targeted manner.
BEST PRACTICE at the customer (name hidden due to NDA contract) Smart data analyses enabled the optimisation of load management processes at a medium-sized energy supplier. The improved database led to savings in procurement costs and more stable grid utilisation.
Smart data as a companion for sustainable corporate success
For many decision-makers, the integration of smart data into existing processes is more than just a technical update - it supports strategic projects and promotes a data-driven corporate culture. This enables managers to better assess risks, drive innovation in a more targeted manner and utilise resources more efficiently.
Transruption coaching can make a valuable contribution here. It supports companies in sensibly managing the transition to intelligent data utilisation and recognising stumbling blocks at an early stage. Clients often report that this support helps to overcome internal resistance and create new perspectives for successful data projects.
In the telecommunications sector, for example, smart data has been used to analyse network utilisation more precisely in order to flexibly adapt capacities to peaks in demand. In the pharmaceutical sector, intelligent data helps to speed up the market launch of new products by better linking research and market requirements. Insurance companies also benefit from smart data when predicting damage and assessing risk.
My analysis
The targeted use of Smart Data opens up a wide range of opportunities for companies to sustainably increase their success. By focussing on high-quality, context-related and action-oriented information, decisions can be made on a much more sound basis. Practical examples from various industries illustrate how smart data increases efficiency in processes, improves customer satisfaction and provides targeted support for innovation. Professional support, such as transformation coaching, can help to fully utilise the potential of smart data and overcome individual challenges.
Further links from the text above:
HubSpot: What is smart data? Definition, application and advantages
Esa Automation: Difference Between Big Data and Smart Data
Krauss GmbH: Smart Data Definition - Basics and Applications
Vodafone Business Blog: What is Smart Data and how does it work?
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