Unleashing data intelligence: Big Data & Smart Data for Decision Makers

In today's digital business world Data intelligence plays a decisive role. Companies are faced with the challenge of managing huge amounts of data - known as big data. However, real added value can only be created by refining this data into smart data. Decision-makers need this high-quality information in order to make informed decisions and organise projects more successfully. The support provided by transruptions coaching helps with precisely this transformation from a flood of data to clear, usable insights.

Understanding data intelligence: From mass to quality

Big data encompasses the enormous variety and volume of data that companies generate every day. This includes data from social media activities, machine sensors, customer interactions and online transactions. The collected information alone does not constitute a benefit - instead, it serves as a raw material. Data intelligence ensures that this raw data is specifically filtered, analysed and transformed into smart data. This data is structured, meaningful and relevant to practice.

In retail, for example, data intelligence enables a precise analysis of purchasing behaviour. This enabled a clothing company to tailor its product range to different regional customer requirements, resulting in a significant increase in sales. Similarly, production companies are using sensor-based data intelligence to optimise their maintenance cycles in order to detect and prevent breakdowns at an early stage. The financial sector is also benefiting as risk analyses based on smart data are more precise and suitable insurance rates are offered.

BEST PRACTICE at the customer (name hidden due to NDA contract) An industrial company used data intelligence to analyse machine data in real time. Fault diagnoses were automated, maintenance was planned more efficiently and downtimes were reduced. The result was a sustainable increase in productivity and lower operating costs.

Why smart data is more important for decision-makers than big data

The difference between big data and smart data lies primarily in the quality of the information. While big data describes large, unstructured data volumes, smart data focusses on precise, filtered and verified data. Only this intelligently prepared data provides concrete recommendations for action.

Companies often report that unreflected big data volumes are hardly usable. One study showed that less than half of external data is correct or useful. Smart data, on the other hand, creates transparency, is secure and complies with data protection regulations. It offers a context that is specifically tailored to the needs of a company.

For example, a mobility provider uses smart data to analyse traffic flows in real time and thus optimise route recommendations. In retail, intelligent data helps to recognise seasonal trends at an early stage and adjust stock levels. In healthcare, too, smart data supports personalised treatment plans based on extensive, verified patient data.

Data intelligence in practice: concrete application examples

In the logistics industry, data intelligence is used to optimise transport routes. Sensors record vehicle statuses and traffic conditions in order to calculate more efficient routes in real time. This saves costs and improves delivery times.

An insurer uses smart data to better assess claims. Precise data analyses allow individual risks to be assessed more accurately. This results in customised offers for customers and an improved forecast of claims frequency.

In the energy sector, intelligent data helps to analyse consumption patterns and better control energy grids. This increases the use of renewable energies and improves grid stability.

How decision-makers can use data intelligence: Tips for getting started

Decision-makers should ensure that data is not only collected but also interpreted in a meaningful way. The following steps can pave the way for smart utilisation:

• Define clear goals: What questions should the data answer?
• Ensure data quality: Incorrect or incomplete data reduces the informative value.
• Use automated analysis processes: Artificial intelligence and machine learning can efficiently process large amounts of data.
• Consider the data context: The information must fit the business model and the processes.
• Prioritise data protection and security: Creating trust through transparent data utilisation.

Transruption coaching offers valuable support in the implementation of data intelligence. They help companies to implement big data and smart data projects in a structured manner and ensure practical relevance. This creates sustainable impetus for innovation and competitive advantages.

My analysis

has long been a key success factor for sustainable companies. Only those who specifically create smart data from the mass of big data can derive real benefit from the data. Decision-makers benefit from high-quality, relevant and well-prepared information that enables fast, effective decisions. Support from experienced coaches helps to successfully shape these complex processes and utilise the opportunities of digital transformation.

Further links from the text above:

From big data to smart data with data intelligence: How to ... [1]

Big data vs. smart data: is more always better? [2]

Unleashing data intelligence: Big Data & Smart Data for ... [11]

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