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

Today, data intelligence is a key competence for decision-makers in a wide range of industries. It is not just about collecting large amounts of data, but above all about utilising this data in a targeted manner and transforming it into valuable insights. The transformation from big data to smart data forms the basis for many companies to make more efficient and well-founded decisions.

Why data intelligence is essential for decision-makers

Decision-makers in all industries are faced with the challenge of managing huge amounts of data on a daily basis. Big data describes these enormous and diverse volumes, which are also generated at high speed. But data intelligence means more than this mass - it stands for the targeted extraction and filtering of relevant, high-quality information, so-called smart data.

For example, data-intelligent analyses enable financial companies to base portfolio decisions on reliable facts rather than assumptions. In logistics, the targeted use of data helps to make supply chains more transparent and recognise bottlenecks at an early stage. Personalised therapies are also becoming increasingly important in the healthcare sector thanks to data intelligence, as individual patient data can be precisely evaluated.

This shows that data intelligence not only takes into account the quantity of data, but also focuses on its quality and usability.

Data intelligence: big data and smart data in interaction

Big data describes the flood of raw data from a wide variety of sources - from social media and sensors to transaction data. However, this data is often unstructured and comprises many different formats. Smart data, on the other hand, is specifically processed information that is obtained from big data to support decision-making.

For example, an industrial company uses sensor data from production to enable predictive maintenance. This is where data intelligence comes into play: from the wealth of sensor information, precisely the data that indicates an impending malfunction is filtered out. In this way, cost-intensive downtimes can be avoided.

In marketing, many companies rely on smart data to precisely segment target groups and customise campaigns. This reduces wastage and strengthens customer loyalty.

With the help of artificial intelligence and machine learning, big data is automatically analysed in order to extract smart data from it. This significantly increases the efficiency and precision of decision-making.

Best practices from the field

BEST PRACTICE at the customer (name hidden due to NDA contract) A logistics company used data intelligence to extract precise KPIs from huge amounts of data. This enabled delivery times to be predicted more precisely and stock levels to be better managed. The result was significant cost savings and improved customer satisfaction.

BEST PRACTICE at the customer (name hidden due to NDA contract) A marketing agency implemented data-intelligent systems that analysed customer behaviour in real time. This allowed campaigns to be flexibly adapted and wastage reduced, which noticeably increased sales and strengthened customer loyalty.

BEST PRACTICE at the customer (name hidden due to NDA contract) In the manufacturing industry, data intelligence has made it possible to continuously monitor production parameters and take immediate countermeasures in the event of deviations. This has minimised downtimes and improved product quality.

Practical tips for the use of data intelligence

For managers, data intelligence begins with the selection of relevant data sources. Not all information contributes to success; what is important is the targeted integration of data that fits business processes.

Secondly, companies should use technologies such as AI and machine learning to quickly generate actionable smart data from the flood of data. Automated analyses take the pressure off employees and increase the speed of decision-making processes.

Thirdly, it is advisable to develop a company-wide data strategy. This ensures consistent data quality and avoids the creation of data silos, which can reduce the benefits of data intelligence.

Application examples from various industries

In retail, data-intelligent systems make it possible to recognise customer preferences and make personalised offers. This increases conversion rates and customer satisfaction.

In the financial sector, analysed smart data helps to better assess risks and make more informed investment decisions.

In the healthcare sector, targeted data analyses improve diagnostics and enable individually tailored treatment plans.

My analysis

Data intelligence is a crucial resource for decision-makers when it comes to profitably utilising today's immense amounts of data. The combination of big data and the targeted transformation to smart data creates an information base that enables agile and well-founded decisions. The examples from various industries show that data-intelligent solutions can be used in a variety of ways and generate sustainable benefits.

Decision-makers who systematically engage with data intelligence create better conditions for innovation and competitive advantage. It is crucial not only to utilise technologies, but also to promote data-oriented ways of thinking and adapt processes accordingly.

Further links from the text above:

Big data explained simply: definition and meaning

Big data vs. smart data: is more always better?

Smart data: definition, application and difference to big data

Big data: the utilisation of large amounts of data

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

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