Today, **data analysis** is a key process for gaining valuable insights from large and complex data sets. The combination of big data and smart data plays a central role, particularly in the third phase of the KIROI approach. These two aspects complement each other perfectly because big data offers comprehensive information due to its sheer volume, while smart data provides relevant, filtered and high-quality data. This enables companies to make better decisions and organise operational processes more efficiently.
Data analysis - big data and smart data in interaction
Big data refers to very extensive and diverse data sets that are usually generated from a wide variety of sources and at high speed. Smart data, on the other hand, is the result of targeted processing and filtering of this raw data. It focuses on quality and relevance in order to enable well-founded analyses. This significantly increases the efficiency of data analysis.
A practical example from the logistics sector shows how smart data improves the analysis of sensor data from vehicle fleets. By intelligently filtering only relevant data points, routes can be optimised, which reduces fuel costs and shortens delivery times. It is also possible to react more quickly to changes in traffic or environmental influences.
In the healthcare sector, precise **data analysis** using smart data supports the customisation of therapies. Clinics analyse selected patient data in order to optimise treatment processes and reduce costs. This enables patient-centred care, which is becoming increasingly important.
BEST PRACTICE at a customer (name hidden due to NDA contract): We collected data from machines, processes and the environment for an industrial company and processed it using smart data. This improved production capacity utilisation, significantly reduced downtimes and increased efficiency. The targeted combination of big and smart data enabled the company to work more economically and with fewer resources.
Smart data as a decisive lever in data analysis
Smart data is created by intelligent algorithms that extract only the really important information from large data sets. This data quality is essential, as an analysis is only as good as the data on which it is based. Companies in various industries often report major challenges when it comes to ensuring data quality.
For example, manufacturers use smart data to predict the maintenance requirements of their machines and avoid unplanned downtime. Financial institutions benefit from precise transaction analyses that help to detect fraud attempts earlier. Retailers also use smart data by filtering sales data to precisely control stock levels and anticipate demand trends.
In traffic systems, authorities analyse sensor data to control traffic lights in real time, thereby improving traffic flow and reducing waiting times. These examples illustrate that smart data as part of data analysis provides impetus for efficiency improvements and innovations across all sectors.
Practical tips for successful implementation of data analysis with Big & Smart Data
For companies that want to master data analysis, it is important to find the right balance between data volume and data quality. The following ideas can help with this:
- Start with a clear objective: What specific questions should the data answer?
- Ensure data quality by continuously checking and correcting errors and duplicates.
- Use modern algorithms and machine learning to automatically filter big data and transform it into smart data.
- Integrate expert knowledge from your industry to better understand and interpret the data contexts.
- Visualise results using comprehensible representations such as heat maps, box plots and network diagrams.
Industrial companies, for example, can use intelligent data analyses to plan maintenance measures, in the financial sector smart data helps to make risks more calculable and in the retail sector it significantly improves inventory management.
Why a trustworthy database is essential as a foundation
A reliable database is essential for data analysis. Companies are therefore increasingly relying on automated checking mechanisms to ensure that data is up-to-date, complete and correct. Only in this way can data analysis models provide precise forecasts and effectively support decision-making processes.
An insurer, for example, can use valid customer data to create more precise risk analyses and calculate customised tariffs. In retail, accurate inventory management prevents stock shortages and overstocking, which both reduces costs and improves the customer experience.
Trustworthy data and transparent origins also ensure the necessary trust of stakeholders in the results of analyses and the strategies based on them.
My analysis
Data analysis** is a central building block for gaining actionable insights from the wealth of information. The combination of big data and smart data, as taught in KIROI Step 3, supports companies on their path to data-driven decision-making. Intelligent filtering of large amounts of data, supplemented by a trustworthy database, ensures relevant and reliable results. This allows processes to be optimised, costs to be saved and innovative approaches to be implemented. The practical examples from logistics, industry, healthcare and other sectors show how diverse and valuable well thought-out data analysis is today.
Further links from the text above:
Data analysis with Big, Smart & Trusted Data for success
What does smart data mean and what are the application scenarios?
Big and smart data - from statistics to data analysis
Big data: definition, application and future outlook
Smart + Big Data | Artificial Intelligence
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