{"id":351331,"date":"2024-12-22T22:10:51","date_gmt":"2024-12-22T21:10:51","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenanalyse-big-data-smart-data-kiroi-4\/"},"modified":"2024-12-22T22:10:51","modified_gmt":"2024-12-22T21:10:51","slug":"data-analysis-big-data-smart-data-kiroi-4","status":"publish","type":"post","link":"https:\/\/sauldie.org\/en\/data-analysis-big-data-smart-data-kiroi-4\/","title":{"rendered":"Mastering data analysis: KIROI step 3 to big and smart data"},"content":{"rendered":"

Data analysis is a key competence in business today. It enables large amounts of data - known as big data - to be analysed in a targeted manner and converted into valuable information. KIROI Step 3 provides a practical demonstration of how companies can master this complex process in order to generate intelligent smart data from huge amounts of raw data and thus make better decisions.<\/p>\n

From big data as a raw material to intelligent insights through data analysis<\/h2>\n

Many industries are faced with the challenge of structuring and utilising vast amounts of data. Big data refers to enormous amounts of data that are often unstructured and so diverse that traditional IT solutions alone are no longer sufficient. Data analysis transforms this raw material by extracting information using algorithms and artificial intelligence methods. This results in smart data - high-quality, reliable and processed data that increases tactical and strategic benefits.<\/p>\n

For example, e-commerce companies can use data analysis to precisely track purchasing behaviour and create personalised offers. In the manufacturing industry, targeted analyses are used to make production processes more efficient and detect errors at an early stage. Data-based analyses also support the optimisation of treatment processes in the healthcare sector by intelligently linking patient data and thus enabling better treatment decisions.<\/p>\n

Practical examples from different industries<\/h3>\n

1 A logistics service provider uses data analysis to monitor supply chains in real time. This allows delays to be predicted and alternative routes to be suggested, saving costs and increasing customer satisfaction.<\/p>\n

2. in the financial sector, analytical models help to carry out risk assessments based on large amounts of data faster and more precisely, which improves the decision-making processes in lending.<\/p>\n

3. by analysing customer interactions in stationary and online retail, retailers gain valuable insights into trends that make product placement and marketing campaigns more targeted.<\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> showed how KIROI Step 3 in combination with modern data analysis was able to increase the efficiency of a production line in the consumer goods industry by 15 % within a few months by recognising bottlenecks at an early stage and optimally distributing resources.<\/i><\/p>\n

KIROI's support in the implementation of data analysis steps<\/h2>\n

Data analysis is not a sure-fire success, but requires a systematic approach and competent support. The KIROI concept focuses on practical support for companies in the transformation from big data to smart data. This means establishing methods, tools and strategies, improving data quality and ensuring data protection and security.<\/p>\n

The support process includes, for example, assistance with data acquisition from internal and external sources, the consolidation of data sets and the development of automated analysis models. Particularly in complex projects, clients report that the structured dialogue with experienced coaches helps to overcome obstacles and provide impetus for adapting the data strategy.<\/p>\n

Another example comes from the software sector, where the user-friendliness of a platform was improved by analysing data. Customer behaviour was understood more precisely and systematically implemented in adjustments. In the area of marketing, KIROI helps companies to plan targeted campaigns based on their analysed customer data and thus sustainably increase conversion rates.<\/p>\n

Recommendations for successful data analysis<\/h3>\n

- Establish a clear objective: Know exactly which questions your data analysis should answer.<\/p>\n

- Rely on the quality of the data: Only well-maintained and valid data delivers meaningful results.<\/p>\n

- Use suitable tools and technologies: Modern algorithms and AI-based solutions support the analysis.<\/p>\n

- Implement a continuous learning and adaptation culture: Data analysis is an ongoing process that requires regular review and optimisation.<\/p>\n

- Seek expert advice: Experienced support from coaching and consulting teams can reduce complexity and accelerate learning success.<\/p>\n

The importance of data analysis in the digital transformation<\/h2>\n

Nowadays, data analysis forms the basis for many innovation processes. Companies that manage to generate relevant smart data from big data can not only make business processes more efficient, but also recognise completely new value creation potential. In dynamic markets in particular, targeted analyses help to better understand customer needs and respond in an agile manner.<\/p>\n

Future competitiveness also depends heavily on how well companies understand and utilise data. Data analysis is therefore not a trivial tool, but an essential skill that supports and promotes strategic decisions.<\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b>In an international B2B company, continuous data analysis enabled an accurate assessment of market trends. This enabled the sales department to carry out focussed acquisition campaigns and thus significantly increase the closing rate.<\/i><\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b>In the telecommunications industry, data analysis helped to network customer behaviour across different channels. This led to improved customer loyalty and a reduction in the churn rate.<\/i><\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b>In the automotive sector, smart data was used to gain a better understanding of service data, which optimised maintenance cycles and reduced downtimes.<\/i><\/p>\n

My analysis<\/h2>\n

Data analysis is a central component of modern corporate management. It helps to gain valuable, applicable insights from disorganised data volumes. The transition from big data to smart data plays a particularly crucial role. Those who master this step with structured support - such as KIROI Step 3 - lay the foundation for sustainable efficiency gains and innovative developments. Companies that rise to this challenge effectively secure their competitiveness in the digital age.<\/p>\n

Further links from the text above:<\/h2>\n

\n[1] What is smart data?<\/a>
\n[2] Smart data: definition, application and difference to big data<\/a>
\n[3] What is big data? Simply explained<\/a>
\n[4] What is smart data? Definition and explanation of the term<\/a>
\n[5] What is smart data? Definition, application and advantages<\/a>
\n[6] Mastering data analysis: KIROI step 3 to Big & Smart Data<\/a>
\n[7] Smart data, or the intelligent use of data<\/a>
\n[8] Big data, smart data: the most important keywords explained<\/a>
\n[9] Big and smart data - from statistics to data analysis<\/a>\n<\/p>\n

For more information and if you have any questions, please contact Contact us<\/a> or read more blog posts on the topic Artificial intelligence<\/a> here.<\/p>","protected":false},"excerpt":{"rendered":"

Datenanalyse ist heute eine zentrale Kompetenz in der Wirtschaft. Sie erm\u00f6glicht es, gro\u00dfe Datenmengen \u2013 bekannt als Big Data \u2013 gezielt zu durchdringen und in wertvolle Informationen umzuwandeln. KIROI-Schritt 3 zeigt praxisnah, wie Unternehmen diesen komplexen Prozess meistern k\u00f6nnen, um aus riesigen Rohdaten intelligente Smart Data zu generieren und somit bessere Entscheidungen zu treffen. Vom … Read more<\/a><\/p>","protected":false},"author":1,"featured_media":351330,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_ef_editorial_meta_date_first-draft-date":"","_ef_editorial_meta_paragraph_assignment":"","_ef_editorial_meta_checkbox_needs-photo":"","_ef_editorial_meta_number_word-count":"","footnotes":""},"categories":[3061,3238,200,3244,3246,3247,4004],"tags":[1579,282,3286,3497,3618,3973,3125,3062,1630,3194,1584,2739,5783],"class_list":["post-351331","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kiroi-blog","category-big-data-smart-data","category-digital-leadership","category-digitale-transformation","category-ecommerce-digitaler-handel","category-hr-arbeit-teams-4-0","category-sanjay-sauldie-glossar","tag-bigdata","tag-compliance","tag-datenanalyse-2","tag-datenintelligenz","tag-ethikrichtlinien","tag-innovationdurchachtsamkeit","tag-ki-2","tag-kiroi","tag-kuenstlicheintelligenz","tag-nachhaltigkeit","tag-smartdata","tag-unternehmenskultur","tag-verantwortungsketten","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-25"],"rmp_feedback_val_new":"4635.04","_yoast_wpseo_primary_category":"K\u00fcnstliche Intelligenz","_yoast_wpseo_focuskeywords":null,"rmp_vote_count":"944","rmp_rating_val_sum":"4635.04","rmp_avg_rating":"4.91","_yoast_wpseo_focuskw":"Datenanalyse","_yoast_wpseo_title":"Datenanalyse meistern: KIROI-Schritt 3 zu Big und Smart Data","_yoast_wpseo_metadesc":"Meta Description: Datenanalyse verwandelt Big Data in Smart Data und optimiert Entscheidungen. 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