Data analysis as the basis for successful decisions
In modern companies, data analysis forms the foundation for targeted decisions and innovative strategies. More and more managers and teams are looking for ways to extract relevant information from the flood of big data and integrate it into their business model in a targeted manner. This is precisely where the KIROI Step 3 phase comes in: It provides a structured way to turn big data into smart, actionable insights.
Many clients find it difficult to analyse data at first because they are confronted with unmanageable mountains of data, a lack of structure and a lack of systematics. The difficulty often lies not in accessing data, but in processing and analysing it in a meaningful way. However, those who successfully master this step can sustainably strengthen their organisation and tap into new potential.
From big data to smart data: the core idea
Big data refers to huge, unstructured volumes of raw data from a wide variety of sources - from sensors and customer transactions to social media posts[6][8]. This data is of little value on its own unless it is specifically analysed. It is only through structured data analysis that it can be turned into smart data, i.e. processed, high-quality data sets with direct benefits for the company[1][2][5].
In coaching, I often accompany teams that have a lot of material but hardly know how to ask the right questions and select suitable algorithms. A decisive impulse here is not just to collect the data, but to focus it on the most important questions. Those who simply try out data science often get lost in collecting rather than applying it. The following therefore applies: first clarify objectives, then analyse data, not the other way round.
The transition from big data to smart data takes place in several steps. Firstly, data is consolidated and checked for quality, then relevant patterns and correlations are identified, for example using machine learning algorithms. The result is information that can be used specifically for business decisions, product development or customer loyalty[4][5].
Practical application examples for data analysis
The applications of data analysis are diverse and affect every sector - from industry and the service sector to public administration. I will explain three typical fields of application with specific examples below.
BEST PRACTICE with one customer (name hidden due to NDA contract)A company in the consumer goods industry was faced with the challenge of combining a huge amount of POS data, online tracking and social media feedback. Together, we analysed which products suited which customers, identified cross-selling and upselling potential and developed targeted offers. The data analysis led to an increase in customer loyalty of 12 % within a year because we used smart data for targeted communication.
In the manufacturing sector, companies are successfully utilising data analysis in predictive maintenance. Sensors on machines continuously measure operating data, algorithms recognise anomalies at an early stage and warn of failures. This reduces downtimes and optimises production processes[3].
Financial service providers benefit from data analysis by combining transaction data, market movements and customer behaviour. This enables them to identify risks at an early stage, personalise advisory services and develop tailor-made products for different target groups.
Step-by-step guide to implementation
How do you get started with data analysis and how can KIROI Step 3 provide concrete support? The following tips will help you to successfully organise the process:
Firstly, objectives and questions must be clearly formulated. Only those who know what they want to know can analyse data in a targeted manner. A typical mistake is to collect too much data without scrutinising its usefulness. In consulting, I support teams in finding the right questions and aligning the analysis accordingly.
In the second step, the data quality should be checked. Not all data is meaningful - there are often duplications, gaps or errors. Careful data cleansing is essential to ensure that the analysis delivers valid results. Data scientists use special tools and methods for this, such as data profiling or automated plausibility checks.
The third step involves the actual data analysis. Algorithms, statistics and visualisations are used here. Companies that integrate machine learning and advanced analytics can recognise patterns that would not be visible using traditional methods[2][3]. A continuous review of the results ensures that the knowledge gained is actually put into practice.
BEST PRACTICE with one customer (name hidden due to NDA contract)A logistics service provider wanted to optimise the utilisation of its fleet. We structured the driving times, maintenance intervals and routes in a central data lake, analysed the data using algorithms and derived specific suggestions for vehicle scheduling and maintenance. This transparency led to a reduction in unnecessary empty journeys and costs.
Mastering challenges and stumbling blocks
Realising successful data analysis is not always easy. Many companies report that although they collect a lot of data, the actual utilisation of this data is slow. Frequent reasons for this are a lack of data quality, unclear responsibilities or a lack of expertise within the team.
Another problem is the IT infrastructure. Data is often distributed across different systems, which makes consistent analyses difficult. A sensible recommendation is to set up a central platform on which all relevant data is bundled and made available for analyses.
Data analysis also requires the protection of sensitive information. Data protection and IT security should be considered from the outset. Companies that neglect these aspects risk legal consequences and a loss of trust among customers and partners[1][5].
My analysis
Data analysis is much more than just collecting facts and figures. It is the key to creating usable knowledge from confusing amounts of data and making companies capable of taking action. A structured approach to this process ensures transparency, recognises opportunities at an early stage and can reduce risks in a targeted manner.
The transformation from big data to smart data is best achieved when teams ask the right questions, ensure data quality and utilise modern analysis methods. Continuous development is essential because technologies and requirements are constantly changing.
As a transruption coach, I support organisations in professionalising their analysis processes, integrating new tools and making smart decisions. Organisations that make data analysis an integral part of their strategy can hold their own against the competition in the long term and develop innovative solutions.
Further links from the text above:
b2bsmartdata - What is Smart Data? [1]
HubSpot - Smart data: definition, application and benefits [2]
Appvizer - Smart data or the intelligent use of data [3]
O2 Magazine - Smart data: definition, application and difference to big data [4]
Kobold - What is smart data? Definition and explanation of the term [5]
Datavance - What is Big Data? Simply explained [6]
expedition.digital - Glossary Big Data and Smart Data [7]
MFR - Big data: definition, application, tips [8]
sauldie - Mastering data analysis: KIROI step 3 with Big & Smart Data [9]
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