S.D.I. English Edition Newsletter: What does it means to extract insights by crunching data

Extracting insights from data is a process that involves collecting, analysing and interpreting data to discover useful information that can guide business decisions. A process designed to help organisations improve their understanding of customers, optimise operations, identify new market opportunities and make informed strategic decisions.

In data management, an 'insight' refers to significant understanding or discovery gained through the analysis of data. Insights may emerge when examining and interpreting patterns, trends or correlations within large data sets. These discoveries can then be used to inform decisions, guide business strategies, improve processes or offer a new perspective that was not apparent prior to analysis.

For example, a company might analyse sales data to discover that a particular product sells best in a specific region or time of year. This insight could then drive targeted marketing decisions or influence inventory management. In essence, insights offer added value because they transform raw data into actionable information.

The business processes involved in the analysis can be viewed individually or in parallel, i.e. in serial mode analysing one single function of the business at a time or using an End-to-End concept, going deep into all phases of the business.

All processes can trigger insights from the data, following a logical flow Objective to be pursued, Available data and the Action to be taken to achieve the insight objective

What Insights Can We Gain from Analysing Sales Data

For example, in the sales department we would analyse Customer Behaviour with the aim of better understanding how customers interact with products or services.

The source of the data in this case will be web browsing data, purchase data, customer feedback to identify buying patterns, product preferences, and friction points in the user experience.

The result will be optimisation of the customer journey on the website, personalisation of offers, and improved customer service.

What goals to achieve in Operations

Following the same reasoning, let's look at some insights we can achieve in the field of operations, extracting insights using operational data; these results help companies optimise performance, reduce costs, improve customer service and make informed decisions.

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How to improve the financial cycle

Every process has its data; extracting financial insights from data requires analysing quantitative and qualitative information to identify trends, risks and opportunities that can influence investment decisions, risk management and strategic planning.

Refining Logistics

In the context of logistics, insights gained from data analysis can provide valuable information to optimise operations, improve efficiency and reduce costs. Here are some examples of logistics data that can be extracted using insights from data:

Most important analysis: End-To-End, the all-round view

In sales and production, an end-to-end analysis starting from order receipt to order fulfilment can reveal various insights into each of the different segments of the value chain. It means analysing each individual process, but with a 'helicopter' overview that allows the 'company' as a whole to be analysed for insights with a complete view.

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Conclusion: data must be crunched for answers

'Data crunching' is an informal expression referring to the detailed analysis of data to extract relevant information or hidden answers. In the industrial, commercial and manufacturing context, this means carefully examining the data collected throughout the entire process of order acquisition, production, distribution to identify patterns, inefficiencies, opportunities and threats.

The use of technologies such as artificial intelligence, predictive analytics and machine learning can help companies extract this data from the vast sets of information available, enabling them to make data-driven decisions to optimise logistics and improve overall operations.

The analysis and extraction of this data can be achieved through various tools and methodologies, such as machine learning, predictive analytics, data mining, and interactive dashboards. This operational data offers companies the ability to identify trends, predict future behaviour and make fact-based decisions, leading to tangible improvements in their operations.

The examples seen; Sales, Finance, Operations, Logistics and End-To-End, illustrate how data analysis can provide valuable insights for informed decision-making, risk management and identification of growth opportunities. The effective use of data analytics tools and an understanding of the financial environment are essential for translating data into strategic actions.

Every company and every type of business will have its own set of insights to contend with; success in extracting insights from data requires a combination of advanced analytical tools, data science skills and a deep understanding of the business context.

To 'crunch the data' and get relevant answers, in-depth analysis is important, which often includes collecting, organising and processing data to identify trends, relationships, anomalies and other significant patterns. This process may require the use of advanced statistical techniques, data mining algorithms, and analytical models that can provide useful insights for optimising production, supply chain, logistics and other business processes. Specific actions must be taken to 'crunch the data' so that insights, suggestions, for action are generated and the result is achieved.

Result to be measured appropriately through a set of suitable K.P.I.'s, process-by-process and end-to-end, that can provide adequate information for verification; ensuring that the data has not been 'crunched' for nothing.

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