{"id":11139,"date":"2025-04-01T13:32:00","date_gmt":"2025-04-01T11:32:00","guid":{"rendered":"https:\/\/optano.com\/?p=11139"},"modified":"2026-01-20T11:37:10","modified_gmt":"2026-01-20T10:37:10","slug":"added-value-data-why-its-worth-conquering-the-data-mountain","status":"publish","type":"post","link":"https:\/\/optano.com\/en\/media\/insight\/1\/mehrwert-daten-warum-es-sich-lohnt-den-datenberg-zu-bezwingen\/","title":{"rendered":"The added value of data: Why it's worth conquering the data mountain"},"content":{"rendered":"
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The added value of data: Why it's worth conquering the data mountain<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data collection and processing in the company<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Many companies face the challenge of preparing their data in such a way that it can be used for Artificial intelligence<\/a>, Machine Learning<\/a> or mathematical methods such as Operations Research<\/a> are usable. However, before these technologies can play to their strengths, the required data must be available in high quality - a complex issue that often represents a major hurdle in practice.<\/p>

An illustrative image that we like to use for this is that of a mountain of data: a company's data is like a huge mountain that needs to be climbed. Depending on the initial situation, this sometimes means climbing only part of the mountain in order to quickly achieve added value. In other cases, an \u201einterim storage\u201c is required - a step-by-step process to prepare the data and make it usable.<\/p>

However, one thing is clear: only with well-prepared and validated data can optimal analysis results be achieved when using AI, ML or - as in our case - mathematical optimization methods. In this article, we explain how we can support companies on this path and facilitate their ascent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Data is the new gold - recognizing the potential of data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data is often referred to as the new gold or oil. As an ever-present topic in the media, its potential is well known. It is often referred to as the strategic capital of companies. The way companies handle their own data therefore has a major influence on their economic activity and also on their competitiveness.
But why do so many companies shy away from the task of collecting and processing data if the added value is known?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Inefficiencies and problems force us to act<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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As long as business is running smoothly, i.e. there are no inefficiencies or production and sales problems, many companies do not see the need to deal with the topic of \u201edata and data preparation\u201c. After all, well-structured data collection and preparation naturally involves investment - both in terms of time and money.<\/p>

But at the latest when decisions have to be made that have a major impact on a company's profitability, data and its preparation become an important issue for every company.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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No valid results and decisions without data preparation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data processing and preparation is about being able to use the data for analysis purposes. This includes predictive and prescriptive analysis as a form of advanced analytics, but also the use of machine learning (ML), artificial intelligence (AI)<\/a> and Operations Research (OR)<\/a>. What these methods have in common is that they generally require the preparation of large volumes of data.<\/p>

The purpose of data preparation is therefore to ensure that the data used is accurate and consistent. This is the only way to ensure that the analysis delivers valid results and that usable findings and information can be obtained for decision-making. Excellent data quality is therefore highly relevant for operationalized decisions - i.e. the form of decision support using analysis methods.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Data quality as a decisive factor<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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There are many reasons for poor data quality. Starting with the lack of data. Sometimes \u201eonly\u201c individual data records are missing, but often the data was simply not collected because it did not appear to be relevant. Data can also be incorrect, incomplete or defective. Or they are available in different formats - often in simple tables in Excel files, sometimes only as an image format. In view of the many adversities, collecting and preparing data seems to be a task for Sisyphus. In order not to be discouraged and to develop an appropriate mindset, a goal is needed. And it shouldn't be the mountain top.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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