{"id":22828,"date":"2023-12-21T11:30:40","date_gmt":"2023-12-21T10:30:40","guid":{"rendered":"https:\/\/www.regestaitalia.eu\/data-normalization-the-first-step-to-data-quality\/"},"modified":"2025-06-03T15:34:35","modified_gmt":"2025-06-03T13:34:35","slug":"data-normalization-the-first-step-to-data-quality","status":"publish","type":"post","link":"https:\/\/www.regestaitalia.eu\/en\/data-normalization-the-first-step-to-data-quality\/","title":{"rendered":"Data normalization: the first step to data quality"},"content":{"rendered":"

One of the key steps to successful digital transformation is to be able to perform analytics, choose strategies and make decisions based on data. This, along with the ability to interoperate both within the company and with external entities, forms the basis of Industry 4.0, but not only. Digital transformation today does not simply have to do with interoperability: data is critical at all levels, from maintenance to marketing, and is one of the most important assets for any company, especially in industries where production or processing of goods is the core business. That is why it is necessary for every company to improve its data culture. And if today, thanks to different monitoring systems, the  quantity of<\/strong> data is not a problem, data quality is often still a critical issue. And the first step in achieving quality uniformity is undoubtedly data normalization. <\/p>\n

Data quality: definition and usefulness<\/h2>\n

If we think that data quality is an overly theoretical or abstract topic for more production-related sectors such as manufacturing, it is worth rethinking the fallout that poor-quality data already has in industry today. The importance of data quality in manufacturing is considerable, to the point that it has even been discussed in the context of ISO standards for some time: For example, the  9000:2015<\/a> also defines data quality within it, but going back to the standards that preceded it we can find traces of it already in the 8402:1994<\/strong>. Even then, the definition read, \u201cThe set of characteristics of an entity that determine its ability to satisfy stated or implied needs<\/em>.\u201d Which also brings with it the concept of  context<\/strong>, which is increasingly fundamental in the modern approach to information management. In this scenario, the  normalization of data<\/strong> is a fundamental technical step, but also, and more importantly, the first piece of a paradigm shift in information management.<\/p>\n

Why is data normalization so important?<\/h2>\n

To understand the importance of working with normalized data we need to go back to the very basics of computer science for a moment, with a very simple example: imagine we have software that triggers an alert whenever the temperature of a piece of machinery exceeds a threshold of degrees Celsius for a certain period of time. The monitoring data provided by the machinery, however, is on a different scale, for example, Fahrenheit. In a primitive approach this means that each reading must first be  converted<\/strong> and then interpreted<\/strong>, which introduces two complexities: one related to the continuous use of machine cycles for conversions, the other related to the introduction of delay times in monitoring. Using the same type of scale throughout the supply chain makes operations faster, more efficient, and less wasteful in terms of required computing power and response time. <\/p>\n

Of course, this is an extremely simple example, but if we extend the same concept to include complex units of measurement, notations for representing decimal numbers, magnitudes, orders and rounding, just to stay in the field of measurements, we see how each piece of data that is not normalized can require dozens of steps to process, each time it is extracted. Data normalization has precisely the goal of  Provide uniform and consistent data upstream<\/strong>, that is, before they are archived.<\/p>\n<\/div>

To understand the importance even more, let us make a further effort of abstraction and imagine how important the amount of work needed can become if the same disjunction problem concerns not only the data understood as quantities, but also the ways in which they are stored.
That is why in recent years, the issue of data normalization has been joined by that of database normalization.<\/p>\n<\/div><\/div><\/div>

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Database normalization: benefits and challenges<\/h2>\n

Normalizing data, and in general the process of normalizing databases lead to an interesting set of benefits, not only in terms of simple analytical rigor. We recall here the main benefits that can be obtained: <\/p>\n