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 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.

Data quality: definition and usefulness

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 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. Even then, the definition read, “The set of characteristics of an entity that determine its ability to satisfy stated or implied needs.” Which also brings with it the concept of  context, which is increasingly fundamental in the modern approach to information management. In this scenario, the  normalization of data is a fundamental technical step, but also, and more importantly, the first piece of a paradigm shift in information management.

Why is data normalization so important?

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 and then interpreted, 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.

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, that is, before they are archived.

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.

Database normalization: benefits and challenges

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:

  • Redundancy reduction: normalizing data means minimizing duplication of information. This reduces the storage space needed and  Facilitates data maintenance.
  • Improved data integrity: through normalization, inconsistencies that may arise due to the aforementioned ungoverned redundancies are avoided. Each piece of data is represented only once, thus increasing its reliability.
  • Query efficiency: a normalized database enables more efficient queries. Since there is no duplication and redundancy, search, insert, update, and delete operations become faster and less complex, without the need for consistency checks.
  • Simplification of data relationships: normalization helps to clearly define the relationships among various data sets, facilitating the development and maintenance of applications and tools that rely on these data.

However, the normalization process also presents some challenges that must be grasped in the right way in order to put business data to value. Here are the main ones.

  • Complexity in design: the process of normalizing data can be complex and require careful analysis of relationships and dependencies among data-it is not a simple removal of duplicates, in short.
  • Performance impact: in some cases, an over-normalized database can lead to decreased performance, especially if it requires numerous interactions between tables to retrieve related data. A good normalization design should also take into account the dynamic behavior of the data.
  • Balancing normalization and performance: finding the right balance between comprehensive normalization and operational needs requires experience and a thorough understanding of both database theory and practical application.

One source, one approach

Undoubtedly in a technical topic like data normalization, the academic aspect plays a key role. However, the idea behind normalization also carries with it implications of a different nature, more related to the strategic aspects of digital transformation. We have previously addressed the issue of the  intelligent transformation. In this case, the normalization of data and the need to bring them into a single  source of trust for the entire enterprise can be, in addition to a compelling technological need, the driving event to achieve a paradigm shift in business thinking. In fact, by putting the different data sources in a position to communicate with each other through the common denominator of normalization, a first fundamental building block is laid to achieve the data permeability that is the first step in the transformation of the factory into  smart factory.

Regesta LAB supports you in the process of Smart Transformation of your company.

Book an hour with one of our consultants, based on your needs they will come back to you with an analysis of the main critical issues and a proposal for improvement.