When discussing business data, it is almost inevitable to quote the well-known adage that “data is the oil of the third millennium.” Wanting to make an exercise in style, it is actually possible to identify in this analogy a greater depth than just the catch phrase. Indeed, like oil, data must be refined and processed to extract its true value, but more than that, a suitable infrastructure must be designed and specific rationalization techniques identified. Database normalization is a good starting point, but there is much more to it today.
The culture of data quality
Looking at past practices, it is almost possible to see how the approach to data has changed and matured over the years.
At one time, it was sufficient to collect them. Then companies evolved to more refined models, and the issue of normalization of databases, in many cases still relevant today.
More recently, the concept of data quality, or the degree to which data adhere to business expectations in terms of accuracy, validity, completeness and consistency, has emerged.
The evolution of data management is such that it generates extremely specific techniques that are useful in the case of very particular needs. In the context of this article, it is particularly interesting to mention the case of data denormalization, a management strategy that involves inserting redundant and precomputed data within a normalized database. This is a seemingly counterintuitive operation, but one that, in some contexts, can considerably improve read performance.
We mention this strategy to introduce a fundamental theme: rationalization of business data must also take into account specificities and needs. In short, there is no universally valid rationalization criterion.
Data rationalization strategies
In short, data management has become a complex discipline: there are now different strategies for each point in the life cycle of a database, starting with the foundation, that is, the model chosen. By way of example, let us recall the five main approaches to modeling:
- E-R (Entity-Relation) Model. – It uses entities and relationships to represent data and their interactions. It is useful for conceptual design.
- Relational Model – Organizes data into tables (relationships) with rows and columns. It is the most common model in SQL databases.
- Object Model – Combines object-oriented programming concepts with data modeling. Uses classes and objects to represent data.
- Template to Document – Used in NoSQL databases, it stores data as JSON or XML documents. It is flexible and suitable for semi-structured data.
- Graph Model – Used to represent data with complex relationships, such as in social networks. Uses nodes and arcs to represent entities and relationships.
Databases, in short, can come in forms and with functionalities that are also very different from those commonly known and widely used.
A support for data rationalization
As complexity grows, so, conversely, does the need to make use of enterprise resources in an increasingly agile, autonomous and effective way. So how can enterprise data be rationalized but retain a high level of control and agile access?
SAP Datasphere is the end-to-end data management cloud platform designed to integrate, manage and distribute enterprise data in a more streamlined way. This tool allows companies to access and work across a wide range of data from different sources, both structured and unstructured, eliminating classic information silos and moving closer to the single source of truth (SSOT) paradigm that is the ultimate goal of any data rationalization strategy.
Designed to help companies overcome data fragmentation through integration, cataloging and orchestration, SAP Datasphere ensures that every piece of data is available in the right format at the right time. Thanks to its capabilities to data virtualization, makes it possible to work on large volumes of data without the need to duplicate them, thus maintaining an efficient rationalization of database resources by borrowing, in principle of operation, the concept of the in-memor databasey that enabled SAP S/4HANA to achieve success at the time of its launch.
More effective databases for more opportunities
Managing data and the databases in which they are organized has become one of the most critical strategic skills. Today, being able to effectively rationalize business information means having a significant competitive advantage. With advanced technologies such as SAP Datasphere, companies can optimize their database infrastructure, gain immediate access to higher quality data, and ensure more streamlined and dynamic information management.
Implementing advanced data management solutions, integrated with customized streamlining strategies, enables more informed decision-making with greater speed and agility, opening doors to new growth opportunities
Want to learn more about the benefits of SAP Datasphere and how to integrate it into your systems? Tell us about your needs, and our experts are ready to provide the answers you seek.
