Data have always been important, but now, especially with the advent of big data, they are gaining a great relevance. However, before they can be used, and therefore the data analyst has access to them, data must be “processed”, in order to be available for analysis. In other words, they must be stored in a database and, during this process, they have to be given a certain structure that makes them compatible with their intended purposes. But note: materially implementing the database and modeling the data are two distinct things: modeling is an abstract process and it is also independent of its realization within a DB. We can therefore define a data model as the abstract representation of data structures within a database. The act of creating a model is called “modeling”. Basically, when a data model is created, not only do we consider the data set, but also the connection between them, which allows, inter alia, to identify the necessary and possibly missing data at the same time. If properly made, a data model enables to display evidence-based and detailed information and to support strategic decisions based on scientific evidence and not on hypotheses that are not always well founded. Due to its relevance, data modeling is becoming more and more remarkable and it is rapidly evolving. Let’s see together what are the latest trends.
The growing spread of Data Modeling
As mentioned above, the amount of data, and the number of sources from which the data will come, is set to steadily increase. At the same time, thanks to the consequences of the Sars-Cov 2 epidemic, on the one hand digitization will become increasingly accelerated, and on the other, the spending capacity will be reduced. These two factors together will ensure that more and more companies will convert to data modeling, also thanks to the fact that currently there are plenty of sophisticated, but easy-to-use, tools on the market that allow you to carry out this complex activity in a much simpler way and that open it up to people who are not data scientists.
The advent of Machine Learning techniques for Data Modeling
In recent years, artificial intelligence, in particular machine learning, has become increasingly sophisticated, reaching levels which were unthinkable beforehand. Machine learning is applied in many areas of data intelligence, and data modeling is not an exception. Data automation and data modeling software already do most of the “hard work”, but human intervention is still necessary. By applying machine learning techniques to data modeling, on the other hand, human intervention manages to be increasingly limited (for instance, when selecting the most suitable model for the problem we face, amongst the different models proposed by the tool), generating a time and resources saving, obviously.
Agile Data Modeling
As in management, the “agile” approach has more and more supplanted the traditional way of managing projects, so in data modeling concepts have evolved and consequently also the requests coming from business intelligence and data analysis. The agile philosophy applied to data modeling is fully in compliance with the agile approach: avoid starting to build cumbersome, expensive and all-inclusive models. In agile data modeling, you create models only if when and to the extent necessary. In this way, the result obtained is more adaptable and better able to conform to situations and also to ever-changing types of data. This kind of approach finds perfect application in NoSQL databases, which are, inherently, non-relational and particularly suitable for managing unstructured and constantly evolving data.
Visual and codeless Data Modeling
Data modeling is a complex activity that requires specialized figures. As with many other fields of programming, also here a trend based on tools and software is developing. Those tools and software are able to provide a visual approach to data modeling which does not require “getting your hands dirty” with programming. Among the tools of this kind, we can mention Whatagraph, Xplenty and the very famous Erwin.
Graph databases use a topographic approach to record data: they connect specific data points (which can be, for example, a company’s customers, sales, or anything else you want to record) and automatically establish relationships among these in the form of a graph. These graphs, also called “knowledge graphs”, are particularly effective in the case of extremely complex and difficult to decipher relationships between data, or huge amounts of data, especially if used in combination with machine learning and artificial intelligence techniques. These are gaining ground, in fact they have been widely used by the AstraZeneca company for its modeling, for instance.