Seeking consistent, reliable results in business? Discover how Data Modelling can produce high quality, structured data to enable just that.
In the world of Software Engineering, data modelling is a process which simplifies the data model of a software system. This data model is used as a basis, or blueprint, for creating a new and improved version of the system.
In data modelling, data is expressed as a series of symbols, diagrams and text to provide a visual representation of how the elements of data interrelate.
When a database is created it isn’t done so in a vacuum where it is never to be improved, upgraded or evaluated. To organise data and make it available as required, databases need to be evolving. Data modelling enables improvement and consistency with security, naming conventions and rules, and ultimately supports the improvement of data analytics.
Data modelling encourages better performing data with reduced errors, which means the overall quality of the data has improved.
Companies are also able to abide by national and global laws relevant to their industry regulations when data modelling is routinely applied.
If you want your teams to make data-driven decisions, then making data modelling a standard part of your organisation’s IT approach will ensure they are accessing the best quality data possible.
There are five different varieties of techniques that are implemented to organise data, as follows:
As the name would suggest, this technique has a hierarchy to it, with a tree-like structure. Data is gathered to one root, which branches off containing other connected data, extending the tree.
You may see this technique applied in HR terms for a company structure, if it has a hierarchical approach, with a number of employees reporting to one department.
A collection of objects or components is referred to as an object-oriented data model. There are three types of models in this type of data design: Class, state and interaction.
Data arranged into columns and rows within tables makes data easy to identify, as it is clearly ordered. This is one of the reasons it is a very popular model to implement. This technique is used to describe relationships between entities.
Sometimes referred to as an ER model, this approach is taking real-life elements and the connections between them. This model groups data into general attributes, entity sets, relationship sets and constraints.
To devise a technical map of rules and structures for data, which can then be applied to specific project needs, a logical data model would be the answer. It’s a more honed understanding of data entities and the interconnectedness between them.
Where the relational model focuses on information being arranged within a list format, this model spotlights the relationships occurring between information.
Simple and abstract, the conceptual data model is very popular as it can communicate ideas with ease, which is important when presenting to a range of people, particularly if you are seeking buy-in with an idea. This model provides a structured business view of the data needed to ensure the business processes are optimal.
Deemed to be one of the most universal of all data modelling tools, Excel offers its formulae to support your data gathering and equations. You can build a relational data source inside an Excel workbook, with tabular data used in PivotTables and PivotCharts.
Programming language, Python, can create and manage data structures rapidly and provides a range of tools for data analysis and manipulation. Data structures and datasets can quickly be represented.
Using Power BI will enable you to set the relationship between two objects. You can do this by dragging a line between the columns. Considered to be a better option for those newer to working with data, BI is simple to learn compared to other offerings and has an intuitive interface. However, it is slower when it comes to handling large quantities of data.
Known for handling large volumes of data at speed, Tableau offers a wide selection of features for visualising data without restrictions on row or size counts, or the total number of data points. Experienced data analysts are big users of Tableau because it is more complex and requires a depth of knowledge and experience to maximise its features.
Open-source software for data analytics, KNIME is free to use and offers users additional options which are competitively priced in comparison to others. With this software users can create visual data pipelines and carry out whichever analysis steps they want. Results can be viewed using widgets. Support is offered via an online community with KNIME proving to be a solid solution for any business who wants an affordable and reliable data solution.
A platform from Salesforce, Einstein Analytics offers a suite of data analytics applications that enable users to dive into predictive analytics and gain fast answers to key business queries and support the understanding of their customer base. Artificial Intelligence is used to offer insights and build AI data visualisations, which support companies in reaching their goals.
Being cloud based, Oracle Analytics gives access across an organisation to data via a single platform, using any device. Data analysis can occur in the cloud, with insights being pulled from ERP data. Oracle can help users discover what is driving business profitability, along with opportunities for growth.
At Academy Xi, we offer flexible study options in Data Analytics that will suit your lifestyle and training needs, giving you the perfect foundation for your future in data modelling.
Whether you’re looking to upskill or entirely transform your career path, we have industry designed training to provide you with the practical skills and experience needed.
If you have any questions, our experienced team is here to discuss your training options. Speak to a course advisor and take the first steps in your Data Analytics journey.