Why is data modelling important for project management?
The day-to-day operations for modern-day businesses are complex and cannot be accomplished by only human labour. Automated operations generate huge amounts of raw data that is of no use unless it is processed into understandable business insights.
This is where data modelling comes into play. It converts a series of meaningless data into an informative format that can be utilised to gain meaningful information. For instance, data modelling can extract useful customer contact numbers and bunch them into usable categories from a general set of telephone numbers. The modelling process makes it easier for businesses to efficiently process huge volumes of information and gain insights for making profitable decisions.
Read on to find out more about data modelling techniques, steps involved in the process and the importance of data modelling in managing business projects.
What is Data modelling?
Data modelling refers to the process of using artificial intelligence (AI) to create different models to select and showcase data from a larger set stored in a database. The models can represent data points, correlations between them or their patterns. If you compare data items to bricks, a data model is an architect’s plan that reorganises data points into an understandable format.
These models emphasise the visual representation of the data that helps managers understand it to develop business regulations, compliance rules and organisational policies.
Types of data modelling
There are three different types of data models.
- Conceptual: These kind of models are created to define what kind of data is required for a business purpose. They define ‘what’ the end result should contain. They are used to define or develop business concepts.
- Logical: These models organise the available information to show correlations between different data sets. They are utilised for developing organisational structures and different implementation processes for company policies. They define ‘how’ the data inferences can be implemented.
- Physical: These models are used for the physical implementation of the programme. These models also calculate the impact of these policies on the organisation. They also specify the pre-requisites for the physical implementation of these policies.
Requirements for data modelling
There are several requirements for creating data modelling.
- Data accuracy: For proper data modelling, it must be ensured that all the data items should be factually correct. Omission or incorrect data can lead to the creation of faulty models that can produce incorrect insights.
- Up-to-date IT infrastructure: Investing in higher quality processors and IT infrastructure can help in faster and efficient data modelling that can yield better results.
- Qualified professionals: The data collection and modelling techniques must be handled by people who are trained in data management and data processing to yield better results.
Where are the benefits of data modelling?
Though data modelling has a huge scope in most domains, it is mostly used in the software industry for developing complex software products. Here are the benefits of data modelling.
- Higher quality software products: A data model helps solve the problem of premature coding which is the major source of failures in any software development approach. Data models hep in defining the problem that the software aims to solve. They also help in eliminating unsuitable developmental approaches at the beginning itself.
- Reduced expenditure: Data modelling helps in catching errors and bugs in product designs in early stages, when they are easier and cheaper to fix. It reduces the production cost and saves the organisation millions in failed attempts.
- Faster production: The process helps in automating many components of the production process which leads to efficient and faster production. This can yield a longer timeline for marketing the product that can drive sales better.
- Clearer scope: A data model provides insights to help businesses figure out what to include and what to omit in their marketing strategies. It also breaks down the jargon to help the leaders understand the pros and cons of the company product. It improves the focus on building a scope for the product.
- Faster processing: A good data model helps in turning databases into faster and more efficient structures. This in turn, reduces the time required for screening relevant information from them.
- Fewer errors: Data models helps in defining relevant information and resolving confusion faster. Hence the product is devoid of major errors. With a well-structured model, developers are less likely to make errors that are difficult to fix.
- Better documentation: Models document important concepts and jargon, proving a basis for long-term maintenance. This can help during staff changes and in the longer run.
- Efficient risk management: You can use data models to estimate the complexity of software, and gain an insight into the development and project risks. This can help in managing or mitigating the associated risks.
Importance of data modelling in project management
Data modelling has a huge potential in the field of project management. Project managers can utilise these models to improve the project plan and increase the efficiency of the production process. It also helps them in risk mitigation and quality assurance.
The process plays a major role in deciding the budget, policies and design in any project. Investing in data modelling can supplement project management to increase the revenue for an organisation.
Today, data modelling is considered an integral component of managing any project. If you want to learn about other aspects of project management in addition to modelling data, you should consider pursuing a course in the subject.
If you want to pursue a degree in project management, the diploma in business management co-op from the Toronto School of Management can be a great choice. The course covers all aspects of managing a project for a business including the technological aspects such as AI and data modelling. The course also gives the opportunity to gain a hands-on experience. To know more about the diploma, click here.