One of the main problems with using data in any business is deriving actionable insights from it. This skill shouldn’t be limited to analysts and data scientists either. Anyone in your organisation should be able to read into the meaning behind a dataset and work out what next steps to take.
There are many ways of achieving this equal visibility and visualising different sources of information; one of the most common, across every industry touched by data, is data modelling.
In this article we are going to cover the different types of data modelling and the benefits of visualising information. We’ll also take you behind the scenes with a Cast Solutions case study, showing how data modelling, with the help of our analytics tools, helped a major Australian business succeed.
What is Data Modelling?
A data model organises individual elements within a set of information and standardises how these elements relate to one another. This set of defined rules can represent an entire system, such as the information stored within a data warehouse, or can represent the relationship between different datasets within a single source. What’s key is that data modelling helps users to interpret complex information and relate these datasets to real-life workplace objectives.
Data models are structured around individual businesses’ needs. Stakeholders and data analysts define rules upfront which organise data. This schema is then applied using business intelligence software when designing a new data model or updating an old one.
Rules should be standardised when creating a data model for more consistent data resource management. But, despite this, it’s crucial to remember that data models are living. As an enterprise’s requirements or data analytics tools change, so too should the data modelling process.
3 Types of Data Modelling
Data models can be divided by levels of abstraction – how applicable data is to real-world situations – or by the ways in which these models determine relationships between information.
1) Hierarchical Model
This model structures data by defining a hierarchy of relationships between different sources of data. This is useful for indicating how certain parts of a business work, but it can also be difficult to retrieve and access data stored in a hierarchical database.
2) Network Model
The network model is inspired by the hierarchical model. However, it aims to reduce the complexity of power dynamics between sets of data by instead showing how each piece of information can be linked with multiple ‘parent’ sets of information.
3) Object-oriented Model
Object-oriented models group collections of objects together by common features and methodologies. This is also called post-relational because it defines data by its relationships without re-organising it as in the hierarchical model.
What are the benefits of data modelling?
At its core data modelling is about making it easier for developers, data architects, analysts, and business stakeholders outside of any IT or data background to understand a set of data. Data models help users, no matter their function within a business, to visualise how a data pipeline works or how information derived from data analytics tools can be applied in daily operations.
There are numerous benefits to making data more accessible to everyone:
- Reduced errors in software and database development. Fewer errors when developing a database reduces the chances of standardising mistakes in day-to-day processes which can impact your organisation in numerous internal and external ways.
- Increased consistency in data system design. Uniform system design means data will be filtered, analysed and flow between different apps in the same way. This makes it simpler and more consistent to assess the performance of your proprietary systems.
- Improved application and database performance. Linked to the above, consistent data system design means that data suites, data warehouses and data pipelines will all work more effectively and enable businesses to more easily achieve their goals.
- Simpler communication between data and other teams. Data is fundamentally an abstract concept to communicate. Data models allow data analysts and scientists to visualise complex ideas in a way which all business stakeholders can understand.
More efficient projects. More effective data systems and streamlined communications between different teams translate to more efficient projects, and potentially on to higher productivity and lower costs.
How can data modelling help better business decisions?
Picking on the benefits mentioned above, we can see that these positives are even more acutely applicable to better business decision making. Most choices made in running an organisation are calculated risks. Data models take a degree of risk out of making decisions as they help key stakeholders to visualise and understand the effect of potential courses of action.
Data modelling with Cast Solutions’ team
What differentiates Cast Solutions from other Australian data services providers is that we factor in granular and diverse data sources when assessing a business’ data modelling needs. By aggregating a dataset for modelling you de-facto push the project in a preconceived direction. A more granular approach to data modelling enables users to discover more accurate insights.
One of our recent projects utilised this granular data modelling, reaping major end-user benefits. The client was an ASX-listed utilities service provider that provided maintenance and construction services in the transport sector.
The organisation gathered data for three different purposes: assessing weather conditions, tracking electricity supply outages and organising resource distribution. These datasets needed to be overlaid to maximise the enterprise’s operational effectiveness, but the team had no idea how to go about integrating these sources.
The Cast Solutions team helped to:
- First, integrate the three sources of information through data pipelines into a single database for analysis;
- Second, create a data model pinpointing correlations between different sets of information and assessing the best use of resources.
- Third, develop a blueprint of action points for the utility provider that would enable the team to test and validate hypotheses on improving operations.
Once the team had this blueprint they could also tweak their plan for streamlining day-to-day work based on real data tested within the integrated data model. This meant the business could be more flexible about operations and adapt to new situations while maintaining real-time transparency.
It also made collaboration between different teams within the organisation simpler as everyone had visibility on what others were facing in daily operations. The blueprint also operated as a single source of truth for clients and senior management, increasing trust between all parties (especially important given the importance of many utilities sector projects).
This project is just one example of how Cast Solutions can instill new life in business operations with actionable insights driven by your data. Chat to the team today about how we can help.
