What is Automation?
Artificial intelligence (AI) and automation are transforming the way businesses operate, innovate, and compete. They offer many advantages, such as increasing productivity, efficiency, quality, and customer satisfaction. They also enable development of new products, services, and business models that create value and competitive edge.
One of the key benefits of AI and automation is process optimisation, which refers to the improvement of business processes to achieve optimal outcomes, reducing costs, and enhancing operational performance. Magellan collaborates with you to identify chances of implementing a strategic, responsible, and people-focused method for harnessing automation’s capabilities, reducing associated risks, protecting your data integrity, securing end-user data, and adhering to stringent governance and regulatory norms.
What is Machine Learning?
Machine Learning (ML) is a branch of artificial intelligence that focusses on using data and business rules to imitate the way humans undertake tasks. In its essence, ML is all about enabling computers to do the tasks that humans do in a controlled, consistent and highly repeatable manner. By defining the best way to carry out each procedure, ML ensures that the best procedure is used against each process, with no unexpected deviations. ML can imitate your best user, completing every process, right first time, every time.
Consider the prospect of hiring a new employee capable of handling multitasking effortlessly, following basic rules and resulting in little to no errors. Someone who thrives on the repetitive tasks that others may find monotonous, and can complete these tasks with ten times the efficiency. Our technology can do this, enabling your valuable staff resources to do the tasks which need the human touch. It’s a straightforward choice, isn’t it?
How does Machine Learning work?
ML uses a series of steps and decision-making processes to perform the tasks at hand. Here is a step-by-step process explaining how ML transforms raw data into valuable insights:
Step 1 – Data collection
The first and most important step in any machine learning process is to provide the data. The quality and quantity of the data can directly impact the model’s performance. This data could be in various forms and read from different internal systems.
Step 2 – Data processing
The data collected in the first step is likely to contain errors or duplicates. Therefore, step 2 entails cleansing the data by removing these inconsistencies. Data processing improves the quality of the data and ensures it can be interpreted correctly.
Step 3 – Choosing a model
Depending on the form of data and the task at hand, a model is chosen. Common examples of a model at this stage might be based on decision trees, or steps in moving or manipulating data in the same way as a human tasked with the same task. There is no manipulation of data at a database level.
Step 4 – Training the model
The most efficient paths and processes through line of business applications are defined, and a data dictionary is created to provide the necessary supplementary information. By recording the most streamlined route through a given process and the rules that drive both a happy and unhappy path, we create a repeatable and robust model to drive efficiency. Using a combination of these processes and the data dictionary ensures that data content, definition and quality are strictly maintained.
Step 5 – Evaluation
Once a particular process is mapped and base test data has been passed through, the model is ready to accept larger volumes of data. Larger volumes of data means we have more opportunity to test the rules and performance of the process. By defining expected run times, we can monitor performance every time it is invoked. Our technology will then ensure that error handling is sufficiently catered for and tracked. This can be by creating the ability for ML to manage errors within defined rules or simply to audit any ‘bad’ data by logging and continuing.
Step 6 – Tuning and optimisation
Following the evaluation, fine tuning is used to improve performance. This can be done by adjusting certain parameters and enhancing the value of using ML and providing you with a higher return on your investment.
Step 7 – Predictions and deployment
Once the model is defined and data content and activity are monitored automatically then any variance on that incoming data can be flagged for attention. Using our Trace file audit feature it is possible to flag or note any deviation and this can be reviewed to increase understanding of the model and aid improvements.
Who is using it?
Most industries are now recognising the benefits of integrating ML into business process optimisation. Organisations dealing with large levels of data who use ML are now able to work more efficiently and gain a competitive advantage over others in their field. Some of the industries adopting ML are:
- financial services
- government
- health care
- transportation
- utility companies
- 3rd sector
In highly regulated organisations, ML is viewed as the first and best step into the world of AI. Harnessing your data and systems with the capabilities of ML demonstrates it is a rapid and safe way to make a real difference to how data flows throughout your business.
From identifying red flags in your data, helping you be proactive in keeping your records up to date, to being able to detect and prevent potential fraud in financial services, ML can have a strong part to play in all organisations. If you have any further questions or are interested is taking that first step, contact us at we are here to help you on you unlock the potential of ML in your business.