Machine learning is an integral part of business intelligence. Not only does it help you improve your business efficiency by automating many of your processes, but it also takes advantage of the strengths of both humans and computers and uses this to help your business, making it one of the most important BI tools for data quality.
With the importance of machine learning in business intelligence, knowing how to implement the technology is important, and in this guide, we will show you how to successfully implement machine learning in your organisation, and how to use big data and actionable insights to help your business make data-driven decisions more effectively.
How is Machine Learning Used in Business Intelligence?
The main use of machine learning in business intelligence is automation. Machine learning algorithms can automate your data analysis process. This saves valuable time and effort for your data analysts. It also provides you with the most accurate insights and forecasting possible, as it is free from potential human error.
You can also use machine learning for real-time data, as well as using it to find hidden patterns or correlations in the data you store. This enables you to have the most up-to-date information at the right time for your data analytics, which can also improve your customer experiences.
The team at Catalyst BI have years of experience in business intelligence and machine learning, so we are well-versed in everything you may need to know. For a quick chat about our services, contact us today.
The 7 Major Steps to Implement Machine Learning
When it comes to implementing machine learning in your BI, there are 7 major steps to follow to help fuel your decision-making. These steps are collecting data, preparing the data, choosing the model, training the model, evaluating the model, parameter tuning, and making predictions.
Collecting Data
The basis of machine learning is data, so it’s important that you collect reliable data so that your machine learning model can find the correct patterns. The quality of the data that you feed to the machine will determine how accurate your model is. If you have incorrect or outdated data, you will have wrong outcomes or predictions which are not relevant.
Make sure you use data from a reliable source, as it will directly affect the outcome of your model. Good data is relevant, contains very few missing and repeated values, and has a good representation of the various subcategories/classes present.
Preparing the Data
After you have collected your data, the next step is to prepare the data for the machine learning process. You can do this by :
- Putting together all the data you have and randomising it. This helps make sure that data is evenly distributed, and the ordering does not affect the learning process.
- Cleaning the data to remove unwanted data, missing values, rows, and columns, and duplicate values.
- Visualise the data to understand how it is structured and understand the relationship between various variables and classes present.
- Splitting the cleaned data into two sets - a training set and a testing set. The training set is the set your model learns from. A testing set is used to check the accuracy of your model after training.
Choosing a Model
There are various machine learning models that you can use to analyse your data, so choosing the right one is important. You should choose a model that is appropriate for the task you are doing.
Our data scientists have access to various machine learning models and we would be happy to help you decide which machine learning model you require for your business.
Training the Model
Training is the most important step in machine learning. In training, you pass the prepared data to your machine learning model to find patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set. Over time, with training, the model gets better at predicting.
Evaluating the Model
After training your model, you have to check to see how it’s performing. This is done by testing the performance of the model on previously unseen data. The unseen data used is the testing set that you split our data into earlier.
If testing was done on the same data which is used for training, you will not get an accurate measure, as the model is already used to the data, and finds the same patterns in it, as it previously did. This will give you disproportionately high accuracy.
When used on testing data, you get an accurate measure of how your model will perform and its speed.
Parameter Tuning
Once you have created and evaluated your model, see if its accuracy can be improved in any way. This is done by tuning the parameters present in your model. Parameters are the variables in the model that the programmer generally decides. At a particular value of your parameter, the accuracy will be the maximum. Parameter tuning refers to finding these values.
The team at Catalyst BI are experts at parameter tuning, and we can help train your staff to use your machine learning models effectively.
Making Predictions
Once you have followed all these steps, you will be able to make accurate predictions on your data. The speed of machine learning means that you will be able to do this in a fraction of the time as sorting and analysing the data manually.
Your machine learning process will also get faster and more accurate and efficient the more you use it. This can help your business become more effective at what you do.
How Long Does Implementing Machine Learning Take?
As with any major project, there is no one specific answer to this question. The time it takes to implement your machine learning will largely depend on how big a project it is, and which software program you decide to choose.
However, on average, it can take between 3-12 months to get a machine learning program implemented and ready for use. This includes training staff on how to use the software.
DataRobot: Catalyst’s Game-Changing Cloud AI Platform
At Catalyst BI, we have our own machine learning and Artificial Intelligence tool to help your business, DataRobot. Using Tools like DataRobot, Catalyst BI can help your business solve some of its most pressing data science challenges and enable your organisation to make better and well-informed decisions.
With an ever-increasing need for value-driven data, DataRobot has been a crucial tool for many professionals across healthcare, manufacturing, retail, law, and financial sectors as well as drastically assisting data scientists, business analysts, and IT professionals achieve desired outcomes with their data.
Easily Implement Machine Learning Into Your BI With Catalyst
Catalyst BI can help your business achieve its full potential through machine learning with our DataRobot platform. By utilising innovative machine learning technology, DataRobot accelerates your AI success by giving your existing team the power to run it.
The valuable insights that DataRobot gives will empower your team to make informed decisions and take action on key insights, leading to better business outcomes and improved operational efficiency. With DataRobot, your business can become an AI-driven enterprise that leverages data to stay ahead of the competition.
Contact us today to find out more about how machine learning can help your business.
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