Gone are the days when organizations only used to store data and query data. Now, we're adding intelligence to the data and letting it speak, rather than just staying in the warehouse or in the database. Data has become a strategic player in decision-making for organizations across the globe. It provides businesses with insights, predictions and forecasts, and helps organizations grow.
These days, everyone is talking about Machine Learning (ML) and Artificial Intelligence (AI) for good reason: these technologies can help organizations advance their growth and understand their customers better than ever before. An example of machine learning is a recommender system. A recommender system uses historical data to make predictions about what that customer is most likely to buy next. Think Netflix. “Netflix Thinks You Might Enjoy These Movies” is a machine learning algorithm based on the previous movies you have watched.
Below is the breakdown of the steps on how to model data to extract insights:
- 1. After we collect and store the data, it’s important to know what the columns or attributes are that interest you. For example, if you were trying to predict what a customer will buy next, it would be important to know what customers have bought previously, something such as historical data. You could use Azure Data Factory, which can store data and recall the data, based on predefined business rules.
- 2. The next step involves querying the data. I use Structured Query Language (SQL), so all our data is stored and regulated by Azure Data Factory. From there, we go to the data modeling phase, which will be performed in Azure Machine Learning Studio. In this phase, it is important to choose the right machine learning algorithms. Azure ML studio comes with a plethora of machine learning algorithms such as classification, regression, clustering and Principal Component Analysis (PCA). If you need help choosing an algorithm, you can refer to this blog. Apart from pre-defined algorithms, a user can build custom modules. Presence of modules like “Execute R Script” and “Execute Python Script” facilitate advanced data analysis operations.
- 3. Next, we work on data preparation for the model development. Any data redundancy, outliers or missing values will be taken care of in this phase. At the end of this phase, the data is split into two parts for training and testing purposes.
- 4. In the training phase, we use the training dataset in the modeling algorithm. Once we have trained the data, we use the testing data to score the model. Once we are happy with the model, we go ahead and deploy the model.
- 5. Azure ML Studio has a very easy-to-use platform, and all you need to do is click “deploy the model”. After the model is deployed, the results can be visualized in Power BI or Excel. In addition to visualization, notification by email can also be set if the organization is interested in seeing any abnormalities in data.
These steps have been designed as a high-level overview. If you are interested in learning more about Azure ML Studio, I invite you to attend our “Predict What Your Customers Will Buy Using Azure Machine Learning Studio” free workshop. Spend the morning with me and the DSI Data Science team at Microsoft, learning about this amazing tool. Feel free to reach out to me with any questions or comments you may have: email@example.com.