One question that keeps popping up in myc customer AzureML projects is ‘How do I conduct data preparation on my data?’ For example, how can we join the data, clean it, and shape it so that it is ready for analytics? Messy data is a problem for every organisation. If you don’t think it is an issue for your organisation, perhaps you haven’t looked hard enough.
To answer the question properly, we need to stand back a little, and see the problem as a part of a larger technology canvas. From the enterprise architecture perspective, that it is best to do data preparation as close to the source as possible. The reason for this is that the cleaned data would act as a good, consistent source for other systems, and you would only have to do it once. You have cleaned data that you can re-use, rather than re-do for every place where you need to use the data.
Let’s say you have a data source, and you want to expose the data in different technologies, such as Power BI, Excel and Tableau. Many organisations have a ‘cottage industry’ style of enterprise architecture, where they have different departments using different technologies. It is difficult to align data and analytics across the business, since the interpretation of the data may be implemented in a manner that is technology-specific rather than business-focused. If you take a ‘cottage industry’ approach, you would have to repeat your data preparation steps across different technologies.
When we come to AzureML, the data preparation perspective isn’t forgotten, but it isn’t a strong data preparation tool like Paxata or Datameer, for example. It’s the democratization of data for the masses, yes, and I see the value it brings to businesses. It’s meant for machine learning and data science, so you should expect to use it for those purposes. It’s not a standalone data preparation tool, although it does help you partway.
The data preparation facilities in AzureML can be found here. If you have to clean up the data in AzureML, my futurology ‘dream’ scenario for AzureML is that Microsoft have weighty data preparation as a task, like other tasks in AzureML. You could click on the task, and then have roll-your-own data preparation pop up in the browser (all browser based) provided by Microsoft or perhaps have Paxata or Datameer pop out as a service, hosted in Azure as part of your Azure portal services. Then, you would go back to AzureML, all in the browser. In the meantime, you would be better trying to follow the principles of cleaning it up close to the course.
Don’t be downhearted if AzureML isn’t giving you the data preparation that you need. Look back to the underlying data, and see what you can do. The answer might be as simple as writing a view in SQL Server. AzureML is for operations and machine learning further downstream. If you are having serious data preparation issues, then perhaps you are not ready for the modelling phase of CRISP-DM so you may want to take some time to think about those issues.
3 thoughts on “Data Preparation in AzureML – Where and how?”
We really appreciate the mention. We announced last month that we are partnering closely with Microsoft to provide Paxata’s Adaptive Information Platform to Azure customers for all of the relevant services including AzureML: https://www.paxata.com/press-release/paxata-advances-the-adaptive-information-platform-for-microsoft-azure-launches-hybrid-multi-cloud-support-with-intercloud-connect/. So AzureML customers can take advantage of the power of Microsoft’s ML capabilities powered by Paxata! Sometimes you can have your cake and eat it too. 🙂
That’s fabulous news and I will look forward to seeing that! Thanks Nenshad! I will look forward to playing with the Paxata Adaptive Information Platform in Azure. Great news!