Data availability and accessibility has never been higher, and with Microsoft’s Power Platform and the Dataverse, it’s now all harnessed within one ecosystem, but what about the quality of this data?
At DQ Global, we have a range of data management products that seamlessly integrate into the Power Platform at each stage of the data journey to fix, enrich and master the data flowing through.
In this series of blogs, we’ll detail how our products work at each stage of the cycle, leaving you with the highest quality data possible to drive analysis and insights.
The Power Platform and the Data Journey
Microsoft’s Power Platform has done an excellent job of connecting Power BI, PowerApps, Power Automate and Power Virtual Agents together and integrating them into the Dataverse.
Spanning programs like Office 365, Azure, Dynamics CRM and many more standalone applications, the low-code no-code approach implemented by the Power Platform means it’s accessible to a wide variety of users, from huge international organisations with storied histories, to citizen developers who have just begun their developer journey.
Thanks to Andrew Welch and Lee Baker, the journey through the Power Platform into the Dataverse can be broken down into the following six stages:
- Data Collection
- Data Sources
- Ingestion
- Storage
- Analysis
- Visualisation
Let’s start with the Data Collection stage.
Data Collection
This is the most important stage of the journey. Data is collected in huge amounts, almost instantly through products like Power Automate, Power Virtual Agents and various Power Apps. This data also comes in a variety of forms such as structured, unstructured, and streaming.
With low-code and no-code collection prevalent, and the growth of citizen developers, the likelihood of the data collected being incomplete, incorrect or a duplicate is high, which is why it’s important that data is reviewed at this stage, to prevent bad data entering the Dataverse. Prevention is better than cure, as they say.
Our Solution
Here, is where DQ Global can help. Acting as a buffer between collection of the data and the data then entering the Dataverse, our solutions can deduplicate and cleanse, and apply enrichment, validation, and formatting to the data, leaving you with one mastered record and ensuring that when it does enter the Dataverse, it does so in its best possible form.
Implementing these actions at this stage is essential, as the further into the journey bad data gets, the more timely and costly it can be to fix, but doing so at the collection stage will minimise cost.
Although this is often the most important stage of data’s journey through the Power Platform and into the Dataverse, there are still more important stops along the way where the quality of the data can be improved.
In the next blog, we’ll discuss the Ingestion stage, detailing what happens, what can go wrong, and how we can help.
If you’d like to discuss your data quality needs and find out how we can help, you can contact us today by clicking here.
You can also find out more about our data management solutions, here.
