In our previous blogs on DQ Global and the Power Platform, we discussed the Data Collection and Ingestion stages of data’s journey into the Dataverse, and how our solutions and products fit into the cycle.
In our third instalment of the series, we’ll be discussing the Data Sources stage of the cycle, what it is, and how we can help. We once again extend our thanks to Andrew Welch and Lee Baker for their work.
What are Data Sources?
Simply put, they are just that, sources of data. As well as the data streams we mentioned in our blog on Data Collection, there are an untold number of other avenues that data is collected from outside of Microsoft’s Power Apps.
The data that comes in from these other sources can largely be split into three separate categories:
- Structured – This is data in its best form. Data that arrives in to the Dataverse in a structured form is data that is ready to be processed. The information held within the data is easily identifiable and attributed to the correct field, meaning that we can then take the data and deduplicate, format, enrich and master it into one single record.
- Unstructured – As you may imagine, data that arrives in an unstructured form is the opposite of structured data. Much like you can’t control how a letter is posted to you, you can’t always control how data will be collected. When unstructured data arrives in your database, it won’t be in an immediately discernible format. This means that before you can put that data to use, you will need to analyse and classify it to figure out what fields are contained within.
- Streaming – As with most things in today’s world, data is also on demand. This source of data is largely removed from any human interaction, and is primarily used by products and machines that make up the Internet of Things (IoT). Items such as smart watches and smart home appliances that are always connected to the internet are always collecting data and communicating with each other. Beyond that, this source of data also involves collecting information on customer behaviours such as how much time someone spent on a particular web page and what they did while on that page. This information comes into the Dataverse via streaming and is then used in predictive analysis to derive insights on how a customer might act in the future, rather than reacting to a need or want after the fact.
Where does DQ Global fit in?
Whichever source your data comes to you from, they all end up at the same destination; the Dataverse. What’s important is what happens to the data on its way to the Dataverse, and it’s here that DQ Global’s award winning suite of products can help.
Data being so readily and easily accessible in today’s world is hugely beneficial, but in order to provide insights and not insults, we need to ensure they’re being based on reliable, high quality data.
Our solutions can sit in-between the data source and the Dataverse and act as a filter. The incoming data will be deduplicated, cleansed, formatted, enriched, and mastered into one single customer view and then pushed on into the Dataverse. This means that you’ll be pulling insights from trustworthy, high quality data that will drive your business in the right direction.
In our next instalment, we’ll be discussing the storage stage of data’s journey into the Dataverse.
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.
