Three Main Influencers on Data Transition in Customer's Business
Question
During a pre-discovery, you, as a Solution Architect, learned about a customer and customer's business.
You evaluate the existent customer's data architecture. What are three main influencers on the data transition that you need to become aware of at this stage?
Answers
Explanations
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A. B. C. D. E.Correct Answers: B, C and E
As a Solution Architect, you always need to learn the company's current architectural landscape because your Power Platform solution should be an integrated part of this architecture.
A company can have a mixture of different IT platforms and systems requiring specific software, designs, or processes.
The data architecture and data influencers on a project, in particular, should be the Solution Architect's constant concern.
The influencers, like data location, data history, and data quality, must be considered at every project step.
During the discovery meetings, you need to ask questions about these influencers because they would affect the project's data architecture.
A Solution Architect based the solution design on where data resides today and where it will be tomorrow, how much historical data should be accessible by the system, or whether the data have duplicates, etc.
All other options are incorrect.
For more information about the data discovery process, please visit the below URL:
As a Solution Architect evaluating a customer's data architecture during a pre-discovery, there are several factors that may influence the data transition process. Three main influencers on the data transition that you need to become aware of at this stage are:
Data Location: Understanding where the customer's data resides is crucial for planning a data transition. This includes identifying the physical location of data (on-premises, cloud, or hybrid), as well as any regulatory or compliance requirements related to data location. It is important to evaluate whether the data can be accessed easily and securely during the transition process.
Data History: Data history refers to the records of changes that have been made to the customer's data over time. It is important to understand the completeness and accuracy of the data history as this can impact the success of the data transition. Historical data may need to be migrated, transformed or reconciled to ensure it is compatible with the new data architecture.
Data Quality: Data quality refers to the overall accuracy, completeness, and consistency of the data. Poor data quality can negatively impact the effectiveness of a data transition. As a Solution Architect, you need to evaluate the data quality of the customer's data to determine whether any data cleansing or transformation is required before migrating to a new data architecture.
While the other options (data code page, data logs) may also be relevant factors to consider, they are not as critical as the three main influencers listed above (data location, data history, and data quality) when evaluating a customer's data architecture during a pre-discovery.
