Your data architecture is only as good as its underlying principles.Without the right intent, standards, and universal language, it’s difficult to get your strategy off the ground.
So, before you use customer data to drive analytics operations, take a step back and consider whether you’ve laid the right foundations. Ultimately, following the right data architecture principles will help strengthen your data strategy and enable you to develop pipelines that accelerate time to value and improve data quality.
The right data architecture is central to the success of your data strategy. It’s made up of all the policies, rules, and standards that govern and define the type of data you’re collecting, including:
Perfecting this process is the key to any successful data strategy. As a result, if failure to implement data architecture best practices often leads to misalignment issues, such as a lack of cohesion between business and technical teams.
But how can your business make sure your data architecturestrategykeepsup with modern business demands?
To gain full control over your data, you need to structure your data architecture in a clear and accessible way.To do so, you'll need to follow the best data architecture principles.
By definition, data architecture principles pertain to the set of rules that surround your data collection, usage, management and integration. Ultimately, these principles keep your data architecture consistent, clean and accountable and help to better your organization’s overall data strategy.
Here are the four data architecture best practices for you to follow.
Did you know that bad data quality has a direct impact on the bottom line of 88 percent of companies? To avoid common data errors and improve overall health, you need to design your architecture to flag and correct issues as soon as possible.
However, it’s tricky to spot errors when you have large datasets, complex manual processes, and little support. Fortunately, investing in a data integration platform that validates your data automatically at the point of entry will prevent future damage and stop bad data proliferating and spreadingthroughout your system.
What’s more, filtering out anomalies with an automated tool will help minimise the time it takes to cleanse and prep. With so much data collected every day, it’s vital you only keep the information that provides value, creating a sustainable data validation and error correction loop.
Using a common vocabulary for your data architecture will help to reduce confusion and dataset divergence, making it easier for developers and non-developers to collaborate on the same projects.
