Welcome to the Nightingale HQ overview of data platform auditing services. Here we aim to introduce people to what they need to know.
Data platform auditing reviews your data platform against fitness for purpose measures such as security, compliance and structure.
Data platform auditing helps teams reduce instances of data loss, data corruption, data inaccuracies and data security on their data platform. Improvements can be made to the design of the data platform to ensure that data storage and structure is consistent and that relationships are clearly defined, which reduces instances of duplication and decreases storage costs. The data platform performance can also be improved to improve the speed of data access. Improving the security of the data platform will ensure compliance and reduce the risk of data loss and corruption.You may need this service if:
your team experiences problems of data duplication, corruption or loss.
you have a large volume of data on your data platform and do not have security measures in place.
your team is using a data platform to gain insights that drive decision-making.
KPIs you should consider measuring for this are:
increased speed of data availability
costs saved by reducing unnecessary load
reduced costs associated with data loss and corruption
When implementing data science and AI solutions in your products, the availability, security and accuracy of your data are essential. Reviewing and improving your data platform will improve the performance of your projects and make development smoother and more efficient.Data platform auditing helps deliver:
improved data security and compliance
faster page loads
faster interactive applications
reduced bounced rate
faster access to data for analysis
Get this service if you encounter:
slow performance of web pages and applications.
increasing server resource charges.
high bounce rates.
inadequate data security measures or noncompliance.
Key criteria to consider are:
The cost of improvements compared to benefits from those improvements.
Any potentially unneeded data.