Data Architecture: Best Structural Techniques to Protect Your Data
Updated: May 30, 2019
Building a system that houses your organisation’s data can be daunting, especially now that data acquisition is growing rapidly. The many ways to transport, collect and dispose of data has only increased, more often than not sacrificing security to answer the need for accessibility and convenience.
Though each organisation’s data needs and systems vary, they all share a common denominator– data architecture. A data architecture defines the flow, storage and processing of data in the different aggregation levels. This covers acquisition, classification, storage, security, and access.
We break down the best practices in ensuring your organisation’s data architecture is solid and foolproof:
Move to the Cloud
More businesses are starting to use a cloud-based system. This growing trend addresses the massive expansion of data that is available today and provides an elastic and centralised storage. Cloud-based services offer a way for businesses and organisations to customize their data systems to work best for their needs and priorities. This can replace existing data warehouse solutions as cheaper and more accessible alternatives are available in the cloud.
ETL vs ELT
Traditionally in Data Warehouse environments Data was extracted, transformed and loaded. This principle created costly ETL processes that would load more data in the warehouse than was needed at the time.
In the modern Data Architecture only currently relevant data is transformed and moved to higher aggregation levels. The result is that data is loaded into a dedicated Raw Data storage also known as a Data Lake.
The lake allows to keep all data for a defined period of time and users have the choice of archiving or deleting older versions automatically using retention policies.
Create a Controlled Environment for Staging Raw Data
Securing the environment where your organisation’s data is stored is essential to meet privacy and other regulatory requirements. Typically, the Raw Data store is locked with no direct access of users. Data preparation tools and/or masking tools can be used to provide data scientists with analytics sandboxes for pattern analysis and more sophisticated evaluation models such as machine learning and AI.
Important is that data and its dependency is defined clearly – this allows for the architect to build a structure that works efficiently and flows to best meet the organisation’s requirements.
Set Up Data Access Auditing
One way to protect your data is by tracking who accessed it, what they did, and when they did it. Not only does this enforce transparency and integrity within the organisation but it also abides by most of the compliance requirements from regulating boards.
This minimizes threats to your data’s security. Make use of third-party tools to boost your database’s systematic evaluation and alter features if need be.
Data catalogues are enablers to manage the vast amount of entities in the raw data storage as well as in aggregated data structures. This function was embedded in most data warehouse technologies, but with the decentralisation of raw data this will be essential to manage the quality and value of your data assets across the organisation.
Fusion Professionals keep up with the trends in the ever changing online landscape of rapid data growth. Contact us and we will let you know how we can optimise your database management today.