As many organisations are making data management a priority in 2022, creating an effective data architecture framework is a major factor in both collecting and using data effectively. We know many businesses are currently ensuring their processes are in the best place to enable artificial intelligence (AI) and machine learning (ML). So understanding what sets passable architecture and great data architecture apart is the key to success – and what this blog seeks to explore.
But first, what is data architecture?
Data architecture is a framework for managing data, simply put. It’s your blueprint for collecting, storing, transforming, distributing and using data in a way that lets your organisation make insightful business decisions.
Our clients enjoy streamlined processes, greater efficiency and productivity, more confident decision-making and better financial performance once they have a strong data architecture in place.
Strong data architecture should ultimately streamline processes and experiences for employees and customers alike.
Good data architecture:
… bridges business and data strategies
Like all digital transformation projects, your data architecture framework should be rooted in business strategy. It should be a tool your employees can use to make more-informed decisions, in better time. And as with all digital projects, a common pitfall to avoid is not customising a framework to your organisation’s specific needs and pain points. Use technology to support your business, never the other way around.
Good data architecture helps to break down departmental silos, by giving the right access to your employees to do their jobs to their full ability. Collecting data that can’t be accessed or correctly interpreted and applied is a major business fail.
Think of your architecture framework as the bridge that integrates these strategies.
… prioritises security
When it comes to data, security is king. It is a great privilege to be able to collect data – especially user data – so it is imperative to treat it with the security and respect it deserves. Not to mention, the need to meet regulatory compliance to keep data private and secure from cyber threats.
Good data architecture prioritises security from the outset – it’s not a secondary option, or an afterthought clunkily built in. As with most things, building processes and systems right (in this case, securely) from the very first stages makes for a better outcome.
… has a practical focus
Data for data’s sake is largely useless. Without setting clear practical uses (tied to business objectives) for the datasets you’re collecting, it’s hard to know if you’re:
- collecting the right – complete – data
- using the data to its full potential, not missing opportunities
- comparing appropriate datasets in a useful way.
Building a data architecture framework that supports practical objectives is central to the success of your data use.
The data also needs to be formatted and presented in a way that is practical – providing an intuitive user experience for your employees. Where data is unnecessarily difficult to interpret, compare or apply, is where we begin to see opportunities missed.
… is scalable
As business needs change, grow, pivot – so too does the need for your data architecture to follow. Ensuring your architecture has the flexibility to move with your goals and priorities – and available data – avoids a redundant system that ultimately doesn’t serve you.
Building a cloud-native data architecture framework is one of the strategies we recommend for ensuring scalability and high availability.
… is an ongoing effort
Like most workplace projects, data architecture is not a “set and forget” tool. While you might not need to constantly update your data architecture framework, it is important to schedule regular status checks to ensure you’re maintaining peak efficiency and compliance, collecting full data sets, and making sure you’re making the most out of your available information.
Ongoing checks and maintenance help to avoid having to “catch up” with (otherwise unnecessary) major digital transformation projects every few years, and help you to avoid being “caught out” with accidental non-compliance.
Want to know how your organisation could improve its data architecture?
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