We are each the sum of our parts, and, in our modern technological age, that includes data. Our search queries, clicking compulsions, subscription patterns and online shopping habits – even the evidence collected from wearable fitness tech – feeds into our digital footprint. And, wherever we choose to venture on our online quests, we are constantly being tracked.
Experts claim that we create 2.5 quintillion bytes of data per day with our shared use of digital devices. With the big data analytics market slated to reach a value of $103 billion by 2027, there are no signs of data storage slowing down.
But it’s less about acquisition than application and integration, with poor data quality accounting for a cost of $3.1 trillion per year against the US economy according to market research firm IDC. While device-driven data may be fairly easy to organise and catalogue, human-driven data is more complex, existing in various formats and reliant on much more developed tools for adequate processing. Around 95% of companies can attest that their inability to understand and manage unstructured data is holding them back.
Effective data collection should be conceptual, logical, intentional and secure, and with numerous facets of business intelligence relying on consumer marketplace information, the data processed needs to be refined, relative, meaningful, easily accessible and up-to-date. Evidently, an airtight infrastructure of many moving parts is needed.
That’s where data architecture comes into the equation.
What is data architecture?
As the term would imply, data architecture is a framework or model of rules, policies and standards that dictate how data is collected, processed, arranged, secured and stored within a database or data system.
It’s an important data management tool that lays an essential foundation for an organisation’s data strategy, acting as a blueprint of how data assets are acquired, the systems this data flows through and how this data is being used.
Companies employ data architecture to dictate and facilitate the mining of key data sets that can help inform business needs, decisions and direction. Essentially, when collected, cleaned and analysed, the data catalogues acquired through the data architecture framework allow key stakeholders to better understand their users, clients or consumers and make data-driven decisions to capitalise on business.
For example, e-commerce companies such as Amazon might specifically monitor online marketing analytics (such as buyer personas and product purchases) to personalise customer journeys and boost sales. On the other hand, finance companies collect big data (such as voice recognition and facial detection) to enhance online security measures.
When data becomes the lifeblood of a company’s potential reach, engagement and impact, having functional and adaptable data architecture can mean the difference between an agile, informed and future-proofed organisation and one that is constantly playing catch-up.
Building blocks: key components of data architecture
We can better visualise data architecture by addressing some of the key components, which act like the building blocks of this infrastructure.
Artificial intelligence (AI) and machine learning models (ML)
Data architecture relies on strong IT solutions. AI and machine learning models are innovative technologies designed to make calculated decisions, including data collection and labeling.
Data architecture is built upon data pipelines, which encompass the entire data moving process, from collection through to data storage, analysis and delivery. This component is essential to the smooth-running of any business. Data pipelines also establish how the data is processed (that is, through a data stream or batch-processing) and the end-point of where the data is moved to (such as a data lake or application).
In addition to data pipelines, the architecture may also employ data streaming. These are data flows that feed from a consistent source to a designated destination, to be processed and analysed in near real-time (such as media/video streaming and real-time analytics).
APIs (or Application Programming Interface)
A method of communication between a requester and a host (usually accessible through an IP address), which can increase the usability and exposure of a service.
A networked computing model, which allows either public or private access to programs, apps and data via the internet.
A container or microservice platform that orchestrates computing, networking, and storage infrastructure workloads.
Setting the standard: Key principles of effective data architecture
As we’ve learned, data architecture is a model that sets the standards and rules that pertain to data collection. According to simplilearn, effective data architecture, then, consists of the following core principles.
- Validate all data at point of entry: data architecture should be designed to flag and correct errors as soon as possible.
- Strive for consistency: shared data assets should use common vocabulary to help users collaborate and maintain control of data governance.
- Everything should be documented: all parts of the data process should be documented, to keep data visible and standardised across an organisation.
- Avoid data duplication and movement: this reduces cost, improves data freshness and optimises data agility.
- Users need adequate access to data.
- Security and access controls are essential.
The implementation and upkeep of data architecture is facilitated by the data architect, a data management professional who provides the critical link between business needs and wider technological requirements.
How is data architecture used?
Data architecture facilitates complex data collection that enables organisations to deepen their understanding of their sector marketplace and their own end-user experience. Companies also use these frameworks to translate their business needs into data and system requirements, which helps them prepare strategically for growth and transformation.
The more any business understands their audience’s behaviours, the more nimble they can become in adapting to ever-evolving client needs. Big data can be used to improve upon customer service, cultivate brand loyalty, and ensure companies are marketing to the right people.
And, it’s not all about pushing products. In terms of real-world impact, a shifting relationship to quality data could improve upon patient-centric healthcare, for example.
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