Data is vital to the modern business ecosystem. Hence, data architecture has become a critical sector to maximize. No business should joke about having an optimized and effective data architecture. Well-managed data leads to proper decision-making across every aspect of a business.
Data architecture, as a discipline, involves the documentation of a company’s data infrastructure, standardizing the data collection, storage, transformation, distribution, and usage processes. Data is useless if it doesn’t meet needs. Hence, data architecture aims to bring timely data to those who need, it in an understandable format.
Before studying business data architecture optimization, let’s dig deeper into structured data infrastructures’ influence on your business.
Importance of Effective Data Architecture
Here are some reasons why your business should pay attention to its data architecture.
- Support for data integration and overall data quality improvement companywide.
- Ensures effective governance of data and the maintenance of an in-house standard for data.
- Fosters the development of data strategies geared towards business goals and aspirations.
These benefits are too vital to ignore. Hence, even if your business currently can’t afford an entire data team, take an alternative. Start by looking at how the data market benefits your business.
Components of Standard Data Architecture
The following are the major components of the current standard data architecture.
Data Pipelines
The data pipelines are tools and infrastructures executing data collection, refinement, storage, concrete analysis, and migration.
Cloud Storage
Cloud storage is the new standard for hosting and aggregating information. Cloud storages are off-site locations housing vast data sets, which are accessed only via the internet.
APIs
Application Programming Interfaces (APIs) foster and connects the requester to the host. Different information types can be transported via API.
AI and ML
These concepts automate data architecture. Through AI and ML, autonomous calculated decisions can be used in predicting. AI and ML can also influence data collection and labeling.
Data Streaming
Data streaming infers the movement of data from one point to the other. The moving data requires real-time processing for analysis.
Kubernetes
Kubernetes is the modern standard platform for computing, networking, and maintaining storage infrastructure workload.
Cloud Computing
Cloud computing involves analyzing, storing, and managing data via the cloud. For businesses, cloud computing leads to reduced costs and better data security.
Real-time Analytics
Analytics is vital to businesses. Hence, standard data architectures should facilitate real-time data analysis and insight generation.
How to Optimize Data Architecture for Business Innovation
Upon understanding the realities of innovative business data architecture, optimize your current system doing these.
Move to the Cloud
In the current data architecture ecosystem, any server short of the cloud is obsolete. You enjoy scaled AI tools, serverless data platforms, and Kubernetes containerized data solutions with cloud infrastructures. AI in business offers much competitive advantage. Enterprises are moving to cloud-based data platforms, do the same to stay industrially relevant.
Change to Real-time processing
Batch processing data is costly. Real-time data processing is cheaper and fosters the usage of new applications. For instance, with messaging platforms like Apache Kafka, you can use scalable, fault-tolerant, and durable publish services to manage millions of messages for future use.
Switch to Modular, best-of-breed platforms
It’s high time you ditched pre-integrated commercial solutions. Instead, it’s best to use modular data architecture with flexible and update-friendly open-source components. Hence, per time, you get updates that don’t disrupt your whole data architecture.
