Data architecture is challenging but can meet all enterprise needs from the beginning. Some best practices can help - here are five of them.
When data architecture is put together, they are based on the unique data management and analytics needs of the respective enterprises. But often, many generic components and integrations are inherited. Even customization of data architecture must have happened by default in the initial stages.
5 Data Architecture Principles to Help Business Needs
It's challenging to offer a comprehensive architecture to meet all enterprise needs from the beginning. But some best practices can help - here are five of them.
1. The Stroage Consideration
Data storage was an expensive affair some years ago, but things have now changed. Storage is now a commodity, regardless of whether it is hosted or on-premise.
For many, the positive effects of data storage primarily include cost-effectiveness. Even on-premises storage is considered practical and cheaper these days. Companies even keep large volumes of historical data continuously available. Reason? They can afford to. The only concern is restoring the data in the event of a disaster - not that it cannot be done, but just that it might take some time.
2. Enterprises and Multi-cloud Environments
Many enterprises use different cloud services at the same time - they follow a multi-cloud strategy. They often run multiple cloud platforms, with some connectivity and integration between the clouds. Compared to traditional applications, Cloud applications often offer much better APIs and metadata. However, enterprises still have to account for diverse data structures and differing system latency.
3. Analytics Must Follow Data.
Deploying an analytics tool close to the data source is considered more effective than moving it to an analytics environment. It's more efficient to do the source system's required data modeling, reduction, and shaping work. Furthermore, performance is considerably better with less data movement. With fewer environments to check on, the data governance is less complex, and data governance feels easier.
4. Unanalyzed Data is Wasted Data.
Data should always be treated as a business asset. Unanalyzed data is only an extra cost that requires regular maintenance while not providing any benefits. With data analytics, you unlock new business insights, find patterns, act on them, and bring value to the enterprise.
5. Data Governance Is Not Data Compliance.