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.
Governance and compliance are not the same things, but there's a connection between the two.
Compliance deals with whether the use of data follows the relevant laws of the area it is located in, transferred, received, etc. There might be different regulations across different levels, and they might even change. Being compliant is adhering to all of them.
On the other hand, governance is concerned with making decisions the right way. Data governance needs policies and procedures defined in advance, followed, and later, capable of auditing.
Sometimes, enterprises can be compliant but not well-governed. Find sustainable practices to govern your data and stick with them. Data governance and compliance can go together. Governance should be its core, with compliance taking care of the practical, physical world aspect of the data.
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