How many of us study the nutritional information and ingredients on a product label before we buy? As consumers, we perceive value based on how much we can decode an ingredient list. Then we compare it to the product's price. It's easy to relate an ingredient like refined sugar to unhealthiness. But it’s harder to estimate the hidden price that we pay for consuming that sugary food.
It’s not so different In the world of cloud data integration and enterprise data management. We often focus on the upfront price. But we lose sight of hidden costs and associated risks of, say, managing cloud data warehouses or data pipelines. It’s a challenge to make decisions about data integration ROI. The first step in the process is to take the time to “read the ingredients” and understand the broader data management landscape. Data enterprise components include:
- Data integration
- Data ingestion
- Data transformation
- Data governance
- Data quality
- Data preparation
- Data discovery
Only then can we begin to measure business value. A full business value assessment is recommended instead of a simple total cost of ownership (TCO) calculation. This lets you factor in the financial benefits for each application and data management component. It also provides a holistic picture of the gains and losses that you could incur. To learn more, this survey report from Bloor Research provides an in-depth look at the financial metrics and ROI of data integration tools.
Intelligent cloud-native data integration provides data lineage information and speeds data discovery. It uses artificial intelligence (AI) to analyze metadata. This enables true data transformation. Built for enterprise-grade deployments, cloud data integration is an integral part of a scalable, end-to-end data management solution. It has the flexibility and breadth to add new capabilities as the cloud modernization journey changes over time.
Here are three examples of how cloud-based data integration can deliver business value to your data estate:
1. Multiplying effect of advanced features
Adopting emerging technologies can improve a company’s market position in our competitive world. Often businesses adopt new capabilities that bring about incremental change. But they tend to avoid disruptive technologies. This is true even when technologies can deliver performance improvements that are orders of magnitude better.
For example, integrators are often under pressure to reduce data processing time. This is critical to speed up the organization’s decision-making process. Instead of opting for incremental improvements, the best choices are solutions that offer a combination of best-of-breed technologies. This could result in a multiplying effect when it comes to agility, productivity and performance. For example, a serverless Spark engine and NVIDIA GPU-enabled, cloud-native data integration platform lets you increase data processing speed by almost 5X and drive 72% lower TCO. A future-ready data platform allows you to explore and adapt multiple advanced technologies. No matter which solutions you’re considering, compare and measure their associated benefits, risks and costs.
2. Deployment flexibility
Cloud-based data integration gives you greater flexibility. You can decide how much you want to manage instances on your cloud data platform — whether multi-cloud or cloud-native. You can consider factors like:
- Available skill sets
- Growth patterns
- Compliance guidelines
- Cost governance structures
The long-term benefits of a cloud-agnostic cloud data management platform include the ability to:
- Switch deployment patterns with minimal disruption
- Switch between serverless data integration and cluster management, as needed
- Avoid compromising on features or changing out products
- Switch to serverless to easily see the cost difference, time needed and any missing features
- Drastically reduce the cost of switching or migrating workloads to a different cloud provider
3. Power of collaboration
A cloud-native data management platform lets you remove the friction of handoffs. Your team can share skill sets and reduce downtime — all reflected in your bottom line. Cloud-specific products, disparate tools and hand-coding don’t offer these important benefits. Collaboration means technical and non-technical users can discover, govern and process data. Teams can take a holistic view and resolve issues faster. And companies can take a more inclusive approach. As the number of new users goes up, and the number of data integration requests to IT goes down, it indicates the user ecosystem has become more self-reliant. Overall, the organization becomes more productive and data analytics become more actionable.
These were just a few examples of the business value of cloud data management. I hope it helps you make more informed decisions around hidden enterprise IT costs and associated risks.
For more information, check out this ROI for data integration and management webinar. It’s hosted by Philip Howard, Research Director at Bloor Research. Learn how to evaluate and measure costs and risks associated with enterprise cloud data integration and management.