De-fragmenting Data Silos- Cloud-based Enterprise Data Hub

However, the rapid advancement of cloud technologies and services have sprung machine learning to prominence.

Machine learning algorithms consume and process large volumes of complex data in order to develop enlightening patterns. A Cloud-based data hub streamlines data ingestions, processing and engineering as well as improves data quality, can help accelerateML initiatives and improve the quality of the output from these ML algorithms.

Increased business agility

It is not uncommon to hear that organizations could often spend 70 percent or more of their time processing data, and only 30 percent or less consuming and understanding the analytics. Data processing includes many steps, including data collection, data ingestion and cleansing, data integration, and data engineering. The situation is exacerbated by multiple lines of business within an organization duplicating data processing efforts.

A Cloud-based data hub is designed to centralize data from many sources across the organization and standardize data integration and engineering efforts for the entire organization to consume. It significantly synergizes data processing and improves data standardization and consistency. This greater efficiency allows lines of businesses across the organization to focus on driving analytics to develop solutions directly tied to business operation.

However, while the cloud-based data hub showcases many benefits for organizations, it also presents various challenges:

Integration & Migration

Most organizations have a mixed technology portfolio ranging from legacy on-premise tools and applications, to more recently cloud-based stacks. This mixed technology landscape poses a challenge in instituting a cloud-based data hub as some applications, tools, and processes may need to be integrated or migrated, to the data hub. These integrations and migrations require multiple system connectivity and potentially long periods to develop/test/deploy technology and process compatibility. All these tasks are not easy in an environment where business operational excellence and new capability development are pursued continuously and simultaneously.

Cloud Spending

While Security/Compliance/Risk is still a concern for many organizations, Cloud spending quickly becomes a huge challenge. There is no exception for a cloud-based data hub. The challenge is usually seen in the IT group and enduser consumption. Because there are many building blocks, functions, and services in a cloud-based data hub, it is common that IT teams are unable to rationalize its entire architecture and solutions. It contributes to larger-than-expected cloud run rates and supporting efforts. On the consumption side, as end-users are slow to realize the cost model shifting towards the cloud’s pay-as-you-go model, many organizations are struggling with higher-than-expected computing costs and unexpected egress costs are very common.

Cloud technology evolution

Many cloud technology solutions used in building data hubs are cutting edge like machine learning, big data analytics. These solutions certainly bring exciting new capabilities but may not always meet enterprise standards in terms of performance, reliability, and compatibility. They sometimes fall short integrating with various on-premise technologies, forming end-end business solutions, and expanding business execution capabilities due to immature or underperforming technologies.

Cloud-based enterprise data hub is a very forward-looking and exciting data solution with many promising capabilities and has a bright future despite many challenges.