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De-Fragmenting Data Silos - Cloud-Based Enterprise Data Hub
Sean Xu, Executive Director, MGM Resorts International
In an effort to improve customer experience, organizations are rapidly adopting cloud technologies and big data solutions to develop data-intensive applications such as personalization and target marketing. However, this effort has also easily spawn fragmented and incompatible data “silos”, which are stifling innovation, slowing down business agility, and impacting business productivity.
A potential solution to these data silo challenges is a cloud-based enterprise data hub (EDH), which a centralized data ecosystem which can cost-effectively store structured/unstructured data and integrate and process data in a variety of integration and engineering patterns. In addition, it can provide multiple types of data storage to accommodate various business needs as well as support data discovery, reporting, and analytics.
An enterprise data hub can ease the pain of integrating and merging data silos, but also provide a comprehensive and scalable enterprise data solution to benefit entire businesses and organizations. Let’s look at some key advantages in detail:
The full scale of enterprise-wide data & cost effectiveness
Unlike legacy traditional data systems that store and manage data with associated platforms or applications, the cloud data hub leverages multi-tier storages for structured/unstructured data in a cost-effective way. It can not only accommodate data from transactional business systems but also from other channels such as Internet Of Things, Social, etc. for expanded business insights. Its built-in redundancy and disaster recovery capabilities will keep organizations’ data assets safe and sound.
Reduced complexity & improved productivity
With many disparate and proprietary data stores across an enterprise, it becomes morechallenging when businessesare hungry for more data to accurately predict future rather than guessing at it. Business productivity growth is impacted by data issues and unnecessary, duplicate data processing efforts.
The cloud-based data hub provides options to simplify data integrations for more insightful wisdom while reducing the complexity of managing data volume and structure. It presents a comprehensive data foundation for lines of businesses to institute robust business analytics. This analytics-based solution enables companies and their workforce to become more productive and profitable.
A potential solution to these data silo challenges is a cloud-based enterprise data hub (EDH), which a centralized data ecosystem which can cost-effectively store structured/unstructured data and integrate and process data in a variety of integration and engineering patterns
Acceleration of Machine Learning
Dating back to as early as the 1950s, the old concept of machine learning, was out of touch for most enterprise budgets due to unique demands on data management. 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.
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 end-user 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 cutt ing 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.