When varied sorts of data began to come in — in massive amounts, across multiple data channels, and in real time — the business of data management took on new complications. Analysis of extremely fast, large volumes, and diverse types of corporate data needed the creation and development of advanced data management systems and tools, and cloud computing technologies were born of that necessity. After a single public or private cloud network failed to produce the expected business outcomes, the era of multi-cloud and hybrid cloud environments began.
Typical data management duties include data storage, data integration, data quality management, data security, and database management. With the increasing velocity and volume of data, the diversity of data kinds, and the availability of an infinite number of data channels (sensor data), data management quickly became a nightmare asking for effective technology solutions. To add gasoline to the fire, the difficulties associated with data transmission from data stores to remote servers become unmanageable for the majority of enterprises.
The cloud-based services market is expected to serve “90 percent of companies by 2022.” Despite the immense promise of cloud platforms, cloud service providers confront a number of obstacles. The complicated issues of business analytics are as follows in the contemporary data-first and AI-first era, where real-time data analytics control the business landscape:
Data silos obstruct seamless data integration
Inadequate data quality as a result of the proliferation of data sources, data types, and data volumes
Inadequate data science personnel
There are no well-defined data governance (DG) policies in place.
As a result, businesses have been looking for technology solutions in the shape of Data Management platforms and tools capable of addressing all of the aforementioned difficulties. This also entails developing a comprehensive Data Management plan that considers Data Quality, Data Governance, and future complicated cloud infrastructures.
Multi-Cloud Data Governance Challenges
Consider a business situation in which the client is responsible for managing various business units, each of which is equipped with its own edge computing environment that is hosted and maintained by a separate cloud service provider. Managing data that is geographically and operationally distributed can result in a massive Data Management failure. Multi-primary cloud’s advantage is agility—the flexibility to supply solutions when and when enterprises require them.
Resources are shared across on-premises, private cloud, and public cloud environments in a hybrid cloud system. The primary impediment to the seamless operation of hybrid cloud operations is a lack of governance and regulatory compliance.
Mitigating the Data Management Challenges in Hybrid Cloud indicates that while hybrid clouds may offer future answers, data security and compliance challenges on hybrid networks are potential stumbling blocks that organisations must prepare to address.
And yet, in a multi-cloud or hybrid cloud environment, despite the fact that all computing resources are distributed across wide-area networks, resource management (servers) is relatively fragmented, impairing the flow of computing services. As a result, these cloud computing configurations provide resource management inconsistencies and errors, lowering the overall network performance quality. Additionally, these cloud networks also confront significant compliance and governance challenges in an increasingly regulated information technology world. The article Data Governance Challenges in a Multi-Cloud World discusses some of the finest data governance strategies that firms may implement in a multi-cloud environment.
The Cloud Warehouse’s Data Governance Challenges
The data warehouse’s expanding relevance is reflected in a Mordor Intelligence Report, which estimates that the data warehouse market is growing at an 11.17 percent compound annual growth rate, from USD 6.3 billion in 2019 to USD 11.95 billion in 2025. The cloud further simplifies and accelerates the building of data warehouses. Nonetheless, data governance and security remain two crucial areas that require attention.
Balaji Ganesan, CEO of Privacera, stated the following:
“In order for the data landscape to become as decentralised and diversified as it is today, data governance requires central administration but local enforcement. This effectively means that enforcement occurs at the database and application level, rather than adding another layer that becomes a single point of failure.