Cloud Transformation: The Next Data Frontier for Business Intelligence, Machine Learning and AI

Intelligent data virtualization will unlock your enterprise shared data intellect.

Cloud transformation is happening. The shift to the cloud will drive $1.3T in IT spending by 2022 (Gartner) and spending on public cloud services will total $277B by 2021, a compound annual growth rate (CAGR) of 21.9% (IDC). Enterprises are migrating to the cloud with the hopes that they will realize cost savings, increased agility and enhanced revenues. The last mile of cloud transformation is the process of making data useful and accessible through a process called cloud data transformation.

What is Cloud Transformation

Put simply, cloud transformation is about making data easily visible, usable and accessible to data workers for data analysis and data science, creating a shared data intellect that will drive an increase profitability and revenue.

The size and complexity of enterprise data ecosystems means that cloud transformation presents many challenges.

Cloud transformation involves:

1. Migration of data from legacy systems to reduce costs or leverage the benefits of a cloud or hybrid cloud environment, while minimizing the disruption to business users that migration can cause.
2. Making data from all sources accessible, even in circumstances where data platforms are disparate, with some less performant and not scalable.
3. Preserving the security and governance rules enterprises have set for unique individuals, data platforms, databases, etc.
4. Allowing users to keep using their preferred business intelligence (BI) tools with uniform performance and equivalent results.

Hidden costs and insufficient interoperability may render hopes of gaining these benefits into mirages for many enterprises. Seven hurdles stand in the way of organizations realizing concrete value for their cloud data transformation efforts:

1. Cloud platforms may require restructuring of the enterprise’s data, forcing huge ETL and data translation projects to prepare data for ingestion.
2. Many vendors store data in proprietary formats, effectively chaining customers to one solution.
3. Some siloed or on-premises data is attached to legacy systems that cannot be moved without re-engineering those systems.
4. Interoperability with outside systems and BI tools is limited.
5. Security and entitlements are difficult to maintain when merging data from many silos that may have different users and configurations.
6. Limited strategic plans to realize business benefits beyond moving to the cloud, such as broadening user access or leveraging integrated data.
7. Hidden cost implications of cloud transformation often exceed initially anticipated or budgeted numbers.

These challenges stem from most cloud solutions being technical solutions to technical problems. Enterprises require a solution that serves not just technical needs, but the larger business objective, which is to accelerate increases in revenue with greater efficiency. The solution must provide access to clean, comprehensive data that enables users to act in support of common goals and derive better insights that improve company profitability.

Such a solution is intelligent data virtualization.

Why Intelligent Data Virtualization Is The Missing Bridge in Cloud Transformation

If a cloud migration results in vendor lock-in and limited interoperability with a small number of BI and AI/ML tools, enterprises will find themselves left with the same challenges they had before, and a renewed need to migrate between systems again in the future. To future-proof data access and realize data value more effectively, enterprises need intelligent data virtualization.

Intelligent data virtualization is a solution that facilitates analytical interactions between data and the tools that consume data, allowing one or more data sources to be accessed securely and consistently by one or more software applications or BI tools. The ultimate goal is uniform and shared access to all data that is cost-effective, highly performant and secure—irrespective of where it is physically stored.

The ultimate benefits are dramatically reduced costs for running data infrastructures, improved query performance and significantly deeper insights across the organization. To achieve this, the data virtualization platform must be agnostic to the analytical tools used and location of data sources, providing users with uniform and transparent access to data with any tool they choose.

Data virtualization unifies corporate data and simplifies access to users, allowing them to model and analyze the data and share their data intellect across the organization. They obfuscate the systems involved in the back end, freeing users to concentrate on working with data and generating valuable insights rather than learning complex data engineering methods, hunting for accurate data in forgotten silos or creating extracts of data that increase risk to security, integrity and reliability.

Download our white paper Cloud Transformation: The Next Virtualized Data Frontier for Business Intelligence, Machine Learning, and AI for more on this topic.