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4 Steps to Big Data Transformation in the Cloud

Businesses must deliver a consistent high-value experience—or risk losing customers to someone who can. And they’re turning to big data technologies to help. With big data analytics, organizations can get to know their customers better, learn their habits and anticipate their needs to deliver a better customer experience.

But the path to big data transformation is not a simple one. Legacy database management and data warehouse appliances are becoming too expensive to maintain and scale. Plus, they can’t meet today’s challenges for accommodating unstructured data, the Internet of Things (IoT), streaming data and other technologies integral to digital transformation.

The answer to big data transformation is in the cloud. Sixty-four percent of IT professionals involved in big data decision-making have already shifted the technology stack into the cloud or are expanding their implementation. An additional 23% are planning to shift to the cloud in the next 12 months, according to research from Forrester.1

The benefits of leveraging the cloud are significant. Advantages most often cited by survey respondents were lower cost of IT; competitive advantage; ability to develop new insights; ability to build new customer applications; ease of integration; limited security risks; and reduced time to value.

Big Data Cloud Challenges
While the benefits of the cloud are substantial, shifting big data can introduce several challenges, specifically:

  • Integration: 66% of IT professionals say data integration becomes more complex in the public cloud
  • Security: 61% express concerns around data access and storage
  • Legacy: 64% say transitioning from legacy infrastructure/systems is too complex
  • Skills: 67% say they are concerned about the skills required for big data and building the required infrastructure

4 Steps to Overcoming Cloud Challenges
How do you overcome these challenges and turn them into opportunities? Here are four key steps to leveraging the cloud for big data transformation:

Data Integration
If your enterprise has a diverse and complex data ecosystem, not all cloud or big data technologies may be capable of seamless data integration. Choosing a target technology that would require complex data transformations may not be ideal. Complete a data pipeline analysis before selecting any technology. This will reduce your risk of creating disjointed data and incompatible systems.

Navigating the Complexity of Big Data Transformation

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If your data is confidential and proprietary, or you need to address strict security and compliance requirements, you may be concerned about putting your data in the cloud. In this case, a single-tenant, private cloud solution with a highly customized network and encryption can give you the big data capabilities you need, plus the security of a dedicated environment. Also, remember that public cloud doesn’t mean “no security.” Leading providers such as Amazon Web Services and Microsoft Azure provide cloud-native security authentication solutions and have options that include disk-level encryption and rigorous authorization and authentication technologies. Data security on the cloud is rapidly maturing. Many organizations with stringent security and compliance requirements have successfully leveraged big data technologies on the public cloud.

Legacy Systems
Transitioning from legacy infrastructure always involves data migration and usually involves one of three paths:

1. Lift and Shift:Moving existing workloads to cloud infrastructure as a service―leveraging only the compute, storage and network capabilities of the cloud―eliminates the need for complex application rewrites while offering the benefit of scalable infrastructure.

2. Decommissioning Legacy Data Over Time:You keep your existing data on your legacy systems and send new data directly to the new, cloud-based platform—with no data migration. New features and functionality are designed to be cloud-ready.

3. Complex Data Transformations:This involves acomplete modernization of data-driven applications and is most applicable when applications are nearing end of life. Examples include transitioning from mainframe, AS/400 and older relational database management systems to new databases such as Hive, Hadoop and HBase.

Big data implementations depend on diverse skills, including those of developers, administrators and cloud and big data architects. Demand for such experts exceeds supply, so companies often ask internal or contract personnel to work beyond their core competencies, which can slow implementations. It is much more economical to choose a vendor that provides these capabilities on a turnkey basis. Be sure it has managed multiple, complex big data environments at scale on dedicated environments and public clouds.

Big data is already a huge differentiator in many industries. Companies that harness it successfully are already disrupting industries; those that can’t face the risk of falling behind. The cloud offers the fastest, safest and most future-proof path to big data transformation. Don’t let concerns about data integration, security, legacy systems or skills prevent you from making the right move. These are all easier to deal with than you may realize.

Check out our infographic on Forrester’s big data findings, or download the entire report. When you’re ready to go deeper, check out our free how-to guide, “Moving Your Big Data Strategy to the Cloud,” which offers step-by-step solutions to the challenges of data integration, security, transitioning from legacy resources and acquiring the skills required to move to the cloud.

1Navigating the Complexity of Big Data Transformation,” Forrester Consulting, April 2017