Technology advances have made business systems more autonomous and interconnected, causing data volumes to rise at an unprecedented rate. Unlocking value (and insights) from structured and unstructured data assets and capitalizing on business opportunities requires a clear and dedicated focus on data analytics. But as the complexity and scale of data increase, on-premises systems are unable to process growing volumes of data quickly and efficiently. Read on as we examine the best practices for moving data analytics to the cloud. 

The Drive to Move Data Analytics to the Cloud

In today’s era, data drives every business decision. But with millions of data points now existing across organizations, managing and monitoring the massive volumes of data sets has become extremely difficult. On-premises systems are no longer capable of handling this growing volume and often cause organizations to make incorrect decisions. To overcome these limitations, many organizations have now begun to move analytics to the cloud to analyze and extract insights from large data sets. 

Cloud data analytics increases the ease and efficiency of processing complex and large data sets. It helps organizations deal with the staggering rate of data generation while allowing them to: 

• Deploy analytics projects at speed and scale – without any physical hardware deployment and maintenance. 

• Turn services on and off as needed and only pay for the services they use – thus optimizing their resources.  

• Access top-notch analytics capabilities in the cloud and resolve the problem at hand with accuracy. 

• Save humongous costs by eliminating investment in on-premises systems as well as daily management and maintenance. 

• Enable global access to data, paving the way for improved enterprise-wide collaboration. 

• Leverage powerful analytics tools to make informed decisions that drive their business forward.

The Best Practices to Follow

As you decide to move analytics to the cloud, you must tread the path very carefully. Here are some best practices to keep in mind: 

1. Craft a Robust Cloud Analytics Strategy

Data analytics migration can take an unexpected turn if not planned properly. Before you move analytics to the cloud, you must craft a robust cloud analytics strategy. This strategy should list every critical aspect of the migration process – right from what data sets you want to move to the cloud to what your use cases will be, the support requirements you need, how you will train your users, and more. Your strategy should also list the tools, resources, and training requirements for successful and sustained migration. 

2. Make the Right Cloud Choice

With several cloud options available today, making the right choice can be difficult. As you move your data analytics initiatives to the cloud, it is extremely important to opt for the right cloud. While you do this, make sure to think of the long-term; choose cloud configurations that offer consistency and flexibility. Your cloud choice should also depend on the type of data. For instance, analytics involving sensitive data can be done using a private cloud. For your not-so-sensitive data, you can move analytics capabilities to the public cloud. 

3. Invest in Appropriate Tools

Most organizations are under the assumption that they can instantly exploit the capabilities of data analytics in the cloud. But that’s not the case. The widening range of data types and formats requires specialized tools that effectively load information into the cloud and enable accurate analytics. You must also ensure all the data you move to the cloud is complete, consistent, and updated. The right tools can help create necessary settings and optimization mechanisms to efficiently migrate your analytics projects to the cloud.

4. Ensure Proper Governance

Moving data analytics to the cloud is not without security and privacy concerns. To make sure your data sets and the resulting insights are secured 24/7, you must establish proper governance. Plan your migration with a constant focus on security. Ensure data is always encrypted and strengthen access control mechanisms to allow only authorized users to have access. 

5. Engage with a Qualified Partner

Moving data analytics to the cloud can get extremely convoluted for already stressed and burdened in-house teams. Engaging with a qualified partner can streamline the transition process and enable you to move analytics to the cloud without major hiccups. A partner can help identify the right use cases, leverage proprietary solution accelerators and frameworks, and also streamline integrations and storage. 

In addition, a partner can help build a strong data architecture, enable self-service, and pave the way for advanced visualizations. Such support and guidance can help you achieve your data analytics goals while remaining focused on your core operations. 

Drive Your Business to New Levels of Success with Recode Solutions

If you want to drive your business to new levels of success, you need to outsource the complex task of moving data analytics to the cloud to a competent partner. We at Recode Solutions can help you craft your cloud analytics journey, so you can focus on your business. We can help you build a strong data architecture end-to-end, support your data ingestion and implementation, and help you make the right cloud migration decisions. 

Learn how we helped an Australian bank power its open banking strategy by building a big data solution on Azure. 

Explore our cloud data analytics capabilities and learn how we can help you make the most of modern cloud capabilities.