Four steps to implementing a big data program in the cloud

Big data is the buzzword of the moment, and for good reason. Businesses are collecting more information from different sources ranging from direct customer interactions to Internet of Things (IoT) sensor data. Aggregated and analysed cleverly, this data can provide companies with actionable insights that can dramatically increase their competitiveness and improve the bottom-line.

However, organisations with limited resources are nervous about the technical and infrastructure challenges of implementing a big data program.Cloud infrastructure can provide the resources they need at a relatively low cost.

Because data comes from many sources, is fed into a variety of applications to maximise value, and big data workloads are constantly shifting, big data infrastructure needs to scale elastically. Cloud infrastructure is ideal, since users can scale up and down as necessary without incurring big capital expenses.

The high price of in-house big data infrastructure can hold back many smaller businesses, but it shouldn’t. Being able to rent cloud computing power can open up possibilities for all companies, including smaller ones. By tapping into cloud-based data solutions, any organisation can reap the same benefits as the big end of town.

There are four key steps for organisations looking to implement a big data project in the cloud:

1. Set a game plan

While a cloud-based approach doesn’t necessarily require big infrastructure and hardware investments, it is still important to plan ahead. Like any IT initiative, the time spent on planning to make decisions early in the project will influence the success and speed of future phases. The planning process should start by identifying potential specific business use cases such as customer loyalty, product development, and risk management.

An organisation wanting to leverage big data technology optimally must make sure it is using all of the relevant data it can muster. Be open-minded about what the data can tell you. Often big data analysis can deliver insights you never thought would be possible, and give you a new perspective on your business.

2. Overcome obstacles

Change management is just as important in any cloud project as it would be in a traditional IT implementation. Overcoming a legacy decision-making culture is essential to becoming a data-driven organisation. It takes experience and credibility to make an argument for a cloud-based big data strategy. Also, an effective change management program is crucial for a successful implementation. The substantial up-front cost of the in-house infrastructure needed for big data analytics was once a barrier to many businesses wanting to tap their data. Now, thanks to cloud-based big-data-as-a-service platforms, businesses of all sizes can use the technology with limited investment resources. This option is a fast and low-cost approach to starting on the big data path.

3. Ensure compliance

Organisations in every industry need to deal with data privacy and security management. Additionally, some organisations must comply with specific legislation around data storage, data sovereignty and security, or they could face stiff penalties. Companies must establish an effective data governance strategy to bolster the cloud-based big data project.

4. Future-proof

For a big data application to grow in size and capability, and therefore deliver an ongoing return on investment, it needs to be developed on an architecture that is designed to support its specific needs. This is why it is important to identify the baseline infrastructure and future requirements of a big data project. The scalability, performance, reliability, and security of the cloud infrastructure used for a big data project are key.

Big data projects don’t exist in a vacuum. Big data infrastructure elements should be flexible enough to support multiple approaches to integration with one another, as well as with external elements. An elastic cloud infrastructure gives you this ability, and makes it relatively simple to add or remove big data analytics capability on demand. This makes it a low-risk and cost-effective approach for organisations of all sizes.