The term “big data” not only refers to the size of the data to be handled but also relates to the competencies required and challenges associated with storing and analyzing huge data sets to arrive at meaningful data-driven decisions. Companies that use big data analytics have the potential to significantly change the way their businesses perform and compete. Organizations that invest wisely and successfully derive value from their big data will have a distinct advantage over their competitors — but on the other hand, lack of understanding and incorrect planning could lead to project failures resulting in significant losses. Some of the primary reasons for failure could be related to a lack of understanding about how to get started with big data projects. Most companies end up making big investments in tools and trained resources rather than address the actual business problem, and that could be a wrong approach.

Studies show that nearly two-thirds of big data projects fail to get beyond the pilot phase and end up being abandoned.

So, the best way to move forward with your big data analytics projects is to change mindsets, learn from mistakes of others, and adopt best practices to make your project a success.

This blog defines some best practices that can be followed to achieve desired results with your big data analytics projects.

Plan your business objectives

Organizations must spend a considerable amount of time in planning for the big data project by understanding the business problem that they want to solve with the help of big data. They must propose a clear and concise business case with expected ROI to get project approval. Many organizations just dive into a range of big data projects without clear business requirements. Only after your business requirements are defined, you can plan the next step, which is the selection of business tools, logistics, resources, execution steps, and training requirements.

Take the help of experts while planning

Another major mistake that organizations make is that they do not take the help of subject matter experts (SMEs) or data scientists who are critical resources while planning big data projects. These are useful resources that have expertise in mathematics, logic, business acumen, and institutional knowledge.

Choose your team wisely

Create a team with the right mix of resources that can work on exploring big data. This team should work with business analysts and data scientists and should be capable of building and configuring the selected big data platform. This team should typically include IT resources with BI expertise, data analysts with deep statistical knowledge, and business SMEs.

Choose the right big data analytics tools

Many vendors take undue advantage of the hype created around “big data” and use the words “big data analytics” loosely onto their product descriptions. Before deciding to purchase any big data analytics products or storage platform, you need to ensure that the tools and technologies that you select should address your business requirements.

It is useful to run a proof of concept using at least two products and do comparisons before you choose the right tool for your business. The tools should also be able to integrate easily with your existing infrastructure, such as back-end systems and enterprise platforms.

You must ensure that the tools are easy to use for both your technical and non-technical resources.

Keep Security considerations foremost when working with big data

While working with huge volumes of structured and unstructured data, it is necessary to protect sensitive customer information from hackers and unauthorized resources.

Security measures should include deploying the basic enterprise tools, such as data encryption wherever applicable, identity and access management, and network security. In addition, you should also include appropriate training and enforce strong policies related to the proper access and use of data.

Align project actions with business goals

Other good practices that you can consider are:

• Include big data techniques in data governance, data quality master data management, and metadata management.
• Have adequate training for users so that they understand the new data and can work efficiently in integrating data into the analytical and reporting platforms.
• Build a big data center of excellence (CoE) to identify new big data technologies, learn new skills, and gain a fully-functional practice of Hadoop, Spark and other emerging open source technologies Once these skills are developed, processes can be deployed into the business to speed up production.

Conclusion

Big data and analytics can be an important competitive differentiator for your business. To leverage the benefits, you must define a viable strategy and develop required skills that are needed to succeed with your big data analytics endeavors.