Why Building a Big Data Platform Hinges Upon Sustainable Planning

May 17, 2017

Most businesses recognize the advantage of incorporating big data into their analytics stable. The ability to decipher insight from raw and unstructured data is invaluable. At least 48% of companies invested in big data solutions last year and one out of four companies expect to make an investment over the next two years. Building a big data platform is front of mind for many, but there’s a gap when it comes to the actual logistics of implementing the tools.

Before ever making it out of the pilot stage, 60% of big data projects grind to a halt. What is preventing so many big data projects from reaching completion? A number of large and small miscalculations about the whole process. And one of the most threatening to the lifeline of a big data platform happens early in the planning stages.

Going Too Big Too Quickly Is a Recipe for Failure

Many new big data platforms suffer from Icarus syndrome. Excited by the prospect of accessing untapped insight, these companies want to explore the full limits of the technology immediately. Their big data platforms are treated as if analysis needs to be comprehensive without considering the strain put upon their business.

In my own personal experience, going from nothing to all-inclusive big data strategies is rather toxic. Imagine a company that decides on building a big data platform with a multi-million dollar roadmap. Planning, building, implementation, and socialization will take considerable time with even one or two technologies. Incorporating any more big data tools than that to the primary platform destabilizes projects and makes their delivery far more haphazard.

Let’s say the organization in the above situation used six different champions all struggling to run six different use cases. Many of the same challenges these champions faced would overlap, creating duplicate efforts as they attempted to solve the same problem in fractious ways. Soon, their disjointed approach might reduce down to four use cases or less before the project was terminated. In these instances, it’s not uncommon for organizations to spend most of their budgets without any further traction in understanding the mysteries of the raw and unstructured data in their data lake. Projects like these end up being ROI disasters.

Setting Sustainable Benchmarks for Big Data Success

A more moderated approach is central to the success of big data implementation. Using a proof of concept approach and starting with a smaller strategic project will mitigate the risk of budgetary overspends before reaching the desired results. Similar projects performed with a small use case, lay the groundwork, work out the bugs, and explore how to get business users to adopt these new technologies. Once the project is running efficiently and has the full support of stakeholders, building a big data platform for other divisions of an organization is a much smoother prospect.

Want to learn more about the challenges of building a big data platform? Download our whitepaper “5 Avoidable Big Data Platform Mistakes that Companies Still Make.” We analyze the most common threats to your big data solutions and guide you through the most complicated aspects of the process.



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