Big Data: To Build Or Not To Build?

Searching the Internet, I was surprised to find a lack of complete build versus buy articles published on big data. There were plenty of technical papers, there were several high level articles on the cost of hardware, but no single research paper that looked at all of the various costs when deciding whether to build your own big data solution in house or to simply buy a pre-existing solution. After performing the research myself, I quickly realized why there was a lack of complete business level articles on this subject.

Each big data project and need is assumed to be completely different from one project to another. I argue that most companies are not as special as they believe. They all have different perspectives and have different technical competencies, but they all want to solve similar business problems using their data. Problems like “how do I perform customer attribution across many channels?”, or “how do I consistently make smart decisions to improve my key business metrics?”, or “how do I know where to spend my marketing budget in the next year?”, or “what are the trends across my business I can take advantage of?”

When talking to large and small enterprises, we found that these questions in various forms come up time and time again. So I decided to truly explore the build versus buy decision with big data and even contemplate whether that was the right analysis to do in the first place. Do you really need a big data solution? How much data does your enterprise generate in a month? Do you need a faster and more predictable ROI? Do you need real-time recommendations? Do you need a system that’s more intelligent than a SQL query?

Often, engineers and IT staff use technology inside a company. When does big data stop becoming technology and start becoming something managers can rely on? When does big data follow Arthur C. Clarke’s third law: “any sufficiently advanced technology is indistinguishable from magic.”

At Rubikloud, it’s our mission to ensure big data becomes just that: magic. We are on a mission to provide real business value in real-time from all of your data sources. That means providing actionable alerting when there are problems and long-term recommendations when opportunities arise within your data. You no longer have to worry about complicated integration, purchasing large server clusters, the unknown ROI, or hiring expensive data scientists.

Click here for the free build vs. buy research paper on big data.