If history is any indication, companies will encounter false starts in Big Data initiatives, like we did in the early Data Warehouse days. I see similar confusion in terms of the tools, types of solutions. The variety, volume and veracity of new tools and technology companies offering solutions are enormous. For starters Big Data is tech heavy and geeky. One should know when you see the green screens and Unix/Linux prompts and wonder whatever happened to the GUI. Part of the reason why getting business value out of it is difficult.
But not to worry, technological advances and lessons from the history, will keep us straight with options. Before we talk about that let’s look at what it takes to implement a decent Big Data solution. Assuming the dream team with all the right skills at the right price are in place. Just to prove if the Data is worth producing the business value you are looking for will cost at least 715K. This does not include HW and SW. You get the picture where it is going, expensive experiment no matter how bare to the bones you get.
So how to minimize risk and know the business value for real than some hypothetical assumptions and hypothesis, especially when it is going to cost close to a million dollars. The good news is we have options. Depending on the level of or organization’s maturity in handling Big Data Analytics the following options are worth considering
- Leverage the cloud
- Leverage Partners
- Conduct a POC to identify the value of the Data in question rather than going full steam ahead
Leveraging cloud option relieves the IT infrastructure delays. It also provides ways to compare different solutions. Partners bring not only technology solutions but also provide industry experts who have done this at different clients. Finally POC will validate the assumptions or even level set the expectations.
