I spent yesterday recovering from DataBeat, the two-day VentureBeat Big Data conference held on May 19-20 in SF. And I do mean recovering…not only were there lots of content, but in between sessions, a DJ was dropping beats as if I were back in the NY dance clubs. Almost felt like dancing, but I restrained myself…

The second iteration of this conference promised to present more use cases to show that Big Data can actually deliver business value. I was very pleased to see that DataBeat delivered on the promise…many thanks to Matt Marshall (VentureBeat CEO) and his team.

So where are we with respect to extracting business value from a Big Data project? Well, plenty of well-known companies talked about their experiences and finding “success” – Western Union, NYSE Euronext, RMS, Ford, Charles Schwab. The success would vary – managing data efficiently, uncovering ways to retain customers in shorter periods of time, finding ways to complete data-related activities more efficiently. So that must mean that Big Data is ready for prime time. Every company should be getting on the Big Data wagon, get solutions in place, and start analyzing the heck of their data assets.

Not so fast…

In my last conversation of the conference, I learned that one question still remains with lots of companies…Where do I get started with this Big Data thing?

Why is that question still being asked?

Perhaps people cannot comprehend the myriad of solutions out there and determine which ones will really fit their needs. Perhaps there is so much expectation tied to Big Data that people think that if what they purchase does not bring in “results”, then they have failed. Perhaps they are still trying to determine how exactly a Big Data solution will apply in their companies.

The “Holy Grail” of Big Data is to uncover meaningful insights as fast as the data you are collecting is being generated. So how do you start with Big Data? Consider what needs to be in place to ensure that you are on the path to that “Holy Grail”. Does the data need to be stored differently? Does it need to be curated and managed differently? Are the tools in place to ingest the type and amount of data you want to analyze? Is the organization equipped with the skills and knowledge to use these solutions?

I’m hearing, “Well, it’s not as easy at that, because x,y,z, my dog ate my homework, and these constraints exist, and whatever else.” Guess what? You asked me where to start, not to make it easier. The market is still nascent, and more solutions will continue to emerge. It is not easy to wade through all of the solutions out there. It is easier, however, to focus on what needs to be done first to achieving the “Holy Grail” a little less painful.