I have consulted on enough enterprise system implementations to know that there's anything but uniformity on how to roll out a new set of mature applications. I've seen plenty of different methodologies and technologies for relatively similar back-office systems (read: ERP and CRM). Of course, some were better than others, although the results were remarkably consistent as I describe in Why New Systems Fail.

In comparison to technologies that handle what we now call big data, those internal applications differ on two fundamental levels. First, they are much less mature and, I would argue, by comparison relatively poorly understood. Second, while certainly essential to run myriad businesses, ERP and CRM applications house a relatively inconsequential amount of information. As Jordan Robertson writes in BusinessWeek:

The universe of data being generated and collected today is magnitudes larger than ever before. Companies are combing content that's online (blogs and social media) and offline (DMV and criminal records), as well as the growing amount of bits being spewed by the billions of Internet-connected devices (smartphones and thermostats). Computer-storage maker EMC estimates the amount of digital information in the world in 2020 will swell to 50 times what it is today.

And you thought that big data was, well, big today. We ain't seen nothin' yet.

It should be no surprise, then, that there's an even greater level of confusion diversity with respect to how organizations are operationalizing big data. In terms of strategy, I've written before about how there's no one right way to "do" big data. And let's not forget critical tech considerations – i.e., that there are many different types of distributed file systems and NoSQL databases.

Brass tacks: those looking for a playbook or checklist are often going to be disappointed.

Simon says

Regardless of the merits of any individual big data approach, organizations first have to choose "the right" way to store data. That decision is not as simple as it might sound. This isn't 1998, a relatively simple time in which large organizations considered mainstream relational databases like SQL Server, Oracle and dB2. More than ever, it's an extremely challenging question complicated by the fact that today there's anything but uniformity on core considerations like:

• where (read: cloud computing vs. on-premise vs. hybrid approaches)
• acceptable speeds
• user access
• optimal data-compression rates
• specific sources of information

As Robertson writes in his article, "Even when companies do put their data to work, the answers don't always come easy."

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