Cross-posted from the SQL Server Blog's PASS BAC Preview Series
by Hyoun Park

When Johnny Lee wrote his country classic, Looking for Love (In All The Wrong Places), he wrote with such heart, such pain, and such meaning that you just knew that he was writing about the challenge of creating a business plan for Big Data. For those of you who know the song, you might have missed this detail because you were so caught up in the story. Or perhaps maybe the soothing melody just took you away. But in any case, even a cursory understanding of the lyrics makes it clear that this song was meant to provide guidance to the enterprise analysts and project managers trying to figure out why Big Data is going to help them out.

Just think of the first line, "Well, I spent a lifetime looking for you/Singles bars and good time lovers were never true"

Who hasn't spent a lifetime thinking about how data could help their organization? But the challenges of integrating Big Data into sales, marketing, service, product development, HR, operations, and manufacturing were just too challenging. You could never settle on the correct solution. And when you chose that simple SaaS solution for a one-time need, it never quite worked out the way you expected.

If only there were a roadmap for figuring out how and where to begin in a cost-effective manner. And a way to prioritize how to set a realistic business goal for analytics. And it didn't take a lifetime to find...

"Playin' a fool's game hoping to win/And telling those sweet lies and losing again"

Amen to that. How many promises were analytics supposed to solve? When these analyst firms start throwing around claims like "Analytics pays back $10.66 for every dollar spent," people start to think that kind of return is possible and expected. (OK, I may be guilty for that last statistic.)

But how do you get to that kind of return? How do you play the game of analytics so that this is a realistic business return and not just a sweet lie you tell to your CIO before finding that those returns aren't happening after all?

"I was looking for love in all the wrong places/Looking for love in too many faces/Searching their eyes looking for traces of/What I'm dreaming of"

The vendor landscape for Big Data analytics and data management is enormous. There are a few short lists and short cuts for general analytics deployments, but there are so many specialized tools and new vendors that it is hard to keep up with them. It would be a lot easier if there was a simple way to weed out the contenders from the pretenders without pulling out a full-fledged RfX.

"Hoping to find a friend and a lover/I'll bless the day I discover/another heart ,looking for love."

Somewhere out there is that One True Pairing for your company: Big Data that has the functionality that the IT office wants, the usability that the line-of-business wants, the cost structure that the CFO wants, the support that service and help desk personnel want; and the agility and scalability buzzwords that your executives keep going off about. Should all of these be equally as important? Or are there certain areas where you can skimp on your analytics investment so that you can focus on the areas that truly matter?

"And I was alone then, no love in site/I did everything I could to get me through the night/I don't know where it started or where it might end/I'd turn to a stranger just like a friend."

When you're tasked with building the business case, it sure feels lonely. And you do go out to anyone in Project Management or IT land who has done this before to get some advice. Do I use TCO or ROI and how do I do that without leaving anything out? Am I just looking for some basic business requirements? Will I ever finish this business case or are we just going to end up taking a blind leap into building a data warehouse or implementing a Big Data appliance? Is this going to end up being an all-nighter to figure all this out? Is SQL Server enough or is it time for Hadoop?

"Then you came a-knocking at my heart's door/You're everything I been looking for"

That's the goal, isn't it? Unfortunately, it's probably not going to be as easy as having your analytics solution and all of the value propositions fall in your lap. But there are a number of basic findings that can help you to estimate some of the value propositions you're looking for, such as the keys to maximizing potential ROI, the best way to measure indirect benefits, the four stages of the Analytic Enterprise, the five key components of analytic benefits that Nucleus has identified through over 60 case studies, and attributes that provided Big Data users with an average incremental 241 percent ROI over their existing analytics efforts.

If, like Johnny Lee, you've been trying to build the business case for Big Data in all the wrong ways and in all the wrong places, you should stop by my session at the PASS Business Analytics Conference on April 11th so I can help you find everything you've been looking for in a Big Data business case.

Learn more from Hyoun at his PASS BA Conference session, "Building the Business Case for Big Data."