Data Projects ROI and VFM

Data Projects ROI and VFM

After a fun-filled 2014 I find myself in a reflective mood.

Last year brought new clients and technology partnerships, which made for a varied and enjoyable time. Yours truly personally spent more time on a plane then ever before, so hopefully more blog posts will follow as I share my travel tales.

A recurring theme last year was cost control and price sensitivity. Isn't it always?!?!?

As in many areas of IT, Big Data/Analytics practitioners can't be good at 'just' the technology part of the challenge. That's often the easy part.

Well rounded consultants also need to understand the politics on-site, contribute positively to conference calls with a 'cast of thousands', communicate with stakeholders, support project managers, mentor internal teams and understand how business applications will interact with the analytics platform, whatever that might be.

Unfortunately, clients increasingly expect these Data Rock Stars to be made available at commodity consultant rates.

As in any walk of life, there is always someone out there that says they can do a job cheaper. Surely the Professional Services (PS) day rate isn't the only metric in play when a data project is initiated?

To our surprise, especially against the backdrop of constrained finances, what is often lacking is an attempt to measure return on investment (ROI) for many Big Data/Analytics projects.

When the focus is narrowly fixed on PS daily rates, the ROI in terms of business value is the first casualty.

By deploying inexperienced consultants, or even offshore teams, we could indulge in a race-to-the-bottom and reduce PS rates. Not a problem.

However, as quality and results always matter, our clients are implicitly (and sometimes explicitly) asking for Data Rock Stars. It's simply not possible to provide Data Rock Stars at commodity consulting rates. It's also not VLDB's business model.

We have experienced too many Directors approve a cheaper RFP bid only to call us in 12 months when they realise to their disappointment that cheaper doesn’t mean better.

"The bitterness of poor quality remains long after the sweetness of low price is forgotten"- Benjamin Franklin

Who are we to argue?