Analytics IME: Technology Probably Isn't the Barrier to Delivering Data Value Anymore

One of the biggest surprises I've had during the last 3 years of working in the data space is that the barriers to achieving value from data are rarely technology based. What I mean by that is that I'm (often) not struggling to solve problems of how to process and centralize data, create tooling that enables reporting, or set up algorithms to predict future outcomes. Rather, I'm faced with solving challenges related to the people and processes that create and manage data much more often than challenges with technically processing the data itself.

Perhaps more importantly, that's not a knock on those people or processes. I doubt you would have had many people on project teams 10-15 years ago championing that applications need to be architected to support the consumption of their data for analytical use 20-30 years in the future. Additionally, it's hard enough to manage and communicate well within a team that's focused on delivering BI value and only consists of a data team and stakeholders, MUCH LESS manage multi-functional teams (Dev, BI, Consumers) that need to deliver applications that are performant and usable, analytics that support operations, and operational results that drive profitability and productivity.

So what's the point of this post? Mainly just to serve as a reminder to not get caught up on technology as you're working through your analytical projects, and if you’re coming out of school, be prepared for your soft skills (Empathy, Communication, Negotiation) to be key drivers of success on what you're working on.

At least this is my experience so far… What are the most common challenges that you're facing on your data projects?  

P.S. @LinkedIn Please increase your character limit, so I don't have come up with a catchy title for an article the next time I have a thought I want to share.