Welcome back to the third post in our series on how to get value from your data.
As we stated in a previous post:
“Data, without the proper use of analytics, is meaningless. If data is the new oil, think of analytics as the oil drills—you need both to be successful.”
Of course, getting to “success” is not easy as anyone involved in an analytics project will tell you. This series walks you through our methodology on what it takes—from inception to proof of concept to implementation and deployment—to navigate project pitfalls. However, if you’ve assembled a great team, you will be able to drill for all that oil. In our experience, great teams tend to develop, manage, and sustain successful analytics projects: It all comes down to having the right people with the right skill set. (more…)
Unless you’ve been asleep for the past couple of years, you, like us, have heard this phrase again and again: Data is the new oil. It certainly sounds great but what exactly does it mean? Here’s our take: Getting the most value out of your data can make you better at what you do as well as enable you to do more with what you have. In other words, there’s unrealized value in those data silos that all companies have. But make no mistake: the road to realizing data value is paved with good intentions and often times, poor execution and results.
Today, most companies are drowning in data—there’s historical data from operations, data from public sources, data from partners and acquisitions, data you can purchase from data brokers, etc. These companies have read all the research and want to leverage their data assets to make “better” operational decisions, to offer their existing customer base more insights, to pursue new revenue opportunities. Of course, the real value in that data is derived from the business analytics that deliver the insights that drive better decisions. As we’ve said quite often on this blog: Data, without the proper use of analytics, is meaningless. If data is the new oil, think of analytics as the oil drills—you need both to be successful. (more…)
A top-level view of our data project over a series of posts.
Welcome to the second post of a series on a big data project that will (Mary and I hope) provide clarity and insights on how to successfully complete a big data initiative. Now, just in case you’ve forgotten the first two rules in our Big Data Playbook, I am going to repeat them here because they play into our topic of the day which is all about “starting” your big data project:
Rule #1: Big Data IS NOT rocket science.
Yes, far too often those lucky internal folks tasked with managing a big data project fall into the trap of data science paralysis which is similar in thought to analysis paralysis. By this I mean that there are so many moving pieces to capture, so many technology decisions to make, so many skill positions that need to be filled, so many fill-in-the-blanks that need to get done that you never actually get started which leads me to our second rule:
Rule #2: Garbage in, garbage out.