In recent years, prices for raw metals and minerals have skyrocketed.
Mining companies have fanned out to all corners of the globe,
conferences have been convened to discuss the topic, and all the while prices continue to rise.
But did you know that right now, at this very moment, there are tens of billions of dollars worth of iron ore and other valuable metals and minerals just sitting on the floor of every ocean on earth? Scientists have known about
manganese nodules for decades (as a matter of fact, geologist A.A. Archer estimated that the sea floors and abyssal plains of the ocean contain something like 500 BILLION TONS of untapped metal ore). Iron prices should be dirt cheap!
So what’s the problem? Why has this vast natural resource lain undisturbed for decades? The issue is that no one has been able to successfully come up with a way to mine it cost-effectively.
This got me thinking about the difficulties of different kinds of mining, specifically DATA mining.
For a long time, many companies have been talking about the ability to provide
Analytics. And I will admit that there are some phenomenally cool, whiz-bang products out there in the business intelligence space. However, there has always been one inherent problem with this: Analytics are only as good as the data they’re analyzing.
Those whiz-bang B.I. solutions can only do their cool stuff
once you have the underlying data.Getting at that data traditionally has been harder than stripping iron ore out of a manganese nodule eighteen fathoms below. This is the dirty little secret around why
more than HALF of all data warehouse projects fail.You see, first a company needs to figure out where the data IS, and more importantly, how to get at it. This usually involves getting IT involved and purchasing an ETL tool to extract and cleanse the data.
Then comes the hard part: aggregating the data into one place which requires architecting the data schema and building a
data mart or data warehouse. Then, a presentation or (BI) layer must be selected in order to view and analyze the data. Of course, you will need to do a lengthy requirements phase to talk about what you want to see, etc., reports will need to be built, multiple constituencies need to be involved. There are a lot of moving parts.
Lots of money has been spent just analyzing why these massive data warehousing projects fail – you can read just a small subset of some of the findings
here and
here.
What about analyzing your CRM data, you may ask? You could certainly do that, but what are you really analyzing? Pre-sales data entered by sales reps, complete with equal measures of sand-bagging on one side and pie-eyed optimism on the other.
No self-respecting CFO would run his or her business on such data. I’m talking about true POST-sales data – getting to this rich data is what will make Analytics truly sing.
What if a company could streamline this entire process and make it fantastically simple – giving you access to ALL your post-sales data without any intervention from IT, tedious consulting projects or ETL tools?
Well, don’t despair, Xactly to the rescue! But don’t just take my word for it…
If you speak to
Henry Morris (SVP Worldwide Software & Services Research at leading research firm
IDC) he’ll tell you:
"I always thought business intelligence [BI] on demand would have difficulty taking off, since the application has to get its data from an outside source. But Xactly already has your data.“As a byproduct of solving the variable compensation problem for companies, Xactly has pre-built a sophisticated data warehouse that contains every bit of this rich data. Not only that, but because of our fixed data schema (just one of the innumerable benefits of being a true on-demand company), every company can get access to this data.
We then layer a
wonderful Analytics engine over the top of it, complete with all the tools you could ever need, and now you’ve got something companies have thrown millions of dollars at, usually ending in frustration and tears.
Let us tell you more about it – you’ll wonder where we’ve been all your life.
Now that we’ve got this problem licked, I’m off to tinker with some ideas on these manganese nodules. I may have a few tricks up my sleeve.
Labels: analytics, Business-Intelligence, IDC, Xactly, Xactly-Analytics
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