We live in a data rich world, there’s no doubt about it. Google some curiosity of yours and I bet ya someone out there has already compiled the data, tested a hypothesis and published his/her findings.

I constantly search the cyber world over for the clues that would lead me to the best, empirically justified investment recommendations I can possibly make to our clientele. Up until very recently I’d pull from literally dozens of data vendors, some free, some at a stiff price, spending hours on end, week after week, in an effort to derive a plausible explanation of the present condition of the financial markets. I always have an opinion that I effort mightily to back test.

I recently took the plunge and made an over-sized investment in what I believe to be the most robust research tool available today. In case you were wondering why only one written blog post last week (you’re welcome by the way), now you know. This new tool has me utterly infatuated.

I can now test virtually any hunch I may have about what happens when something happens in a mere fraction of the time I’m accustomed to burning. Or, I should say, I can test what has happened historically, when something happens. I’m talking sectors, regions, currencies, commodities, every equity and fixed income index on the planet, individual securities, virtually all forms of derivatives (think options and futures), the deepest economic statistics, and analyses, covering the world over, and more from a single hub.

The angles from which I can approach each of the above are virtually endless—that is, I’ve yet to encounter the all too familiar (to my old system) brick wall.

So now what? Good question! You might think that the quality of our advice is about to improve exponentially. Well, not so fast! While, indeed, the quantity of the data at my finger tips has just exploded, my discoveries thus far have essentially confirmed what I already knew. For one, that finding correlation is one thing, discovering causation is an altogether different proposition. For another, that the greatest research tool on the planet does not a successful market-timer its operator make.

I can see where a given data set had offered up some consistency—some presumably predictive quality—over an extended time period, only to break down when you’d least expect. I’ve charted price movements (of a broad index, a sector, a commodity, and/or a measure of, say, inflation and/or sentiment) that tell me virtually nothing when checking the annual frequency box. However, check the monthly box and suddenly patterns develop. By the way, you have my solemn promise: never will I make a recommendation based on monthly price movements (although it might inspire a blog post every now and again). And I refuse to check the daily frequency box.

This would be one of the tools used by many a hedge fund manager, who, along with his peers, produced a paltry average return of 7.5% last year (not remotely close to matching the results of the equity portion of your portfolio [assuming you remained diversified and didn’t try to time the market), and has grossly underperformed the broad market averages for the past five years running. As for all of those prancing prognosticators with their ever-compelling charts (many generated by my new tool), well, frankly, their misses come far more frequently than do their hits.

So why, if this cornucopia of data only confirms what I already know, did I make the investment? While I realize I just to some degree discounted the efficacy of my industry’s most robust research tool, it’ll, at a minimum, get me to certain conclusions (such as our current sector targets) in a fraction of the time my research used to take—with a greater source of data to pull from to boot (there’s a lot to be said for productivity). As a portfolio analytics tool, among a zillion other things, I can now easily track risk and return attributions by sector, region, market cap, etc., over any chosen time period. Talk about back testing our targets! As an economy-tracking tool I can, for just one example, now, more easily than ever, track the very same data Janet Yellen and company follow to determine interest rate policy. At a minimum that’ll help us manage client expectations.

For example: It’s common knowledge that Fed chair Janet Yellen’s chosen inflation indicator is the Employment Cost Index (ECI). And when I chart that index along side the PCE (personal consumption expenditures) Deflator (the Fed’s chosen inflation measure [as opposed to CPI]) I can see the relationship, and I can see why the Fed is pretty comfortable with their present approach to short-term interest rates (no raising in the near-term). However, when I lay on the JOC-ECRI Industrial Price Index I see an even stronger correlation with inflation, and I see a present direction that, if sustained (with a little acceleration), could (maybe) impact PCE to the point (once again!) of rude awakening for the Fed—not to mention for all of those investment gurus who are betting big on low interest rates as far as their myopia-ridden eyes can see.

My point? While, organized and assessed properly, you can’t have too much information in my business, you can’t—as evidenced by the track records of hedge fund managers, media prognosticators, econ professors and, yes, Fed governors (all phenomenally bright people)—be too humble either…