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How We Filter Information About IT Products

1,845 Words. Plan about 12 minute(s) to read this.

The amount of information to be found out about tech products is astonishing. Anything you’d like to know about virtually any product is a Google search away. The hits you’ll get back are loaded with information, some useful and some…less useful. I am awash in data to ingest each and every day. I start out most mornings reading through a variety of blogs and related content about the tech industry. The hard part isn’t gathering information. Rather, the hard part is coming to some sort of conclusion with all of the information presented.

The Path To Opinion

For example, I quickly thought up this list of ways in which I might discover information about a given IT product.

  • Press releases. I get these from PR folks all the time. They are useful as a guideline to two things. One, what the announcing company thinks is important in the marketplace. Two, what the announcing company has achieved, or alternatively, has promised to achieve.
  • Briefings. I am briefed by vendors more and more as vendors hope to leverage my influence as a podcaster and writer. They want to be sure they are a part of the conversations I’m having. If I don’t know about them, that can’t happen. The briefings are good for gaining a limited amount of insight into a product from the vendor’s obviously skewed perspective. I generally glean market positioning — which customers are supposed to care about this product and what they’ll do with it. I might also gain a particular perspective on the industry that a vendor holds, which usually is tied very closely to the product they are pitching.
  • E-mail. Many things related to products show up in my mailbox. Often, these are customer experiences, scuttlebutt among a group of friends participating in our secret mailing lists, newsletters, and the like.
  • Tweets. Twitter is a treasure trove of strongly held opinions about IT products. I mostly observe, and glean what I can from the sometimes violent, occasionally pathetic, but almost always entertaining exchanges.
  • Blogs. The blogosphere contains a mix of colossal ignorance, vendor diatribe, hardcore detail sourced in engineering prowess, genuine thought leadership, and everything in between. I gain lots of information from blogs. After a time, you learn an author’s particular bias, because it will come out again and again in their writing.
  • Podcasts. I listen to a few tech podcasts, and the information gathered varies in quality. Lately, I’m enjoying an analyst podcast where they talk through the software-defined market segment. This has been an education, because I’m finding out just how difficult of a segment cloud, SDx, and related technologies is to cover, largely due to vendor ambiguity in defining their products.
  • Analyst reports. In fairness, I don’t read these, because they aren’t especially useful to me. I still come at the IT market from an engineering & architecture point of view, and analyst reports, besides being costly to obtain, don’t help me understand whether or not a product is viable in my specific operational environment.
  • Whitepapers. These documents range in value from worthless to interesting. Some whitepapers are pure marketing, loaded with buzzwords and lingo. These are like eating cotton candy. You could swear you were eating something, but now that it’s all gone, all you’re left with is a vaguely sweet taste in your mouth and self-loathing. Other whitepapers get into the technical details of a product such that you can make an intelligent assessment of their usefulness and relationship to similar technologies you might already be familiar with.
  • Datasheets. Once you learn how a vendor specifies their products, data sheets become useful references for product sizing and grasping product capabilities. However, data sheet formats and metrics vary by vendor (Cisco different than Juniper different than Brocade different than Avaya), but all of them usually have the information you’re looking for if you peer into the columns long enough.
  • Personal experience. We all have opinions of technology based on actually using it. For example, my opinion of stacking technologies will forever be tempered by the fact that I had a bad experience that was a real pain to sort out at a data center I supported several years ago. Has stacking technology moved on and gotten better? Absolutely. Do I still sigh heavily and cross my arms reflexively whenever I evaluate stacking technologies? You betcha.
  • Documentation. Like most engineers, I’ve spent a lot of time reading technical documentation. Most of it is awful, obviously written by someone who neither understands how I will use their technology, nor the sorts of decisions I will need to make about their technology as I implement it. Cisco is the king of truly useful documentation, although they aren’t perfect. Still, documentation from most other vendors isn’t nearly as good as the thorough treatment and well-indexed goodness Cisco builds around certain of their products.
  • Indiscretions. If you hang around IT industry people long enough, you’ll catch them in, uh, situations shall we say, where they might say more than perhaps they should. While I never broadcast this information outside of the situation in which it was shared, this information is often very interesting as related to a company’s products, long-term goals, funding, and interpersonal relationships.
  • Word-of-mouth. I hear about many products because someone volunteers information about the product, good or bad. Sometimes people just ask me for an opinion of a product, whether I’ve heard of it or not. But that can be enough to spark an investigation on my side.

All of this data amasses in my brain. What I do with this information is run it through a series of filters. Some of these filters are consciously applied, and some not. Either way, I know that the information is filtered, and that filtration process leads to my opinion. There’s at least ten filters I can think of that could impact my opinion of a given set of data.

  • Nonsense. Certain data I filter out as absolute nonsense. Most often, this is in the form of vendor claims about their product that I know are rubbish.
  • Myopia. I recognize that I can see the world close to me, but not the world beyond my circle of vision. Therefore, I have to be careful to see a technical product in the light of how other people might use it. Judging a product solely based on its usefulness to my specific circumstance is inappropriate, and a filter I am wary of.
  • Missing data. I am constantly revising my opinion on products based on new information, meaning that at any given point in time, I am possibly missing key data points. I tend to shy away from strong opinions for this very reason. Too often, I’ve found out new data that’s changed my perception, meaning that missing data is actually a filter.
  • Opinion. New opinions are sometimes formed in the context of old opinions, as well as the opinions of others. If someone I respect thinks product X is garbage, I am predisposed to agree with them. Why? Because of that respect. Sometimes, that’s a valid filter to use, but just as often I find that I disagree with that person I respect because I detect that their own filters are preventing them from rendering an unbiased opinion.
  • Facts. If you sift through enough data about an IT product, you can find actual facts. Facts can be harder to find than you’d think. For example, throughput of DPI firewalls can vary widely depending on the mix of traffic pumped through them. Therefore, things as simple as raw numbers describing performance may, in fact, not be facts. But when you can find actual facts, these contribute to an opinion about an IT product.

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  • General cynicism. As a long-time engineer, I am naturally cynical about technology. I’ve been on the “upgrade to this new thing that’s so much better” hamster wheel for a long time. My cynicism stems from that fact that most IT refresh cycles trade in one set of problems for another, over and over again. Functional problems for users might be traded in for operational problems for IT staff. Opex problems might be traded in for capex problems. Etc. Why will this shiny new tool do anything any better than anything else? Reality is that it probably won’t, but I shouldn’t let that filter my opinion of a product’s merits.
  • Lies. Lots of people involved in the IT circle of life lie for lots of reasons. Some lies are subtle. Some are overt. Some are bold, brassy lies, where you nearly admire the prevaricator for their gumption. Some are more like misstatements or exaggerations. But lies they all are. If some of the data you’ve been given are lies, that will filter your ultimate opinion in ways that can’t be good.
  • Fear. Your opinion might be filtered because you are scared of what someone else will think of your opinion. I’ve seen bullying tactics on the part of vendors trying to control the opinions of the mainstream tech press and independent tech writers. I’ve been threatened myself to not say the wrong thing, and have also had vendors react negatively to things I’ve written. I know of folks in organizations who won’t recommend the product they really believe in because they are scared their management won’t be receptive. Fear is a powerful filter of opinion.
  • Specific distrust. There are certain vendors whose data I will always look askance at because they have lied to me, or the IT populace in general, in the past. Therefore, any new information or claims are filtered by a specific distrust, because in the past information supplied proved untrustworthy.
  • Bias. I have a number of my own biases that I have to be careful of. For example, there are emerging technologies I favor because I believe they are right and just and exactly what the world needs. There are certain companies I favor because I think they have employed brilliant, talented people who I respect. There are some products I favor because I’ve used them in the past, and they won my heart. Therefore, any new iteration of that product is likely to be thought of well by me, based on that bias rooted in previous experience. Bias is a really nasty filter, because it can be hard to change an opinion you’ve held, even when changing that opinion is warranted.

As I pondered this data digestion process, I was struck with the realization that what we each end up with is our very own, personal opinion. The opinions might be informed or ridiculous, but they almost assuredly are not representative of truth. Truth is something off to the side that we could only find with a complete set of entirely factual data, the digestion of which was untainted by our filters.

Sadly, truth is hard to come by.