In a recent LinkedIn posting just before the holidays, THE REVELATOR asked a very important question. A question that may have gone overlooked given that many people are busy trying to get their work done before the holidays so they can get a few days off. And a question that must NOT be forgotten.
1. How does the old technology phrase “garbage-in, garbage-out” apply to Gartner’s Data Fabric post?
Data files. Databases. Data stores. Data warehouses. Data lakes. Data Lakehouses. And now … the data fabric … which is, when all is said and done, just another bullsh!t organizational data scheme designed to distract you from the fact that your data is dirty, that data storage providers don’t know what to do about it, but these data storage providers still need to sell you on something new to maintain their revenue streams.
You see, the great thing about today’s SaaS middleware enabled apps is that they don’t care where the data is, what organizational structure the data is stored in, etc. As long as the data has a descriptor that says “this field, which is in this format, in this db stores X” (where X describes the data) and an access key, the SaaS middleware can suck the data in, convert that data into the format it needs, and work with that data.
However, now that we are in the age of “AI”, the most important thing has become good, clean, data. However, just “weaving” your bad data together doesn’t solve anything. In fact, with today’s technology, it just makes things MANY times worse. We are now at garbage in, hazardous waste out!
Unfortunately there’s nothing we can do if the AI zealots are now adding hallucinogenics to their kool-aid, because it sounds like they are trying to bring back the magical medeival Æther … *groan*
THE REVELATOR then went on to ask …
2. Why does Gartner confuse more than inform and enlighten?
At the end of the day, you have a better chance of appearing as an enlightened Guru to someone who is lost and confused than to someone who is clear headed and confident in one’s direction!
Like the other big analyst firms, they profit off of being the Gurus the executives turn to when they can’t make sense of the hogwash filled marketing madness they are inundated with every day!
More specifically, their sales people need to say: “Our senior analyst has all of the answers … and they can be yours at the low, low introductory price of only 9,999,99 USD a day*.” So they don’t really care about whether or not they are confusing more than enlightening, as long as the sales are coming in. (In fact, they aren’t even looking to see how they are doing as long as the money keeps rolling in …
* one day only, after that, full rate of 29,999.99 a day applies …
But the questions didn’t stop there. The next question was:
3. Why are Data Problems Solved Downstream?
The answer to this is not as easy or straightforward, but when you consider that:
- it’s hardwork to solve the problems at the source and
- most of these analyst firms are staffed with analysts with little fundamental understanding of technology or the domains they are analyzing the technology for, don’t want to admit it, and are happy to take guidance from the vendors cutting them the biggest cheques and spending the most time “educating” them on the paradigm the vendor wants to see …
What should one expect.
Case in point. Did IDC just happen to come up with a “Worldwide SaaS and Cloud-Enabled Spend Orchestration Map” on its own at the same time a whole bunch of these solutions hit mainstream? (Especially when it takes person years of research and development to design a new map and analyze vendors, at least if you want to try and get it right.) Especially when they don’t have enough senior analyst talent to adequately cover core S2P?
Another case in point. Did Gartner merge it’s P2P into a S2P map because it honestly believes the entire market is heading there (FYI it’s not, look at the Mega Map), or because it doesn’t have enough analyst talent left to attempt to cover the market fragmented?
At the end of the day, it takes many years and many degrees to get a fundamental understanding of modern technology (which all runs on math, by the way) and many more years to get expertise in a business domain … so what can you honestly expect of kids straight out of school who make up significant portions of analyst teams???
Which led to the next question.
4. Can innovation co-exist with exclusivity?
Innovation happens, but then big stalwarts in the space scoop it up to try and remain competitive enough to keep their current customers locked in, a vacuum is created, and the cycle starts anew.
Until Trump dismantles them entirely, the US, like most of the pseudo-free first world, has enough anti-monopoly laws to ensure the cycle continues.
So yes, innovation can coexist with exclusivity, it just takes decades to realize what could happen in less than one decade as a result of having to start over so many times.
Finally, this led to the final question:
5. Does the VC investment model of: for every ten investments, seven fail, two are mediocre, and one “hits pay dirt” have anything to do with the 80%+ technology project failure rate?
It most certainly does! The fact that VCs are happy for seven investments to fail entirely (and then just move the good people to other investments if those people want to keep working) doesn’t help the project failure rate … especially since so many companies don’t survive long enough to master models that will lead to success, instead of failure, 80%+ of the time or to take the time to gauge, plan, and do implementations properly (because, if they don’t sell the next deal within a quarter, the investors will drop them faster than a hot potato).