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Companies have a large problem of having their data tucked away or inaccessible to the stakeholders. When people talk about AI / ML what they actually need is t

Companies have a large problem of having their data tucked away or inaccessible to the stakeholders. When people talk about AI / ML what they actually need is their data cleaned to the point where they can communicate to their stakeholders. Also, all of the companies who sell AI / ML as consultants are really good already at cleaning data. When companies actually hire data scientists what they typically do is clean data for a few months to a year . Then they interpret the data by probably being able to perform linear regression. At that point the data is in a state where it can be easily understood by those stakeholders and then they have created value. Whether or not the linear regression or whatever model has been learned may mean something. But, at the end of the day you need to tell stakeholders how they can create value and guess what SQL and Bash will do 90% of the job.

soared 9y ago
Agreed. The advertising agency I previously worked at would take the first month and only work on how data is collected and stored. After that first month they would begin addressing goals, plans, etc. About a year later is when true value would be realized because data that previously never existed could be analyzed. Few things were more enjoyable for me than getting a new client, imagining what analysis I'd like to do, figuring out what data would be necessary, and then implementing the system to make it reality.
- mygo 9y ago
  how did you convince them to stick on for a year when most clients want to see some results within 2 months or they give up? according to your schedule they’d ask what I’ve been doing and if I tell them I’ve just been “collecting data” that translates to them as “I haven’t done anything in 2 months”. If I say “keep paying me and you’ll see results in a year” that translates to “I haven’t done anything in 2 months but I want you to continue to pay me for another 10”. What do you do to qualify a client? How do you know your engagement with them won’t just waste everyone’s time when they quit halfway through and then damage your reputation?
  - 1123581321 9y ago
    Companies with budgets are willing to think in terms of quarters or years. You’ve laid out the plan for them and they’ve agreed to it. The money is allocated in an annual budget or obtained with a purchase order, so it’s not going to be suddenly reallocated unless there’s a big problem. Experienced clients wouldn’t move to another agency to do analytics “faster” because they would know they’d just have to ramp up the relationship and the systems again.
    - mygo 9y ago
      well then in that case in many regards working with companies that have quarterly / annual budgets is easier than working with clients who do not. But it appears to be harder to make the sale if you don’t already have a legendary track record that warrants their one-year-commitment.
      - 1123581321 9y ago
        No, analytics management is something many companies feel they need to pay for since the marketing team wants to show good numbers, they need to justify ad spending, web development etc. You do need a good relationship with a client to sell more than piecemeal work, but you don’t need to be “legendary” to get enough clients.
      - PeterisP 9y ago
        Making B2B sales is hard, that's unavoidable. Expect to spend literally as much manpower and budget on sales as you do on engineering. Also, you need a proven track record - if you don't have previous clients, have a working proof of concept for something similar; which can lead you into signing them up for a 3-6 month pilot project, followed by a real contract or two and maintenance. This also leads nicely to the pricing - this can not be done cheap, since if you sell, say, 20 man months of engineering to a customer, it's not enough to get paid for 20 man months of engineering they do; the contract also needs to pay for 5 man months of sales (including engineer time) it took to arrange their contract, the 15 man months of sales you spent on other, failed leads to get this one contract (I mean, most sales will fail), and for a part of 20 man months of R&D it took to develop the various proof of concepts and demonstrations that were absolutely necessary to start getting contracts but not paid by anyone.
jrs95 9y ago
Even worse than stakeholders not having data is stakeholders having relatively raw data and building “reports” themselves with Excel that don’t actually mean anything. Sometimes this is just done to provide justification for something someone wants to do, but over time these things get built up and entrenched and the business is actually driving decisions off of completely meaningless metrics. So while it may be tempting to simply hand data over to the business because “that’s not my job”, that can totally come back to bite you in the ass when your product roadmap is being dictated by ill conceived Excel formulas.
- zitterbewegung 9y ago
  It gets even worse when they want an outcome that they themselves only believe instead of being impartial.
rdlecler1 9y ago
This is a very good point. Analysis can become quite trivial when you have clean data. With messy data you may need 3+ orders of magnitude of data which is often not possible. Unfortunately VC pays a higher premium for data scientist than data janitor.
nkozyra 9y ago
100%. What this all boils down to in 2018 is data. Data acquisition, data cleansing, data filing, etc. But what else is there, now? You're going to create a new, novel ANN network? Cool, can't wait to peruse your paper. Maybe someday it's considered groundbreaking. Right now it's not solving immediate problems. Immediate problems were solved with 1970s ideas + 2018's infrastructure.
- itronitron 9y ago
  Yes, and there are some exceptional papers from the 1960's as well.
- leblancfg 9y ago
  A word against hasty generalisation - just because you don't interact with it on a daily basis doesn't mean it isn't being worked on elsewhere. There is an increasing number of ML-first companies out there. They are solving problems like object detection, portfolio strategy optimization, medical diagnosis, etc. Things that are 100% not possible to do with just SQL and Bash, and much less expensive than to have humans run those tasks. In those fields, these companies will outperform their competition on average. I think it's smart for investors to look for in knowledge and talent in the companies they look into. If there's AI/ML talent in the team, their value for acquisition (acqui-hire) is higher. If you want to solve problems for your existing company, you certainly don't need AI/ML. You can tackle many, many low-hanging fruits with just SQL and Bash. If you want to build the kind of company that will be around in 30 years, you need to have an ML-first mindset, and you probably need to start now.
  - nkozyra 9y ago
    My point is less that everything can be done with SQL, but that most of what people are doing is based on things possible 10, 20, 40 years ago. This leaves ml engineers dealing more with software modeling than ml modeling.
  - Mahn 9y ago
    Maybe we could all just agree that AI and ML have their use cases, but they are not necesarily a good fit to any and all computing problems indiscriminately?
kriro 9y ago
Pretty much this. However I think that you can actually get pretty far with cleaning a surprisingly small subset of data and just quickly tossing together a prototype. Talking to people helps a lot. "Ok so you have this and that data...how helpful would it be if you could predict the price of item X that you buy 10% better"...well ok it would be worth roughly Y$/year. Let's build a small prototype and see how well we can actually predict it. At this point you quite often find out that "oh we more or less predict it via one person who makes the buying decisions by experience" and it might take some convincing to actually compare the two. I'd summarize that the "big three" of ML/DL are all nontechnical 1) Finding the data/cleaning the data 2) Is there actually some reference you can benchmark against 3) It's not really all that hard and you might need less data than you think to show enough business value (broadly speaking a pretrained net + some usecase layers is good enough to get started for most business cases) #3 is interesting. I tend to be quite honest but I've seen a lot of mumbo-jumbo hand waving "oh it's very tough" which of course makes sense if you want to sell shiny new stuff. Reminds me a lot of ERP sales where I've seen basically SQL+a little webfrontend pitched as rocket science :D
darkhorn 9y ago
Where you can learn to become a data scientist? Linear regression and similar things? At what field you need to study? B.S. on Computer Science?

soared 9y ago

Agreed. The advertising agency I previously worked at would take the first month and only work on how data is collected and stored. After that first month they would begin addressing goals, plans, etc. About a year later is when true value would be realized because data that previously never existed could be analyzed. Few things were more enjoyable for me than getting a new client, imagining what analysis I'd like to do, figuring out what data would be necessary, and then implementing the system to make it reality.

mygo 9y ago
how did you convince them to stick on for a year when most clients want to see some results within 2 months or they give up? according to your schedule they’d ask what I’ve been doing and if I tell them I’ve just been “collecting data” that translates to them as “I haven’t done anything in 2 months”. If I say “keep paying me and you’ll see results in a year” that translates to “I haven’t done anything in 2 months but I want you to continue to pay me for another 10”. What do you do to qualify a client? How do you know your engagement with them won’t just waste everyone’s time when they quit halfway through and then damage your reputation?
- 1123581321 9y ago
  Companies with budgets are willing to think in terms of quarters or years. You’ve laid out the plan for them and they’ve agreed to it. The money is allocated in an annual budget or obtained with a purchase order, so it’s not going to be suddenly reallocated unless there’s a big problem. Experienced clients wouldn’t move to another agency to do analytics “faster” because they would know they’d just have to ramp up the relationship and the systems again.
  - mygo 9y ago
    well then in that case in many regards working with companies that have quarterly / annual budgets is easier than working with clients who do not. But it appears to be harder to make the sale if you don’t already have a legendary track record that warrants their one-year-commitment.
    - 1123581321 9y ago
      No, analytics management is something many companies feel they need to pay for since the marketing team wants to show good numbers, they need to justify ad spending, web development etc. You do need a good relationship with a client to sell more than piecemeal work, but you don’t need to be “legendary” to get enough clients.
    - PeterisP 9y ago
      Making B2B sales is hard, that's unavoidable. Expect to spend literally as much manpower and budget on sales as you do on engineering. Also, you need a proven track record - if you don't have previous clients, have a working proof of concept for something similar; which can lead you into signing them up for a 3-6 month pilot project, followed by a real contract or two and maintenance. This also leads nicely to the pricing - this can not be done cheap, since if you sell, say, 20 man months of engineering to a customer, it's not enough to get paid for 20 man months of engineering they do; the contract also needs to pay for 5 man months of sales (including engineer time) it took to arrange their contract, the 15 man months of sales you spent on other, failed leads to get this one contract (I mean, most sales will fail), and for a part of 20 man months of R&D it took to develop the various proof of concepts and demonstrations that were absolutely necessary to start getting contracts but not paid by anyone.

jrs95 9y ago

Even worse than stakeholders not having data is stakeholders having relatively raw data and building “reports” themselves with Excel that don’t actually mean anything. Sometimes this is just done to provide justification for something someone wants to do, but over time these things get built up and entrenched and the business is actually driving decisions off of completely meaningless metrics. So while it may be tempting to simply hand data over to the business because “that’s not my job”, that can totally come back to bite you in the ass when your product roadmap is being dictated by ill conceived Excel formulas.

zitterbewegung 9y ago
It gets even worse when they want an outcome that they themselves only believe instead of being impartial.

rdlecler1 9y ago

This is a very good point. Analysis can become quite trivial when you have clean data. With messy data you may need 3+ orders of magnitude of data which is often not possible. Unfortunately VC pays a higher premium for data scientist than data janitor.

nkozyra 9y ago

100%. What this all boils down to in 2018 is data. Data acquisition, data cleansing, data filing, etc. But what else is there, now? You're going to create a new, novel ANN network? Cool, can't wait to peruse your paper. Maybe someday it's considered groundbreaking. Right now it's not solving immediate problems. Immediate problems were solved with 1970s ideas + 2018's infrastructure.

itronitron 9y ago
Yes, and there are some exceptional papers from the 1960's as well.
leblancfg 9y ago
A word against hasty generalisation - just because you don't interact with it on a daily basis doesn't mean it isn't being worked on elsewhere. There is an increasing number of ML-first companies out there. They are solving problems like object detection, portfolio strategy optimization, medical diagnosis, etc. Things that are 100% not possible to do with just SQL and Bash, and much less expensive than to have humans run those tasks. In those fields, these companies will outperform their competition on average. I think it's smart for investors to look for in knowledge and talent in the companies they look into. If there's AI/ML talent in the team, their value for acquisition (acqui-hire) is higher. If you want to solve problems for your existing company, you certainly don't need AI/ML. You can tackle many, many low-hanging fruits with just SQL and Bash. If you want to build the kind of company that will be around in 30 years, you need to have an ML-first mindset, and you probably need to start now.
- nkozyra 9y ago
  My point is less that everything can be done with SQL, but that most of what people are doing is based on things possible 10, 20, 40 years ago. This leaves ml engineers dealing more with software modeling than ml modeling.
- Mahn 9y ago
  Maybe we could all just agree that AI and ML have their use cases, but they are not necesarily a good fit to any and all computing problems indiscriminately?

kriro 9y ago

Pretty much this. However I think that you can actually get pretty far with cleaning a surprisingly small subset of data and just quickly tossing together a prototype. Talking to people helps a lot. "Ok so you have this and that data...how helpful would it be if you could predict the price of item X that you buy 10% better"...well ok it would be worth roughly Y$/year. Let's build a small prototype and see how well we can actually predict it. At this point you quite often find out that "oh we more or less predict it via one person who makes the buying decisions by experience" and it might take some convincing to actually compare the two. I'd summarize that the "big three" of ML/DL are all nontechnical 1) Finding the data/cleaning the data 2) Is there actually some reference you can benchmark against 3) It's not really all that hard and you might need less data than you think to show enough business value (broadly speaking a pretrained net + some usecase layers is good enough to get started for most business cases) #3 is interesting. I tend to be quite honest but I've seen a lot of mumbo-jumbo hand waving "oh it's very tough" which of course makes sense if you want to sell shiny new stuff. Reminds me a lot of ERP sales where I've seen basically SQL+a little webfrontend pitched as rocket science :D

darkhorn 9y ago

Where you can learn to become a data scientist? Linear regression and similar things? At what field you need to study? B.S. on Computer Science?