What You Should Know About Machine-Aided Analytics


More organizations are supplementing their analytics capabilities with intelligent systems that are easier to use than ever. While the results may look impressive, the devil is in the details.

Credit: Pixabay
Credit: Pixabay

There is a key difference between traditional analytics systems and some of the newer analytics systems that is very important. If you understand the difference, you'll be a step ahead of your peers.

Old: Input → Output

Traditional analytics systems tend to be rules-based which means they have "if/then" scenarios built into them, so if a user clicks the red button, one result occurs. If she clicks the blue button, then another result occurs. The key thing to know here is that, assuming the programming is done right, an input results in a predictable output. That's great, but it doesn't work so well with the complex Big Data we have today, which is why machine learning is gaining momentum.

New: Buckle up

Modern systems use machine learning to provide more intelligent solutions. The solutions are more "intelligent" because the machine learns what humans feed it, and depending on the algorithms used, they may be capable of learning on their own. Training by humans and self-learning allows such systems to "see" things in the data that weren't apparent before, such as patterns and relationships. The other major value, of course, is the ability to comb through massive amounts of structured and unstructured data faster than a human could, understand the data, make predictions on it, and perhaps make recommendations. It is the latter characteristics -- prediction and prescription -- that are most obvious to analytics users.

What's not well understood is what can potentially go wrong. An analytics system designed for general purpose use is likely not what someone on Wall Street would use. That person would want a solution that's tailored to the needs of the financial services industry. Making the wrong movie prediction is one thing; making the wrong trade is another.

As users, it's easy to assume that the analytics we get or come up with are accurate, but there is so much that can affect accuracy -- data quality, algorithms, models, interpretation. And, as I mentioned in my last post, bias which can impact all of those things and more.

Why you should care

There is a shortage of really good data science and analytics talent. One answer to the problem is to build solutions that abstract the complexity of all the nasty stuff -- data collection, data preparation, choice of algorithms and models, etc. -- so business users don't have to worry about it. On one hand, the abstraction is good because it enables solutions that are easy to use and don't require much, if any, training.

But what if the underlying math or assumptions aren't exactly right? How would you know what effect that might have? To understand how and why those systems are working the way they are requires someone who understands all the hairy technical stuff, like a car mechanic. That means, like a car, do not pop the hood and start tinkering with things unless you know what you're doing.

Some solutions don't have a pop-the-hood option. They're black boxes, which means no one can see what's going on inside. The opaqueness doesn't make business users nervous, but it's troublesome to experts who didn't build the system in the first place.

Bottom line, you're probably going to get spurious results once in a while, and when you do ask why. If it's not obvious to you, ask for help.,

What's your experience?

Ever scratched your head over an analytical result you couldn't understand? What did you do? What was the outcome? We'd love to hear about your experiences in the comments section.

Lisa Morgan, Freelance Writer

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include big data, mobility, enterprise software, the cloud, software development, and emerging cultural issues affecting the C-suite.

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Re: In the wings?
  • 9/9/2016 10:55:52 AM
NO RATINGS

Agreeing that "self-learning allows such systems to "see" things in the data that weren't apparent before, such as patterns and relationships" of course doesn't mean it's always going to be accurate in using the results to predict or even explain the past. There's still a lot of serendipity that's going to come into view.

Re: Crummy analytical results
  • 9/1/2016 11:13:12 PM
NO RATINGS

..

Louis writes


Good point that Machine Learning should be used to eliminate the obvious, but apparently Amazon doesn't want to invest in this - it is good enough for them to just follow you throughout the Net and never let you forget you happened to look at an item.

 I think Amazon's goal here is basic - just "don't forget about us" regardless of the relevance or often ill-relevance of the Ad.


 

Maybe they'll eventually figure out that customer annoyance is not helpful to goodwill toward their retailing and marketing?

Then maybe they'll decide it's worth spending some extra dough to upgrade their algorithms ... say, to not just keep repeating in their ads what you just purchased, but perhaps to display ads along the lines of "People who bought X also bought Y".

That oughta be fairly easy to do, since they already display promos like that on their item webpages. In a couple of cases, it's persuaded me to purchase one or even all of the suggested products that are related to the item.

Anything should be better than over and over again trying to get me to re-purchase that thing I just bought.

 

Re: In the wings?
  • 8/31/2016 7:12:52 PM
NO RATINGS

I understand, but I am impatient.

Re: In the wings?
  • 8/31/2016 5:00:18 PM
NO RATINGS

Pierre, absolutely correct on clients that are dismissive when they don't know or can't grasp the subject. It can be tricky, so you have to maneuver carefully to keep them engaged. The ones that Hide behind jargon is the most frustrating to me.

Re: Crummy analytical results
  • 8/31/2016 3:37:32 PM
NO RATINGS

I have heard of ad decay, but I think it's been a theory rather than a specfic set of actions to be implemented. Maybe with ML in place, more study can lead to a body of best practices.

Re: In the wings?
  • 8/31/2016 3:29:25 PM
NO RATINGS

Tom that will take a bit of time to get to. As I mentioned in another response, it's challenging for people to admit what they don't know within a professional context.  I think people will get better at it, but it will take time.

Re: In the wings?
  • 8/31/2016 3:26:36 PM
NO RATINGS

Rbaz, I agree - it's too hard, especially in a meeting or discussion, to admit that one does not know an answer. I get that from small business clients, and I have to weight if the response is backed by an earnest want to learn more or a dismissive action that will cost me more time and money to explain.  

 

Some usually do not understand analytics, but if I keep a good tone and explain clearly, sometimes it leads into something more. But not understanding creates a cul-de-sac in the diaolog. I need the potential client to explain what they need.  If they don't understand, they'll sometimes try to mask it. I always assure clients that not understanding is ok, and should be expected at the start. We just need to work on the data, metrics, and media that will allow progessive steps to understanding.

 

Re: In the wings?
  • 8/30/2016 3:51:23 PM
NO RATINGS

Tom, one of the hardest things to do is having someone admit that they don't understand or grasp what is being said. The inclination is to assume that the material is so brilliant that it's beyond them rather than challenge it for clarity. Going along without full understanding is flying blind with faith the navigator.

Re: In the wings?
  • 8/30/2016 3:29:39 PM
NO RATINGS

Now if we can get more people to understand the dangers os using something that you don't know what it does.

Re: In the wings?
  • 8/30/2016 2:21:06 PM
NO RATINGS

Tom, I am in complete agreement with you. The higher the stakes, the more I want to know what's in the black box. Let's try and see or trust me is not an option when a lot is riding on the result.

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