20 Analytics Predictions from the “Experts” for 2020 Part I

Guess how many will be 100% accurate?

(We’ll give you a hint. You only need one hand. You won’t need your thumb. And you’ll probably have fingers to spare.)

the doctor has been scouring the internet for the usual prediction articles to see what 2020 won’t have in store. Because if there is just one thing overly optimistic futurist authors are good at, it’s at pointing out what won’t be happening anytime soon, even though it should be.

This is not to say they’re all bust — some will materialize eventually and others indicate where a turning point may be needed — but they’re definitely not this year’s reality (and maybe not even this decade’s).

So, to pump some reality into the picture, the doctor is going to discuss the 19 anti-predictions that are taking over mainstream Net media … and then discuss the 1 prediction he found that is entirely 100% accurate.

In no particular order, we’ll take the predictions one by one.

Performance benchmarks will be replaced by efficiency benchmarks

This absolutely needs to happen. Performance benchmarks only tell you how good you’ve done, not how good you are going to do in the future. The only indication of that is how good you are doing now, and this is best measured by efficiency. But since pretty much all analytics vendors are just getting good at performance benchmarks and dashboards, you can bet efficiency is still a long way coming.

IoT becomes queryable and analyzable

… but not in real-time. Right now, the best that will happen is that the signals will get pushed into a database on a near-real time schedule (which will be at least daily), indexed on a near-real time basis (at least daily), and support meaningful queries that can provide real, usable, actionable information that will help users make decisions faster than ever before (but not yet real-time).

Rise of data micro-services

Data micro-services will continue to proliferate, but does this mean that they will truly rise, especially in a business — or Procurement — context. The best that will happen is that more analytics vendors will integrate more useful data streams for their clients to make use of — market data, risk data, supplier data, product data, etc. — but real-time micro-service subscriptions are likely still a few years off.

More in-memory processing

In-memory processing will continue to increase at the same rate its been increasing at for the last decade. No more, no less. We’re not at the point where more vendors will spend big on memory and move to all in-memory processing or abandon it.

More natural-language processing

Natural language processing will continue to increase at the same rate its been increasing for the last decade. No more, no less. We’re not at the point where more vendors will dive in any faster or abandon it. It’s the same-old, same-old.

Graph analytics

Graph analytics will continue to worm its way into analytics platforms, but this won’t be the year it breaks out and takes over. Most vendors are still using traditional relational databases … object databases are still a stretch.

Augmented analytics

The definition of augmented is a system that can learn from human feedback and provide better insights and/or recommendations over time. While we do have good machine learning technology that can learn from human interaction and optimize (work)flows, when it comes to analytics, good insights comes from identifying the right data to present to the user and, in particular, data that extends beyond organizational data such as current market rates, supplier risk data, product performance data, etc.

Until we have analytics platforms that are tightly integrated with the right market and external data, and machine learning that learns not just from user workflows on internal data, but external data and human decisions based on that external data, we’re not going to have much in the way of useful augmented analytics in spend analysis platforms. The few exceptions in the next few years will be those analytics vendors that live inside consultancies that do category management, GPO sourcing, and similar services that collect meaningful market data on categories and savings percentages to help customers do relevant opportunity analysis.

Predictive analytics

As with natural language processing, predictive analytics will continue to be the same-old same-old predictions based on traditional trend analysis. There won’t be much ground-breaking here as only the vendors that are working on neural networks, deep learning, and other AI technologies will make any advancements — but the majority of these vendors are not (spend) analytics vendors

Data automation

RPA is picking up, but like in-memory processing and semantic technology, it’s not going to all-of-a-sudden become mainstream, especially in analytics. Especially since it’s not just automating input and out-of-the-box reports that is useful, but automating processes that provide insight. And, as per our discussion of augmented analytics, insight requires external data integrated with internal data in meaningful trends.

No-code analytics

Cue the woody woodpecker laugh track please! Because true analytics is anything but low-code. It’s lots and lots and lots of code. Hundreds and Thousands and Hundreds of Thousands of lines of codes. Maybe the UI makes it easy to build reports and extract insights with point-and-click and drag-and-drop and allows an average user to do it without scripting, but the analytics provider will be writing even more code than you know to make that happen.

Come back tomorrow as we tackle the next ten.

2020 Is Here. Will we ever Get 20/20 Vision into our Technology Providers?

AI. Virtual Reality. Augmented Intelligence. Big Data. Autonomous Software. The Futurists are in a prediction frenzy and throwing around these words not only like everyone understands them but every provider has them.

Very few providers actually have these technologies, but the sad reality is that very few providers aren’t claiming to have them. obviously, this is a problem. A big problem. Because the number of providers claiming to have these technologies and actually have them is only a small percentage — making it hard for anyone to see the big picture.

But we need to — and we need to see it clearly. Very clearly — because, as we have indicated many times, there is a lot more applied indirection out there than artificial intelligence. Similarly, it’s not really virtual reality unless its immersive, and while a lot of gamers might immerse all of their focus into their games, most are not truly immersive. It’s not augmented intelligence unless the application intelligently provides a recommendation, and associated process, that is at least as good as you would come up with and, preferably, as good as a human expert. It’s not even close to being Big Data unless the application is capable of processing and working with more data than can fit in memory on an average server. (Big Data is a moving target — what was big in 2000 is small today.) And it’s not autonomous unless the application is capable of doing processes that would normally take a human to do on its own with the exception of truly exceptional situations (as it should be able to handle most exceptions, especially if the exception was handled before).

The reality is that while software is going to get more automated, and usability is going to continue to improve, we’re not going to see real AI for a while. The “Big Data” that most applications will be capable of handling will continue to be limited to user machine / browser memory. Virtual Reality is a ways off. Augmented Reality will continue to advance, but primarily in gaming.

But depending on what you are looking for, you likely don’t need AI, don’t need “big data”, don’t need autonomous, and definitely don’t need virtual reality. You just need a system that allows you, with some simple RPA, to digitize paper processes, automate common processes, and improve productivity.

And it would be nice if we could get some real 20/20 vision into what vendors actually have and what you really need.

But that might still be a pipe dream.

Twas the Night Before Auction

Originally published December 24, 2009.

Twas the night before Auction, when all through the plant,
Not a creature was stirring, not even an ant.
The bid sheets were placed by the display with care,
In hopes that a new award soon would be theirs.

The workers were waiting for news from afar,
While visions of bonuses danced in the stars.
The boss with his black tie, and I with my Dior,
Had just readied our guns for a long bidding war

When out on the lawn there arose such a clatter,
I sprang from the desk to see what was the matter.
Away to the window I flew like a flash,
Tore open the shutters and threw up the sash.

The moon on the breast of the new-fallen snow
Gave the lustre of mid-day to objects below.
When, what to my wondering eyes should appear,
But a miniature sleigh, and eight tinny reindeer.

With a little old driver, so lively and quick,
I knew in a moment it must be St. Nick.
More rapid than eagles his coursers they came,
And he whistled, and shouted, and called them by name!

“Now Dasher! now, Dancer! now, Prancer and Vixen!
On, Comet! On, Cupid!, on Donner and Blitzen!
To the top of the roof! to the top of the wall!
Now dash away! Dash away! Dash away all!

As dry leaves that before the wild hurricane fly,
When they meet with an obstacle, mount to the sky.
So up to the house-top the coursers they flew,
With the sleigh full of Goods, and St. Nicholas too.

And then, in a twinkling, I heard on the roof
The prancing and pawing of each little hoof.
As I drew in my head, and was turning around,
Down the smoke-stack St. Nicholas came with a bound.

He was dressed all in fur, from his head to his foot,
And his clothes were all tarnished with ashes and soot.
A bundle of Goods he had flung on his back,
And he looked like a peddler, just opening his pack.

His eyes-how they twinkled! his dimples how merry!
His cheeks were like roses, his nose like a cherry!
His droll little mouth was drawn up like a bow,
And the beard of his chin was as white as the snow.

The stump of a pipe he held tight in his teeth,
And the smoke it encircled his head like a wreath.
He had a broad face and a little round belly,
That shook when he laughed, like a bowlful of jelly!

He was chubby and plump, a right jolly old elf,
And I laughed when I saw him, in spite of myself!
A wink of his eye and a twist of his head,
Soon gave me to know I had nothing to dread.

He spoke not a word, but went straight to his work,
Restocking the warehouse, then turned with a jerk.
And laying his finger aside of his nose,
And giving a nod, up the chimney he rose!

He sprang to his sleigh, to his team gave a whistle,
And away they all flew like the down of a thistle.
But I heard him exclaim, ‘ere he drove out of sight,
Happy Auction to all, and to all a good-night!”

Have You Solved Your Supply Chain Water Problem?

While energy production and availability is likely to be a problem in the decade to come, most experts believe that non-renewable energy production will peak between 2030 and 2035 and then trail off as hydro, wind, solar, geothermal and other renewable methods take over and begin to meet energy demands for decades to come.

However, the situation is not the same when it comes to demand for clean, drinkable, usable water. Global water demand is expected to increase from about 4,600 km3 per year to 6,000 lm3 per year. As a result, by 2050, the projection from the United Nations World Water Development Report is that nearly 6 Billion people will suffer form clean water scarcity by 2050. That’s almost 6/7ths of the current population. Think about that for a minute. BY 2050 ONLY 1 IN 7 PEOPLE WILL HAVE ENOUGH CLEAR, DRINKABLE, USABLE WATER FOR THEIR NEEDS.

Now think about this. WHAT IMPACT IS THAT GOING TO HAVE ON YOUR SUPPLY CHAIN? Regardless of your industry huge. There isn’t a single industry that doesn’t require water. Agriculture, Apparel, Electronics, Forestry, Manufacturing and so on all require huge amounts of water. And Apparel, for example wasn’t a typo – it takes 7,600 litres of water to make one pair of jeans. And Agriculture, Electronics, and Forestry all take considerably more water than you think. That cup of coffee you’re drinking now required 140 litres of water. The smart phone you might be reading this post on, 900 to 1,000 litres on average. And that quarter pound of bacon you’re eating, 526 litres of water.

And your workers need water too. And right now even first world countries are experiencing water issues. Thanks to aging (lead-based) infrastructure, there are a number of places in North America where the population (including school children) do not have clean drinking water. And thanks to drought and lack of infrastructure, water shortages are becoming more and more common. Just this year alone saw major problems in (Cape Town) South Africa and (Chennai) India.
In fact, the World Resources Institute (WRI) identifies seventeen (17) countries, and 1.7 billion people (or 1 in 5 people on the planet), as experiencing “extremely high” level of baseline water stress (as per this graphic from the WRI). (Most are in the Middle East or Asia, or Africa.) Moreover, another 27 countries are experiencing high baseline water stress and within a few years we could be seeing this list (and population base) double. Plus, while the US ranks well overall, the state of New Mexico has “extremely high” water stress (similar to the UAE that is 10th on the list) and projections are that within a few decades the southern Great Plains Southwest Rocky Mountain States, and California will also be under extremely high water stress. (And if you go five decades into the future, about half of the US.)

Without an immediate reduction in water use, improvements in wastewater recycling and reuse, and overall process efficiency across industry, water scarcity and stress will soon hit everyone, and every supply chain, hard and put entire companies, countries, and global supply chains at risk.

So, Have YOU Solved Your Supply Chain Water Problem?

Another Decade Has Passed. How Are You Doing on the 10 Rs?

Ten years ago (yes, this blog has been around for a long time, especially in internet years), we picked up on a great article by SupplyChainBrain on Ten Steps to Green Packaging in the CPG Industry which was a great article not just because it demonstrated just how many ways there were to make packaging green, but because it gave us so many ideas on how to make our entire supply chain green.

In brief, the ten steps were:

  1. Replenish
    Purchase raw materials from suppliers who employ sustainable resource management practices.
  2. Re-use (Re-explore)
    Use recyclable material.
  3. Reduce
    Use ergonomic design and optimization to minimize the use, and size, of packaging material.
  4. Replace
    Replace hazardous and harmful substances with eco-friendly materials.
  5. Reconsider
    Use renewable materials whenever possible.
  6. Review
    Inspect, monitor, and control waste in the packaging process.
  7. Recall
    Immediately recall harmful packaging and put processes in place end harmful packaging.
  8. Redeem
    Collaborate with retailers and collect reusable and recyclable packaging materials.
  9. Reinforce
    Set up a Centre of Excellence (COE) to disseminate environmental best practices.
  10. Register
    Sign up for a carbon reduction commitment initiative and follow-through.

And they are globally applicable.

  1. Replenish
    Regardless of what you are buying, you want a supplier who is focussed on sustainability.
  2. Re-use (Re-explore)
    Modern science has advanced us to a point where most materials are reusable and recoverable. You should be working to get to 90% re-used/recycled/replenished content within a decade.
  3. Reduce
    Modern structural analytic techniques (especially with the low-cost availability of high-powered computing, low-power cores, and the ability to host data centers in naturally cooled environments) allow for the usage of much less material than before, without compromising any structural integrity
  4. Replace
    There is no need for hazardous materials in the majority of products on the market today. Science has delivered us alternatives.
  5. Reconsider
    Non-renewable materials are becoming limited. It’s not just a cost or green consideration anymore, it’s becoming a necessity.
  6. Review
    Waste should be minimized inside your organization and eliminated in your supply chain. Waste to you can be raw material to someone else. Food stuffs don’t meet your level of quality for human consumption? Might more than surpass the level of quality for animal consumption and, if not, there’s always bio-mass energy production. Metal scraps? Straight to smelting and recycling. And so on. Your waste can always be someone else’s inputs if you are smart about your process.
  7. Recall
    Whatever you are creating should be benefiting the consumer, not harming them. If you screw up, recall the product, immediately fix or recycle it, and improve your processes so it doesn’t happen again. (Don’t reprimand the workers, but fire the pointy haired idiot who requested it or was responsible for guiding the workers. And yes, SI still disdains the average Master of Bullshit Administration.)
  8. Redeem
    Make all of your packaging reusable and get it back. (Considering how many empty miles exist in the trucking industry, this is not a big deal or big cost if properly planned. Coupa Sourcing Optimization and Jaggaer One Advanced Sourcing Optimization in particular have models customized for transportation and reverse transportation. USE THEM!)
  9. Reinforce
    … and mandate! Set up the COE, make an executive mandate that policies must be followed, and green your operation.
  10. Register
    Make a public commitment to carbon reduction, waste reduction, and energy usage reduction, measure annually, publicly report, and follow-through. (And don’t just buy carbon credits or carbon offsets. Don’t make your problem someone else’s.)

Sustainability isn’t hard anymore … and the organizations that start now will be the ones that will be around in the decades ahead.