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

In our last post we started reviewing 20 analytics predictions being peddled by the major analytics futurists and analytics sites. Why? Because while overly optimistic futurist authors rarely get it right, their predictions do point out two things. What should be done — and isn’t getting done — and where the space needs to go.

And even though 19 of these anti-predictions won’t (fully) come to pass this year, we started reviewing them one by one to give you a reality and indicate what is likely coming sooner than later, and what is still a pipe dream. Most of the predictions we reviewed yesterday were those that fell into the “aren’t happening” or “aren’t really happening at all” (because they are more of the same old, same old) buckets, but today we get to some that will start to materialize and the one, yes one, that is 100% true — and that you need to be fully aware of.

So settle in and let’s finish this.

AI becomes more mainstream

Well, acceptance of AI will continue to become more mainstream, but considering that most “AI” providers are actually providers of “Artificial Indirection” and have no AI at all, not even at the level of “Assisted Intelligence”. Most providers of “AI” are just providers of RPA (robotic process automation) at-best, and a configurable rules-engine at worst.

Multi-hybrid

A few vendors are offering multi-hybrid analytics solutions, and a few more will, but there will be nothing new. It will be one solution for integrated in-platform analytics, another for do-it-yourself analytics, and possibly an in-house developed third for database management and cube construction. But there’s going to be no significant changes here — most practitioners are going to use what their vendors give them.

Analytics will become usable by business analysts

Well, this one is half true. With recent advances in user interfaces and usability, it will become more usable … but … only to the better half of the business analysts … and … only with training. And this is where this particular prediction fails. Training has been high on the priority list for a decade, and it’s also been high on the “cut when budgets need trimming” list for a decade as well. There will be little to no training as per the norm, so only the most dedicated will self-learn and use it.

Data governance takes centre stage

This prediction is likely to come sooner than you might think, but not in 2020. Until there is a big cost associated with the lack of data governance, like training, it’s going to remain high on the priority list but not going to get centre stage. This will only change when lack of governance risks a huge fine or a large organization loses a major court case with a large judgement that was the result of lack of governance (which resulted in data exposure) which could have happened to any governance.

AI ethics standards will emerge

We all wish this will happen, but as with data governance, until a large organization loses a discriminatory court case as a result of an AI decision, and the court holds the organization responsible for that AI decision, no one is going to put any real effort, beyond lip service, into AI ethics. At least from a vendor perspective. A few lawyers hungry to make a name for themselves might, but that’s about it.

Analytics will hit the C-Suite

Re-set the woody woodpecker laugh track. If the average business analyst is not going to get much more involved with analytics, then you can bet the average C-Suite executive is not going to get much more involved either. They might get better reports and dashboards, but that’s it.

Intelligent assistants that connect the dots will become more pervasive

This is another half-truth. “Intelligent assistants” that allow a user to interact with the application in natural language, and especially English, will continue to infiltrate S2P platforms, but as to connecting-the-dots … not likely. That will require true embedded machine learning technology, and that’s still far away for the average provider.

Open source is going down the drain thanks to cloud platforms

This is yet another half-truth. While it is true that as more and more providers lock into a cloud platform (such as Azure, Microsoft, and Google) they will lock into whatever analytics are provided in the platform, this is not going to stop open source efforts — although uptake may trickle off for a while.

Effective implementation will continue to be a challenge

This is mostly true. Effective implementation will continue to be a challenge for the majority of organizations, and only a few best-of-breed providers will see the challenge of effective implementations decrease. As data continues to proliferate, especially considering the average quality of data, analytics will continue to get more challenging on the whole.

And now, finally, the one prediction the doctor found that is 100% accurate.

Companies will continue failing analytics & AI transformations

This is absolutely true. Considering that analytics requires good data and AI requires lots of good data, good algorithms, and experts to guide the algorithms, and most companies have poor data, poorer algorithms, and a dearth of experts … and often rely on vendors who peddle applied indirection, the doctor expects a big uptick in failures until the space educates themselves on what AI truly is, what the levels are, what is actually out there, and who is actually offering it.

For details on what the levels are, and what is coming, keep your eyes on SI and SM, and if your organization has been smart enough to subscribe, check out the doctor‘s pieces over on Spend Matters Pro on AI in Supplier Discovery, Sourcing, Optimization, Procurement, and Supplier Management.

AI in Procurement: [Spend Matters Pro subscription required]
Today Part I,
Today Part II
Tomorrow Part I,
Tomorrow Part II,
Tomorrow Part III
The Day After

AI in Sourcing: [Spend Matters Pro subscription required]
Today
Tomorrow Part I,
Tomorrow Part II
The Day After

AI in Sourcing Optimization: [Spend Matters Pro subscription required]
Today
Tomorrow
The Day After Part I,
The Day After Part II

AI in Supplier Discovery: [Spend Matters Pro subscription required]
Today
Tomorrow
The Day After

AI in Supplier Management: [Spend Matters Pro subscription required]
Today Part I,
Today Part II
Tomorrow Part I,
Tomorrow Part II
The Day After

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?