Come and get it!!

My pre-launch book sale campaign is underway!! Here’s a taste of what’s up for sale!

What’s Your Problem

Solving world hunger or fending off COVID-19 are a different problem space than what’s for dinner. It’s pretty easy to follow that distinction; however, it’s important now because we do have much more information and many more tools for tackling the former. Twenty years ago, solving world hunger seemed to be a matter of getting the best minds together with a sufficient checkbook. The matrix of issues and identities though is much more complicated than just ideas and the finances to back them. Although it does take funds to make things happen, the funds don’t solve the problem of itself.

Better understanding the dimensions of problems determines solution space as well as tools and methods for addressing its aspects and effects – because it may or may not be solvable.

“We are doomed to choose” – Isaiah Berlin

The world and its problems are becoming more complex.

The world and its problems are becoming much more intricately entwined.

We all need to make better data decisions because subsequently the world we live in is subject to greater reactions and effects.

We gotta figure this out. We can’t just keep pushing more and more words and numbers into documents without providing a way to comprehend them better.

I may vomit. This part of writing a book is the hardest – actually trying to get people to buy it. For those who’ve been following my book posts on LinkedIn and Facebook, this is the pre-sale campaign I’ve been hinting at for the past few months. Now that I’m in the revision phases of my manuscript and have New Degree Press as my publisher, I now get to worry about funding the actual publication process!

If you’d like to help me on my publishing journey, please pre-order a signed copy by clicking here!

Does Amazon Know What’s In Your Bank Account?

I watched a marketing webinar last night.

It was free. Typical of that genre, the point was to give away some information but sell you on a product or service that will give the rest of the picture. That seems a good trade off considering their time and effort put into the creation of the webinar. In return, learning from their experience made my time and effort watching it worthwhile. That’s a fair market return.

Amazon knows what’s in your bank account 🙂

The presentation wasn’t interactive other than the pop up order form at the end. So it was basically a recording, once produced it can be repeated with only the additional broadcast cost. From what I can tell, it is played twice a day, most likely for a “limited time.” The product being sold is more of the same information. More extensive knowledge and examples are digitally boxed for download. Some level of customer service is implied.

For me, it potentially could be a good product.
The topic – Marketing – isn’t my strong suit, so I make an easy, albeit skeptical, target. I might have bought it … but the price was a bit more than what I would spend. I have a technique for shopping where if I like something enough to want to purchase it, I mentally calculate what it is worth – to me. The process includes thinking about my bank balance, the relative “need” for the product or service, how long it will last, will it pay me in return, alternative resources that may or may not do the same things for more/less time and/or money. The final consideration is Quality of Life – will I be happier/faster/slower/better for having purchased it. Whether a house or a pair of shoes, I scramble all that data to run it through the gonkulator that is my brain and contrive a cost I am willing to pay.

Then I look at the price tag.

Marketing & Economics 101

Ah price point – where supply and demand meet the wealth of nations. Wrigley made a fortune from penny sticks of gum. Today’s $.99 ebook downloaded a million times is close to a million dollars (depending more on its one time set up cost.) On the other end of the spectrum, the multi-million dollar yacht salesman needs 3 to hit the annual sales target. One puts him out of business and five will hold over for another year.

Adam Smith touted that without the tethers of government interference, private monopolies, lobbyists or “privileged” entities, the free market wields the perfect price where supply and demand meet. When banana crops fail, the price of those that make it to market goes up enough to meet whomever is willing to pay for more expensive bananas.

I’m sure he would have been spellbound by the much more elaborate threading of airline flight tickets. An incredibly (I don’t use that word lightly) sophisticated algorithm cultures those seat prices like a mother hen, sub-minute by sub-minute searching databases and optimizing just how much the seat should be priced. Would Mr Smith’s contemplations and calculations hold under the intense smoothing of the pricing integral? His theories have been smeared all of the earth with both creamy taste and reputed disgust.

He would’ve been further floored with the near-zero production costs of electronic products and services, although I’m sure he’d have much to say on it. These are the elements of economic evolution that test free market capability in a global economy.

Big Data Price Point

Of course, you probably wouldn’t be here reading this unless you’re more interested in Big Data than my spending habits. This is hardly business continuing education credit either.

But these “small” data examples are all market driven. The future – the Big Data Price Point – is the price will shift according to your ability as much as willingness to pay. That means the webinar product that I didn’t want to pay $497 would have been further discounted to the $247 that would have made me uncomfortable but ready to use my credit card.

How is this possible? Supply and Demand. For one, it is a product with almost zero production cost. Unlike the widget which requires materials, labor, manufacturing, storage, and distribution. The digital warehouse doesn’t need a water supply or a janitor. The one stored copy (and backup) replicates instantly. There is a set-up cost; the knowledge must be conveyed creatively. People are needed to develop the product which must be marketed and maintained. The elegance of the digital effort minimizes the cost as the theoretical sales count grows infinitely. This is not a scenario Mr Smith would have anticipated.

As for demand, Big Data Price Point (PP) kicks in by knowing YOU along the same lines I mentioned that I use to make a purchase. Big Data PP would figure out if I have enough cash or credit for the sale. Big Data PP knows if I spend money on these types of products already. Big Data PP knows whether this fit my spending lifestyle or if it is a reach. Big Data PP determines if I can or cannot deduct it as a business expense. Even more so, Big Data PP calculates whether I need deductions at this point, against how many deductions I have already accrued in my fiscal year.

Big Data PP will charge me a different amount than the next sale to a different customer. An entity with a bigger bankroll may get charged more, or they may be offered a morphed package of sales for services I cannot afford: X downloads, unlimited downloads, additional webinars or custom services.

That’s not fair!

The same product is sold for different prices – because of how much I make? Well … yah. Want to write your Congressman or call your attorney? Think again.

Buy now!!

Google (of course) is already doing it

And there are others. As early as 2000, Amazon was “price testing” multiple prices for the same DVD. They took some heat when consumers found out, so they dropped the practice (as well as the price.) In 2005, dynamic pricing came into play again when a University of Pennsylvania study noticed prices differed according browsing history – someone who had shopped competitors would get the more competitive price. In 2012, the Wall Street Journal reported how Orbitz ‘s offerings priced up to 30% higher for Mac users – Mac users have a higher income average.

Although the level of complexity may be surprising, we have become accustomed to cookies and their impact on our experience. Most don’t appreciate the impact though on the bottom line in the shopping cart.. The Atlantic explains:

the immense data trail you leave behind whenever you place something in your online shopping cart or swipe your rewards card at a store register, top economists and data scientists capable of turning this information into useful price strategies, and what one tech economist calls “the ability to experiment on a scale that’s unparalleled in the history of economics.” –

The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be, one study found. For shoppers, that means price—not the one offered to you right now, but the one offered to you 20 minutes from now, or the one offered to me, or to your neighbor—may become an increasingly unknowable thing.

https://www.theatlantic.com/magazine/archive/2017/05/how-online-shopping-makes-suckers-of-us-all/521448/

I’m going to need to see your zip code, ma’am

Physical or virtual, cost of living has always been tied to location. You can expect prices to be higher in New York City vice Greenbow, Alabama. I’ve known friends who won’t shop the grocery stores near their house because they are reputed to have higher prices. Then there’s the contra-pricing. I live in a resort area and to help out those of us who “suffer” through the throngs of vacationers to live the other nine months in peace, there is “locals only” pricing. That includes parking, some attractions and often secret pricing whispered over the phone ordering pizza or with the cashier for lattes.

Around the World

All the way back in the physical world, in Kenya most shopping is done in the very personal, one on one basis. Outside the upscale Village Market shopping mall in Nairobi, the Masai Market meets weekly in an open lot across the street. Advertised as an artisan fair of sorts, the goods are nevertheless likely to be found in a variety of souvenir shops anywhere in the country. It’s the experience though, another country’s version of selling what makes them unique.

I was excited. I was looking to find perhaps practical items to bring back home to share this lovely country’s culture. I was accompanied by a local. Although well-practiced at haggling from many countries around the world, I would need his native language and color to get a decent price. Otherwise, I would get the “mzungu” price, their understandable upcharge for an American, which I deserve. I didn’t expect to pay the same price as locals but I couldn’t afford the inflated price. The average monthly wage in Kenya is $76. Pricing is relative – to my location, my income, my nationality, my experience (traveler, savvy, job, education, common sense). I should pay more but not more than what the gonkulator tells me.

In the cloud

Where you live is already calculated in your costs and the virtual world does not provide escape any more than it does anonymity. My daughter realized a typical price difference phenomena when she went to college. The prices she knew from shopping online at home increased noticeably on her laptop while in Washington DC. She text a friend back home to shop simultaneously and compare. She was shocked to find the results. When she tried to change the zip code for delivery purposes – hoping to trick the system – the price refused to budge. Amazon knew where she was.

Final Morph

So pricing in the virtual world has not gone into our personal pocket books yet (that we know.) The online market does use digital information such as browsing history and location to triangulate your willingness to pay a certain price. This is still within the Small Data genre of capability, utilizing mean and median sources.

Big Data Price Point though – and I believe it will – knows YOUR personal bottom line. This is not a random variable calculated through the local and not so location population supply and demand. Big Data Price Point knows exactly what price to set for you from all your transaction history in stores and online, your taxes, your job, your household status, and much more.

Is that scary? Perhaps. But it is already very close to possible.

What would have happened with the Housing crash of the early 2000s? Big Data Price Point would have offered houses at rates that individuals could afford. Would it have curbed the domino effect or accelerated it that much more? Perhaps Big Data Price Point would have sensed the cumulative errors the isolated banks were unknowingly committing and eventually unwilling to admit. It’s a twist again for Mr Smith’s legacy to wrap around.

The world out there is waiting to sell you the next Best Thing and Big Data or not, marketing will continue to morph to find the magic price you are willing to pay. Big Data Price Point though will be oh-so intimately familiar with you and your money. In the end though, Big Data Price Point can only posture the question: will you buy?

The answer is still up to you.

Who is Betting $25 Million on the IoT? Who will be left behind?

Marc Andreessen is betting $25million

on the Internet of Things (IoT) becoming a big thing. His famous ability to forecast the evolution of emerging technology weaves a picture of sensors embedded in walls and doors and doorknobs. It makes today’s humble offerings – the likes of remote operation of your house security and appliances or fitness bands that connect to a mobile device and the cloud – seem kinda silly.

Marc Andreessen stretches out that belief a little further with his opinion that cell phones will become obsolete. In his perspective a single point of contact such as a cell phone is a limiting vision. He conceptualizes that screens will pervade many more surfaces so much that having to carry your own electronic portal seems a bit ridiculous. That’s an intriguing perspective from an expert on the subject. Goodness knows that a home phone or a pay phone seems quite antiquated today; even paying for a phone call is becoming upended. It’s becoming possible to speak to anyone in the world with an Internet connection and a choice of free apps.

So what happens to the countries …or continents … that are amassing mobile service by leaps and bounds but still lagging far behind? Are they going to be left even further behind …again? Will the technology gap drop those that can’t afford the latest and greatest?

Africa for example doesn’t have as many mobile devices per capita as the rest of the world, but they do use mobile phones in the din of the more remarkable remote locations. Their mobile technology isn’t so much smart as functionally adaptive, performing the most cursory and vital functions, such as currency, in more creative ways than more technologically adapted regions.

So how could it be possible this makes Africa a better candidate to take a technology leap than further advanced countries? Let’s looks at that.

In 2008 Intel reported on their self-developed “Technology Metabolism Index” – a map of what countries adapt technology relative to economics. What they found was that those that “have” don’t necessarily beat out the “have nots” to adopting new technology. The United States had a relatively low index in consideration of the greater resources, access and availability of new technology.

Some experts worry that a technology gap threatens to leave behind vast swaths of the world population too poor to afford new innovations, but Nafus work could be seen as evidence that more complex dynamics are at play than just disposable income levels. http://www.wired.com/2008/06/intel-anthropol/

Intel’s Dawn Nafus, a Cambridge PhD, conducted the study in order to better determine the potential of emerging markets versus mature markets. Her team found a couple of surprises. The first is a bit counter-intuitive.

Population size is actually a constraint on technology adoption, just the sheer number of connections between people seems to slow adoption.

The research also supported that foreign direct investment, or how much money foreign firms pour into a country’s economy, can actually constrain how fast technology is adopted. Again, it’s not what you’d think, when more money usually portends resources and capabilities.

Didn’t See It Coming

So let’s look at another technology adaptor story – Colombia. For years, Colombia was known for illegal drug activity that held it hostage. By investing in technology growth its economy has tripled from what it was 10 years ago and its middle class has increased 50%. The country’s remarkable make over has attracted US businesses, increasing import from the US to Colombia by nearly 400% since 2003. Google, Facebook and Microsoft have established presence in country, and of course, Starbucks is there as well to fuel the creativity.

The advances are attributed in great part to fostering entrepreneurship in tech hubs such Ruta N in Medellin and Bogoda’s HubBOG, a 5-year-old tech campus. These hubs are a common theme worldwide.

Another reason entrepreneurs may want to take a strong look at Colombia is because it’s coming online quickly. The Colombia government is working to bring 63 percent of its population online by 2018, according to a report by Colombia Reports. It also boasts 69 percent smartphone adoption, according to the GSMA “Mobile Economy” report for Latin America. http://www.cnbc.com/2015/05/07/three-growing-start-up-cities-in-south-america.html

Big Data Africa Opportunity

According to the TMI index map, a case could be made for South America, Africa, Eastern Europe or the Far East to make Big Data a turn-around industry for any of these regions or countries within.

The availability of Big Data is an inevitable global factor that grows exponentially continuously. Because it is ubiquitous and open source, in Africa or any of the other emerging regions, Big Data is a tremendous opportunity.

Big Data is already deployed in multiple directions: competitive industry, government interest and non-government action. Where there is a will, there is a Big Data way. Training and deployment of all levels of Big Data tools are ubiquitous, given connectivity. Big Data has a relatively low cost of entry as well.

Africa already has a healthy network of these centers. The number of technology centers in Africa has increased dramatically over the past couple of years. More than half of African countries have a technology hub and leader South Africa has nineteen. According to World Bank, more than half of African nations have at least one tech hub, totaling around 90. In 2014, investment in tech hubs has gone from $12 to $26 million.

A wide variety of participants are needed to make Big Data work. The Big Data industry has an extreme shortage of data scientists and technicians. Even the occupation “data scientist” is actually not as straight forward as it would seem. This is opportunity to find a niche or create a new one.

Not Another NGO

The volume, velocity and variety of data accumulating are a worldwide phenomenon. Perhaps it is relative size, but every person in every country is creating more and more streams of information. In the same thread, every business creates and has access to tremendous amounts of data, but who uses it varies greatly. It’s not a necessity now for entity differentiation, but it is rapidly becoming so.

Those that don’t adapt to utilizing Big Data will get left behind – corporation or country. We are in a global economy ecology as well. In a post 9/11 world, we can’t leave others behind and hope for the best. We’ve got to create the rising tide to lift all boats. Bring on the Big Data.

World Bank Using UAVs for Disaster Risk Reduction in Tanzania

Data capture. This reblog has a fantastic video explaining the extreme detail of massive data collection. Low cost for high Return On Investment. This data set can be infused in a Data Lake to find signal in the noise such as relief for traffic and relief for flooding. The Data Lake can be tapped for more innovation as well.

The Elephant(s) In the Room – Just How Many Are There?

Counting Elephants – Using Big Data to Solve Big Problems, it’s as easy as 1-2-3

One … two … three … four hundred … five hundred thousand …. How many elephants can you count before it’s too many (or too much)?

Counting is one of the first skills learned as a child. Before addition and subtraction, the numerical building blocks of 1-2-3 are right there with the ABCs. Whether it’s your blessings or the dealer’s cards or your money, counting comes in handy. How many or how much is core to decision making. How much money or how many resources do I have? How many does the enemy have?

Counting to 10 or maybe 100 is easy, but as more needs to be counted, it becomes tedious and time intensive. The practice loses its return on energy. That’s where math and probability and statistics come in. Using what can be counted easily can be leveraged not only to count more but also to add value to the meaning of what is counted.

Enter the classic “what to wear” problem (poignant for math geeks).

Instead of laying out each combination and “counting” it, you know how many outfits you have. This simple example can be exploited in far greater combinations…to the nth degree.

But what if you don’t know how many shirts and pants you have?

Big Data Counting – The Next Generation

Counting is another excellent venue for exploring Big Data. Just as math saves hours of manipulating closet items, so Big Data can help with Big Problems, providing greater choices and better decision capability as a result.

Let’s look at a Big counting problem –

Just how many elephants are there in Africa? (and why does anyone care?)

A shocking increase in poaching has ripped down African elephant populations. In the past 10 years, the African elephant population has taken a dramatic hit with estimates that 12,000 plus a year have been slaughtered since 2006.

“The threat of local extinction feels very real. In October 2013, Elephants Without Borders flew a survey over a park where we had previously counted more than 2,000 elephants. We counted just 33 live elephants and 55 elephant carcasses. That is why this research is so important.”

Dr. Mike Chase, director and founder, Elephants Without Borders. http://www.elephantswithoutborders.org

Wildlife preservation is a delicate entente in the best of circumstances, but the lucrative draw of poaching in the myriad of African countries where they habitate has challenged several of its iconic epicenters. Lions, rhinos and elephants are the majestic leaders of a rich wildlife pyramid whose dramatic loss crushes the whole ecologic system, including the native peoples that live and exist off the balance.

Poaching itself is lucrative. The transit from Africa to Asia transforms ivory at $200 to over $2000. Because of international standards outlawing this black market material, poaching profits only illicit activity and most dangerously – terrorism.

Except in South Africa

South Africa “suffers” from too many elephants. Here the growing numbers continue to roam and forage as is their nature. That means knocking over even the most sturdy of trees and stripping them of the best digestible leaves. Just imagine an elephant walking through your yard or the neighborhood park taking down a couple trees that look tasty. Imagine what a herd of 20-30 can do. They just don’t stand still either. They keep on the move, journeying for miles in a day carving a pachyderm hurricane path.

In any amount, this is nature’s process, culling the forest for new vegetation. Their trails create natural fire breaks and they dig for water which other animals use. But where farm and urban sprawl encroach this roaming territory, it quickly becomes man versus animal. The number of touch points are growing too. The nature of elephants – their survival – is roaming. Their legendary memory too has them cross paths where man’s development has erased the past.

To attempt that delicate balance, game parks in South Africa have taken to birth control and water management methods in order to keep their numbers in check.

eNCA

So What Numbers Are We Talking?

Anyone who has tried to count children at a birthday party or getting all students back into a classroom after recess knows the challenges of counting live bodies. Counting crowds is actually a science. And Science isn’t about Knowing so much as Getting a Good Estimate. Here’s how they counted President Obama’s inauguration crowd.

https://www.youtube.com/watch?v=3AwMmEYLWt0

Although a several ton elephant is noticeably slower and harder to miss, expand the search over the wildernesses of half a continent dissected into several countries, some war torn, and accurate counting is hard to imagine. But someone is trying.

Not Just Throwing Money at the Issue

That effort is the Great Elephant Census, the largest pan Africa aerial survey since the 1970s, and it’s backed by one of the world’s smart guy-in-the-room icons – Microsoft co-founder Paul Allen. Not only does this count have deep pockets, it also has expert guidance.

ABOUT THE COUNT

The Great Elephant Census is applying a strategic, consistent approach to counting elephants in numerous countries in varying climate and terrain, with an integrated audit program in situ.

The Great Elephant Census is designed to provide accurate and up-to-date data about the number and distribution of African elephants by using standardized aerial surveys of tens of hundreds of thousands of square miles. Dozens of researchers flying in small planes will capture comprehensive observational data of elephants and elephant carcasses. Our standardized method of data collection, which is validated by an independent TAT advisor ensures all data is impartial and accurate.

It’s somewhat like counting the crowds for President Obama’s inauguration. Even with such meticulous effort though, most elephant accounting is predicated on “known” and “estimated” numbers.

But … Back to Big Data

So that’s how the experts are counting elephants. Let’s explore counting elephants instead with a Big Data lens. An elephant census doesn’t have to solely be tallying head counts, albeit a magnificent head with flowing ears and strong tusks. The count can be created through a variety of volumes of data that exists already and grows by the minute. In a Big Data Elephant Census, information is created by the community and also serves the communities in return.

Big Data Elephant Census begins with a data lake of information collected from the prevalent sources: cell phone usage, transactional data, weather, heat signature, game warden activity/reports, international shipping and markets, and of course, social media. Big Data ingests the volume, velocity and variety of the data to look for patterns that emerge. Like trying to count moving children, Big Data can exploit information that is too complex for “naked” human observation. Like picking outfits from the mathematically derived wardrobe, Big Data Elephant Census provides an answer to how many elephants as it elicits the holistic picture of what that means.

THE POINT OF COUNTING ELEPHANTS NOT TO KNOW HOW MANY ELEPHANTS THERE ARE. THE POINT OF COUNTING ELEPHANTS IS TO LEARN THE ELEPHANT POPULATION EFFECTS on OUR LIVES – DESIRABLE AND UNDESIRABLE.

The point of counting elephants is not just to know how many elephants there are; we want to know all the factors that evolve in the elephant environment. How does the diverse animal and vegetation habitat ebb and flow with the tramping of elephant feet? How are indigenous and foreign humans influencing and being influenced by the elephant footprint? (har har) How are poaching and anti-poaching efforts impacting the community as well as the elephants? How are farming and native livelihoods affecting and being affected? What other passive economic factors, weather, and politics shift accordingly?

CONSERVATION IS NOT A STASIS …

So let’s stop trying to capture a picture and instead focus on what the flow is. Pulling Big Data elephant count from a volume, velocity and variety of data sources articulates how the system (man and animal) manifests. Instead of trying to chase the right amount of elephants, Big Data Elephant Counts observe the evolving energy to find the signals in the noise. Gentle shifts or environmental shocks are recorded in situ with all the elements and players. Big Data Elephant Counting is less reactive and is both predictive as well as evolving – like the trails we all carve through the forest.