Here’s What You Really Need To Know About Blockchain

In 1970, a scientist at IBM Research named Edgar F. Codd make a remarkable discovery that would truly change the world. Though few realized it at the time, including IBM, which neglected to commercialize it. It was called the relational model for the database and it would spawn an entire industry.

Yet while today few have heard of relational databases, everybody seems to be talking about blockchain. Much like Codd’s idea nearly a half century ago, blockchain represents the opportunity to create a new data infrastructure, which in turn, is likely to help power business for another half century.

Still — and very contrary to the current hype — few of us will ever work with a blockchain or even know it is there. The real revolution will come not from the technology itself, but from its secondary effects in the form of new business models. To leverage these though, you will first need to understand how Edgar Codd created the data economy in the first place.

How Relational Databases Changed the World

Imagine taking a trip back to 1980. Ronald Reagan was just elected President, and Terry Bradshaw led the Pittsburgh Steelers to yet another Super Bowl. Just a year before, Larry Ellison and two friends launched the first commercial product based on Codd’s ideas. Two years later, they would change the company’s name to Oracle.

Now imagine trying to explain to someone in 1980 what they would use a relational database for. Back then, few people used computers, which were primarily used for back office tasks and heavy computational jobs like scientific research. Very little that relational databases did was relevant to how people worked back then.

What made relational databases important is how they changed how we could manage data. They made data fungible. Classical or “flat file” databases worked very much like an Excel spreadsheet. They stored data in a columns and rows which lacked flexibility. You really needed to know how the database was set up to find the information you needed. Anybody who has tried to understand someone else’s spreadsheet knows what that’s like.

With relational databases, however, all you need to know is the query language and you can extract what you need from any database, no matter who set it up. That’s why today, we can hop on a system like the Internet and pull data from just about anywhere we want. It’s what made the information age possible.

Why Blockchain Matters

Relational databases were designed for centralized computing. Data was stored in a mainframe and we would use a terminal — and later a PC — to get information out. For example, executives use ERP software to pull data from far-flung operations and manage business processes more effectively. Marketers access research databases to understand consumers. Salespeople leverage CRM systems to service their customers.

Today, however, computing is no longer centralized, but radically decentralized. We carry smartphones in our pockets that are more powerful than what would have been considered a supercomputer back when relational databases were invented. We use those devices not only to retrieve information, but also to send it to centralized databases, often without knowing we’re doing it.

That creates an information bottleneck that is often insecure for a number of reasons. First, while most commercial databases are encrypted, data needs to be unencrypted for us to use it, which leads to problems like the one with Facebook and Cambridge Analytica. Data is also unencrypted at the source, so firms can access our data and store it without us having any control over it.

The most salient aspect of blockchain is that it functions as a distributed database. Unlike relational databases that house data in one location, blockchain distributes data everywhere at once in a secure form. So we can track data wherever it goes, what it’s used for and see who alters it in any way. That will create a radically more transparent information economy.

What a Killer Blockchain App Will Look Like

In a recent conversation I had with Bernie Meyerson, IBM’s Chief Innovation Officer, I asked him what he was most excited about. Thinking he would talk about the Watson program or a futuristic research project, I was somewhat surprised that the first thing he mentioned was his company’s joint venture with Maersk to develop a blockchain infrastructure for global trade.

With everything going on at IBM, from artificial intelligence to developing new computing architectures like quantum computing and neuromorphic chips, shipping seemed a bit low brow to me. Nevertheless, once I started digging into the numbers I could begin to see what he meant. Blockchain really can have an extraordinary impact on global trade.

Consider the fact that a 2013 study by the World Economic Forum found that reducing back-office friction in international trade could increase GDP by nearly 5% and commerce by 15%. Global GDP amounts to about $80 trillion, which means you’re talking about a $4 trillion potential impact. If even a fraction of that pans out it’s huge!

The thing is, nobody is going to buy a product and say, “Wow! This is 5% cheaper thanks to blockchain!” The truth is that no one will ever see it. Blockchain, much like the relational databases that came before it, is technology infrastructure. It’s basically like paved roads were to cars — an absolutely essential enabling technology, but not a “killer app.”

Where to Find the Next Big Thing

Over the next decade, we’ll see the impact of blockchain unfold, but it will look a lot more like the IBM-Maersk joint venture and Oracle than the next Google or Facebook. If you don’t work with a relational database now, you probably won’t have much to do with blockchain in the future.

Still, that doesn’t make the impact any less real or exciting. Much like the Internet distributed computing, blockchain will distribute secure data and that is likely to radically increase transparency and security while reducing costs. “Disintermediation” is a term we can expect to hear a lot of in the future.

For example, Hu-manity.co is a new startup that plans to give patients more control over their health data. Today, when we sign a consent form for our data to be used for research, we essential give it away. However, with blockchain, we will be able to track it, decide for ourselves how we want our data to be used and even be reimbursed for it.

So if you want to know how to profit from blockchain, start looking for information bottlenecks, like global shipping or medical data. Eliminating those bottlenecks is how blockchain will truly change the world.

Source: digitaltonto.com

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The future is ear: Why “hearables” are finally tech’s next big thing

The explosive growth of their AI voice assistants has Google, Apple, and Amazon racing to put your entire smartphone in an earpiece.

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In October 2016, an impressive group of tech industry royalty took the time to get a demonstration of a product from a startup called Doppler Labs. Microsoft cofounder Bill Gates and CEO Satya Nadella each got one, as did Apple Internet chief Eddy Cue and Jimmy Iovine, head of Apple’s Beats headphone group. So did C-suite executives from Amazon, Facebook, Google, and Tencent.

Donning pre-production versions of Doppler’s Here One wireless earbuds, they experienced the device’s ability to cancel out unwanted background noise, amplify the voice of a particular person in the room, and even converse with people speaking in another language. About a half-second after a Doppler staffer asked a question in Spanish, the wearer heard a computerized translation back into English.

At least two of the companies made informal acquisition bids, but none offered a high-enough price to convince Doppler to give up its dreams of launching a momentous new product. Sales failed to take off, and a year later the company was shuttered. But that’s not the end of the story. Within weeks of its closing, more than half of Doppler’s top technologists were working for the tech giants.

Amazon, Apple, and Google each have high-priority projects to pick up where Doppler left off. All three are working on products that combine the utility of the hearing aid with the entertainment value of a pair of high-end headphones, and potentially much more, say sources. Since all three have announced plans to get into healthcare, they could easily add fitness and health monitoring sensors for everything from counting steps to measuring oxygen saturation. And while it may take years to happen, none want to be left behind should it become possible to create a general purpose, in-ear computer that allows consumers to leave their phone in the desk drawer.

“Ultimately, the idea is to steal time from the smartphone,” says Gints Klimanis, Doppler’s former head of audio engineering. “The smartphone will probably never go away completely, but the combination of voice commands and hearing could become the primary interface for anything spontaneous.”

Spokespeople for Amazon, Apple, Google, and Microsoft all declined to comment for this article.

Why Hearables, Why Now?

For half-a-decade, Doppler and other startups have been trying—and failing—to come up with a “hearable” with the combination of sound quality, battery life and cool factor to become a mass market hit. So. why the sudden interest from the big guys? Because personal voice assistants such as Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, have suddenly emerged as the biggest interface revolution since the iPhone popularized the touchscreen.

Our desire to use technology without the hassle of touching it has made smart speakers the fastest growing new hardware market in years, says Strategy Analytics’ analyst Cliff Raskind. By 2023, 63 percent of U.S. homes will have a device like the Amazon Echo or Google Home, up from .03 percent in 2014 and 16 percent in 2017.

By then, Americans will speak rather than type more than half of their Google search queries, predicts Comscore. The market for ads delivered in response to voice queries will be $12 billion, according to Juniper Research. And these predictions don’t even contemplate a future when consumers have computers in their ears for more of their waking hours, providing tech giants with even more data on their movements and desires—not to mention a channel into their brains that makes shopping as frictionless as saying “Alexa, buy (fill in blank).”

The Future is Ear

“There’s much more that tech companies can do with ears than amplify music and make phone calls,” says Satjiv Chahil, a former Apple marketing executive who has advised hearing aid maker Starkey Hearing Technologies in recent years. “It’s about allowing your virtual assistant to whisper in customers’ ears throughout the day, while also enhancing their health and well-being.”

Your ears have some enormously valuable properties. They are located just inches from your mouth, so they can understand your utterances far better than smart speakers across the room. Unlike your eyes, your ears are at work even when you are asleep, and they are our ultimate multi-taskers. Thousands die every year trying to text while they drive, but most people have no problem driving safely while talking or dictating messages–even if music is playing and children are chatting in the background. Ears are not in the front of your face, so it may be easier for the Jony Ive’s of the future to come up with fashionable or even invisible designs for the ear than for the eye. Remember Google Glasses?

Those are just the obvious advantages. With the right sensors and processing on board, a hearable can tell if your head is pointed toward a store shelf in front of your face or at a billboard down the road. Add in a heart-rate monitor to measure stress and an electroencephalogram sensor to analyze spatial brain activity, and it could know what you are thinking about to some degree—say, how much of your attention is being paid to the sound of footsteps coming up behind you, says Poppy Crum, chief scientist at Dolby Laboratories.

Yes, AI-enhanced hearables in the future will be able to understand more than the words we speak. A Cambridge, UK-based startup called Audio Analytic is already licensing the ability for a device to recognize the sound of a window breaking or a baby crying. At this rate, it won’t be long before Amazon can send ads for Robitussin when it hears you cough.

The Hearable Challenge

The ear also presents nasty challenges for any company hoping to sell a mass-market computing device. Such a device must be tiny, nearly weightless and fit perfectly in each person’s anatomically unique ear canal to be comfortable for long stretches of time. At the same time, it must have enough battery power to last at least as long as a smartphone, not to mention a strong antenna and on-board processor. There’s also the problem of earwax, and the unsolved mystery of how to use an ears-only device without too much head-shaking, hand-waving, ear-tapping, or self-talking. According to one recent study, only six percent of Americans said they were comfortable talking to their voice assistant in public.

Then there’s the stubborn stigma against hearing aids. While hundreds of millions of people think nothing of wearing head or earphones, only 16 percent of the 48 million Americans who could benefit from hearing aids have purchased a pair, says the Hearing Loss Association of America. Those that do buy tend to put it off for an average of seven years.

Tight regulation of the industry hasn’t helped. Because hearing aids have been defined as medical devices, manufacturers must get products approved by the Food & Drug Administration, and consumers need to get a doctor’s prescription and pay to see an audiologist, usually with no help from insurance. Content to go for profit margins over sales growth, a stodgy oligopoly of five companies has been able to dominate the $6 billion-a-year hearing aid industry , selling products that cost an average of $2,700 per pair, according to Consumer Reports. A top-of-the-line pair will set you back $10,000 or more.

Now, that regulatory anchor is about to come loose. Last August, Congress passed the “OTC Hearing Aid Law of 2017”. When it goes into effect in August 2020, if not sooner, companies will be able to sell hearing aids over the counter to people with mild to medium impairment online or at any drugstore, just like glasses makers sell $10 readers to people who don’t want to bother with an optometrist.

This opens a large and growing market. The World Health Organization says that 1.1 billion children and young adults around the world are at risk of hearing loss, having grown up with earphones blasting away at point-blank range.

The law could have dramatic impact. Suddenly, anyone who finds themselves saying “what?” more often than they would like will be able to walk into a Walgreens and buy a consumer-y looking device for a few hundred dollars. Very likely, they will pick it up in the electronics aisle next to colorful iPhone covers and FitBits, not in the aisle for Depends and other products for the elderly. The device may not be marketed as a hearing aid at all, but as Bluetooth earphones with “hearing enhancement” or “personalization.”

“I’ve been waiting for this moment for 20 years,” says KR Liu, Doppler’s former vice president of accessibility, who has worn hearing aids to battle severe hearing loss since she was three. “You have these amazing companies that can do amazing things and have the branding power to de-stigmatize hearing aids.”

Doppler’s Influence

Doppler didn’t invent the hearable, but it had outsized influence during its brief existence. Music industry exec Noah Kraft and former Microsoft executive Fritz Lanman created the company in 2013 to come up with a product the Coachella crowd could use to customize the sound of live music, such as the ability to add a “fuzz” effect or put an upper limit on the volume. By early 2016, it had assembled an impressive team of audio experts and was working on the Here One, which added more “hearing augmentation” features as well as the ability to make phone calls and stream music.

As word of the product’s capabilities began to spread, Doppler began getting inquiries from tech giants interested in catching the hearable wave. Although the Here One was supposed to come to market within months, Kraft, the company’s CEO, declared that October to be “Demo Month.” A small team set up shop in a swank conference room overlooking San Francisco Bay in the offices of Universal Music Group, one of Doppler’s first investors.

Visitors included venture capitalists such as Mary Meeker and Yuri Milner, and teams from Amazon and its Silicon Valley-based R&D unit, Lab 126, as well Google, Apple, and Facebook. While a few companies put some informal bids on the table, none offered anything close to the valuation Kraft and Doppler’s board felt the company was worth.

Any unicorn dreams faded away after the Here One went on sale in early 2017. The tech press praised the device for its innovative design, but abysmal battery life and difficulty explaining why this wasn’t just another wireless earphone led to poor sales. There was one bright spot, however. Nearly a quarter of the buyers had purchased it as cheaper, better sounding alternative to hearing aids—without any marketing effort by Doppler to reach this audience. With Apple’s AirPods taking the consumer earphone market by storm, Doppler decided to pivot. While the engineering team focused on hearing functionality, Liu took a lead role in lobbying for the OTC bill in Washington D.C.

By the time the bill became law last August, Doppler was in dire straits. Kraft re-approached potential acquirers, who immediately agreed to meet. A team from Microsoft, including Nadella, explored whether hearables could be used to boost worker productivity. The companies collaborated on several interesting ideas. Since the Here One had an inward facing microphone that amplified the sound of the wearers own voice, why not create software commands Word or Excel users could whisper so quietly that workmates wouldn’t even notice. In the end, Microsoft decided to pass.

Several teams from Google took another look and passed, including one from the X “moonshot factory” and one from the company’s hardware division, which was looking for help finishing up its soon-to-be-panned Pixel Buds (aka “Pixel Duds”).

In September 2017, Apple sent a large team for another round of talks. It clearly didn’t need Doppler. Apple had been learning about hearing technology since 2011, when it began forging partnerships with hearing aid makers, so customers could pipe sound picked up by the mic in an iPhone directly into their hearing aids (a student, for example, could put their iPhone near a teacher at the front of the room to hear the lecture more clearly). The company had poured big money into creating technology such as the W1 communications chip, which has helped make the AirPods a stand-out in terms of sound quality, battery life and ease-of-use. AirPods captured 24 percent of all wireless earphone sales in the first half of this year, far ahead of runner-up Beats with just 3 percent, according to NPD Group. Still, Apple remained interested in an aqui-hire of key Doppler technologists, particularly those working on hearing algorithms, but wasn’t willing to pay enough to interest Doppler.

Talks with Amazon lasted the longest and were the most serious. With a powerful hearable, it’s customers would be able to shop via Alexa even when not near a smart speaker—and without having to depend on an iPhone or Android device. The Lab 126 team had been looking for years for a way to “get Alexa out of the house.” But aware of Doppler’s fast-declining finances—and possibly because it had learned so much about Doppler’s technology and business during negotiations–Amazon’s deal-makers only offered a low-ball bid

Rather than accept any of the bids, Kraft chose to shut down the company a few weeks later. He later sold Doppler’s intellectual property to Dolby, which specializes in audio software to enhance the sound of movies and other media. Dolby has not confirmed any new products based on Doppler’s patents, but “we are spending time identifying how our technology, ecosystems, and knowledge are relevant to the hearable marketplace,” says Crum, the company’s chief scientist.

“It’s good to hear that Doppler’s vision lives on even though the company doesn’t,” says Kraft via email. “We’re proud of what we built and proud that the Doppler team is helping others bring the in-ear computer to fruition.” Kraft declined to comment on any discussions with Amazon, Apple or any other suitors.
Next Up

Doppler is gone, but the vital signs of the hearables market are getting stronger. Salaries for audio technologists are soaring, with big tech companies often paying $200,000 salaries to top talent from startups and the traditional hearing aid companies. Mobile chip giant Qualcomm introduced its first family of chips specifically for hearables in March, and other chip companies are expected to follow suit by the end of the year.

Amazon, Google, and Apple are keeping their cards to the vest. Three former Doppler employees say Amazon already had a team of 70 people working on hearables when the companies were in talks last year. While Google’s hardware team continues works on Pixel Buds and other products, Google’s X unit is looking at developing fully independent in-ear computers, while the Google Voice unit focuses on ways to make that personal assistant more accessible via ear-based devices, says a person that’s had dealings with all three.

Apple is also marching ahead in its deliberate way. Rather than build a revolutionary new product to usher in the hearable era, it will continue to add new capabilities in familiar form factors, sources say. According to Bloomberg, the company will announce high-end headphones for music lovers by the end of the year, and will introduce a water-resistant upgrade of the AirPods, that includes the ability to activate the device by saying “Hey, Siri.”

Other pioneers of the hearables market are already preparing for the big guys’ arrival. Bragi, a Belgian company founded shortly before Doppler, recently decided to stop selling its hearable devices in favor of licensing its software.

“When you’ve got Apple and others coming directly after you, you need to change where you invest,” says CEO Nikolaj Hvvid. “On the other hand, it’s nice to suddenly be getting all this company.”

Source: FastCompany

Top 10 roles in AI and data science

When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? I hope not!

analytics anywhereGoogle’s Geoff Hinton is a hero of mine and an amazing researcher in deep learning, but I hope you’re not planning to staff your applied data science team with 10 of him and no one else!

Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience.

If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.

#0 Data Engineer

We start counting at zero, of course, since you need to have the ability to get data before it makes sense to talk about data analysis. If you’re dealing with small datasets, data engineering is essentially entering some numbers into a spreadsheet. When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with.

#1 Decision-Maker

Before hiring that PhD-trained data scientist, make sure you have a decision-maker who understands the art and science of data-driven decision-making.

Decision-making skills have to be in place before a team can get value out of data.

This individual is responsible for identifying decisions worth making with data, framing them (everything from designing metrics to calling the shots on statistical assumptions), and determining the required level of analytical rigor based on potential impact on the business. Look for a deep thinker who doesn’t keep saying, “Oh, whoops, that didn’t even occur to me as I was thinking through this decision.” They’ve already thought of it. And that. And that too.

#2 Analyst

Then the next hire is… everyone already working with you. Everyone is qualified to look at data and get inspired, the only thing that might be missing is a bit of familiarity with software that’s well-suited for the job. If you’ve ever looked at a digital photograph, you’ve done data visualization and analytics.

Learning to use tools like R and Python is just an upgrade over MS Paint for data visualization; they’re simply more versatile tools for looking at a wider variety of datasets than just red-green-blue pixel matrices.

If you’ve ever looked at a digital photograph, you’ve done data visualization and analytics. It’s the same thing.

And hey, if all you have the stomach for is looking at the first five rows of data in a spreadsheet, well, that’s still better than nothing. If the entire workforce is empowered to do that, you’ll have a much better finger on the pulse of your business than if no one is looking at any data at all.

The important thing to remember is that you shouldn’t come to conclusions beyond your data. That takes specialist training. Just as with the photo above, here’s all you can say about it: “This is what is in my dataset.” Please don’t use it conclude that the Loch Ness Monster is real.

#3 Expert Analyst

Enter the lightning-fast version! This person can look at more data faster. The game here is speed, exploration, discovery… fun! This is not the role concerned with rigor and careful conclusions. Instead, this is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what’s worth pursuing with more care.

The job here is speed, encountering potential insights as quickly as possible.

This may be counterintuitive, but don’t staff this role with your most reliable engineers who write gorgeous, robust code. The job here is speed, encountering potential insights as quickly as possible, and unfortunately those who obsess over code quality may find it too difficult to zoom through the data fast enough to be useful in this role.

Those who obsess over code quality may find it difficult to be useful in this role.

I’ve seen analysts on engineering-oriented teams bullied because their peers don’t realize what “great code” means for descriptive analytics. Great is “fast and humble” here. If fast-but-sloppy coders don’t get much love, they’ll leave your company and you’ll wonder why you don’t have a finger on the pulse of your business.

#4 Statistician

Now that we’ve got all these folks cheerfully exploring data, we’d better have someone around to put a damper on the feeding frenzy. It’s safe to look at that “photo” of Nessie as long as you have the discipline to keep yourself from learning more than what’s actually there… but do you? While people are pretty good at thinking reasonably about photos, other data types seem to send common sense out the window. It might be a good idea to have someone around who can prevent the team from making unwarranted conclusions.

Inspiration is cheap, but rigor is expensive.

Lifehack: don’t make conclusions and you won’t need to worry. I’m only half-joking. Inspiration is cheap, but rigor is expensive. Pay up or content yourself with mere inspiration.

Statisticians help decision-makers come to conclusions safely beyond the data.

For example, if your machine learning system worked in one dataset, all you can safely conclude is that it worked in that dataset. Will it work when it’s running in production? Should you launch it? You need some extra skills to deal with those questions. Statistical skills.

If we’re want to make serious decisions where we don’t have perfect facts, let’s slow down and take a careful approach. Statisticians help decision-makers come to conclusions safely beyond the data analyzed.

#5 Applied Machine Learning Engineer

An applied AI / machine learning engineer’s best attribute is not an understanding of how algorithms work. Their job is to use them, not build them. (That’s what researchers do.) Expertise at wrangling code that gets existing algorithms to accept and churn through your datasets is what you’re looking for.

Besides quick coding fingers, look for a personality that can cope with failure. You almost never know what you’re doing, even if you think you do. You run the data through a bunch of algorithms as quickly as possible and see if it seems to be working… with the reasonable expectation that you’ll fail a lot before you succeed. A huge part of the job is dabbling blindly, and it takes a certain kind of personality to enjoy that.

Perfectionists tend to struggle as ML engineers.

Because your business problem’s not in a textbook, you can’t know in advance what will work, so you can’t expect to get a perfect result on the first go. That’s okay, just try lots of approaches as quickly as possible and iterate towards a solution.

Speaking of “running the data through algorithms”… what data? The inputs your analysts identified as potentially interesting, of course. That’s why analysts make sense as an earlier hire.

Although there’s a lot of tinkering, it’s important for the machine learning engineer to have a deep respect for the part of the process where rigor is vital: assessment. Does the solution actually work on new data? Luckily, you made a wise choice with your previous hire, so all you have to do is pass the baton to the statistician.

The strongest applied ML engineers have a very good sense of how long it takes to apply various approaches.

When a potential ML hire can rank options by the time it takes to try them on various kinds of datasets, be impressed.

#6 Data Scientist

The way I use the word, a data scientist is someone who is a full expert in all of the three preceding roles. Not everyone uses my definition: you’ll see job applications out there with people calling themselves “data scientist” when they have only really mastered one of the three, so it’s worth checking.

Data scientist are full experts in all of the three previous roles.

This role is in position #6 because hiring the true three-in-one is an expensive option. If you can hire one within budget, it’s a great idea, but if you’re on a tight budget, consider upskilling and growing your existing single-role specialists.

#7 Analytics Manager / Data Science Leader

The analytics manager is the goose that lays the golden egg: they’re a hybrid between the data scientist and the decision-maker. Their presence on the team acts as a force-multiplier, ensuring that your data science team isn’t off in the weeds instead of adding value to your business.

The decision-maker + data scientist hybrid is a force-multiplier. Unfortunately, they’re rare and hard to hire.

This person is kept awake at night by questions like, “How do we design the right questions? How do we make decisions? How do we best allocate our experts? What’s worth doing? Will the skills and data match the requirements? How do we ensure good input data?”

If you’re lucky enough to hire one of these, hold on to them and never let them go. Learn more about this role here.

#8 Qualitative Expert / Social Scientist

Sometimes your decision-maker is a brilliant leader, manager, motivator, influencer, or navigator of organizational politics… but unskilled in the art and science of decision-making. Decision-making is so much more than a talent. If your decision-maker hasn’t honed their craft, they might do more damage than good.

Instead of firing an unskilled decision-maker, you can augment them with a qualitative expert.

Don’t fire an unskilled decision-maker, augment them. You can hire them an upgrade in the form of a helper. The qualitative expert is here to supplement their skills.

This person typically has a social science and data background — behavioral economists, neuroeconomists, and JDM psychologists receive the most specialized training, but self-taught folk can also be good at it. The job is to help the decision maker clarify ideas, examine all the angles, and turn ambiguous intuitions into well-thought-through instructions in language that makes it easy for the rest of the team to execute on.

We don’t realize how valuable social scientists are. They’re usually better equipped than data scientists to translate the intuitions and intentions of a decision-maker into concrete metrics.

The qualitative expert doesn’t call any of the shots. Instead, they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker. Having them on board is a great way to ensure that the project starts out in the right direction.

#9 Researcher

Many hiring managers think their first team member needs to be the ex-professor, but actually you don’t need those PhD folk unless you already know that the industry is not going to supply the algorithms that you need. Most teams won’t know that in advance, so it makes more sense to do things in the right order: before building yourself that space pen, first check whether a pencil will get the job done. Get started first and if you find that the available off-the-shelf solutions aren’t giving you much love, then you should consider hiring researchers.

If a researcher is your first hire, you probably won’t have the right environment to make good use of them.

Don’t bring them in right off the bat. It’s better to wait until your team is developed enough to have figured out that what they need a researcher for. Wait till you’ve exhausted all the available tools before hiring someone to build you expensive new ones.

#10+ Additional personnel

Besides the roles we looked at, here are some of my favorite people to welcome to a decision intelligence project:

  • Domain expert
  • Ethicist
  • Software engineer
  • Reliability engineer
  • UX designer
  • Interactive visualizer / graphic designer
  • Data collection specialist
  • Data product manager
  • Project / program manager

Many projects can’t do without them — the only reason they aren’t listed in my top 10 is that decision intelligence is not their primary business. Instead, they are geniuses at their own discipline and have learned enough about data and decision-making to be remarkably useful to your project. Think of them as having their own major or specialization, but enough love for decision intelligence that they chose to minor in it.

Huge team or small team?

After reading all that, you might feel overwhelmed. So many roles! Take a deep breath. Depending on your needs, you may get enough value from the first few roles.

Revisiting my analogy of applied machine learning as innovating in the kitchen, if you personally want to open an industrial-scale pizzeria that makes innovative pizzas, you need the big team or you need to partner with providers/consultants. If you want to make a unique pizza or two this weekend — caramelized anchovy surprise, anyone? — then you still need to think about all the components we mentioned. You’re going to decide what to make (role 1), which ingredients to use (roles 2 and 3), where to get ingredients (role 0), how to customize the recipe (role 5), and how to give it a taste test (role 4) before serving someone you want to impress, but for the casual version with less at stake, you can do it all on your own. And if your goal is just to make standard traditional pizza, you don’t even need all that: get hold of someone else’s tried and tested recipe (no need to reinvent your own) along with ingredients and start cooking!

Source: hackernoon.com

The Data Science Process

The Data Science Process is a framework for approaching data science tasks, and is crafted by Joe Blitzstein and Hanspeter Pfister of Harvard’s CS 109. The goal of CS 109, as per Blitzstein himself, is to introduce students to the overall process of data science investigation, a goal which should provide some insight into the framework itself.

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The following is a sample application of Blitzstein & Pfister’s framework, regarding skills and tools at each stage, as given by Ryan Fox Squire in his answer:

Stage 1: Ask A Question
Skills: science, domain expertise, curiosity
Tools: your brain, talking to experts, experience

Stage 2: Get the Data
Skills: web scraping, data cleaning, querying databases, CS stuff
Tools: python, pandas

Stage 3: Explore the Data
Skills: Get to know data, develop hypotheses, patterns? anomalies?
Tools: matplotlib, numpy, scipy, pandas, mrjob

Stage 4: Model the Data
Skills: regression, machine learning, validation, big data
Tools: scikits learn, pandas, mrjob, mapreduce

Stage 5: Communicate the Data
Skills: presentation, speaking, visuals, writing
Tools: matplotlib, adobe illustrator, powerpoint/keynote

Squire then (rightfully) concludes that the data science work flow is a non-linear, iterative process, and that there are many skills and tools required to cover the full data science process. Squire also professes that he is fond of the Data Science Process as it stresses both the importance of asking questions to guide your workflow, and the importance of iterating on your questions and research, as one gains familiarity with one’s data.

The Data Science Framework is an innovative framework for approaching data science problems. Isn’t it?

Source: kdnuggets.com

Top 5 tips for businesses implementing RPA

To remain competitive, businesses must digitalise their operations. One way to speed up this process and improve returns is through robotic process automation

Robotic process automation (RPA) is gaining popularity as enterprises discover new ways to drive business impact and speed up digital transformation. RPA is software that mimics how humans use applications to process transactions, harness data and communicate with other systems.
RPA can provide businesses with fast returns on investment by automating manual data processes, freeing up employees for more value-added tasks and improving operational and cost efficiencies. For enterprises that are digitally transforming their operations, RPA software is becoming fundamental to improving productivity, compliance and competitive advantage. The new economy outlines the top five tips businesses should consider for successful RPA implementation:

Start small and learn
By starting their digitalisation transformation with RPA, enterprises can more effectively plan how they will tackle the process. For example, some businesses may look to identify and automate the lengthiest and most repetitive tasks first, before replicating this in other processes. Others may consider automating operations that can impact a specific function.
RPA is at its most effective when combined with other technologies, but starting small and automating simple, tedious and repetitive processes is a good way to develop a strategy that can be implemented more broadly later on.

Implement holistically
Robotics not only enables companies to automate time consuming human processes, it also promotes innovation in terms of how the business is run and what services are offered to customers. For this reason, senior executives should approach RPA implementation holistically, keeping end-to-end processes in mind and looking for opportunities to exploit machine learning and analytics.
Robotics not only enables companies to automate time consuming human processes, it also promotes innovation
For example, an employee might manually open an email and a PDF attachment, review an invoice and enter that information into a software system. RPA streamlines this process, entering invoice amounts into the system faster and more accurately. In doing so, the software creates actionable business insights that can be used to improve process performance even further.

Consider your workforce
Traditionally, business process operations are intensive, service level-focused environments dealing with planned and unplanned peaks, seasonal variations and exhaustive end-month and end-week periods.
When RPA is introduced, this working environment changes dramatically. Automated processes not only work faster than their human counterparts but they can work 24/7 and automatically scale up to deal with peak demand and scale down in periods of lower intensity. There needs to be a shift away from traditional workforce management and rostering, towards a more skilled, customer-focused, value-adding workforce.

Train bots like humans
Treating software bots as human staff is not as farfetched as it sounds. To achieve the most with RPA, businesses should first give their bots something small and task-orientated to work on. Throughout the bots’ lifecycle, it is important to carry out maintenance and reviews, just as you would with human staff evaluations.
Enterprises should make bots just as accountable to the business as the human workforce is. The most successful RPA implementers have maintained this clarity of accountability.

Establish automation governance
Enterprises need to establish automation governance systems as an extension of corporate governance. To ensure robotic processes comply with regulatory controls, businesses must closely monitor and manage digital workers. Businesses must also ensure they have a clear understanding of what robotics laws are in place and how to effectively comply with them.
RPA is an important asset in any enterprise’s digital transformation. By implementing automation with time-intensive, manual, administrative tasks first, companies can learn from RPA-enabled processes and replicate those successes elsewhere in the business. Combining RPA with a broader set of technological tools ultimately improves business outcomes across end-to-end processes.

Source: theneweconomy.com