Category: Investment

Digital Transformation of Business and Society: Challenges and Opportunities by 2020 – Frank Diana

At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.

With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.

Our Emerging Future

He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:

  • Because of exponential progression, it is difficult to imagine the world in 5 years, and although the industrial era was impactful, it will not compare to what lies ahead. The danger of vastly under-estimating the sheer velocity of change is real. For example, in just three months, the projection for the number of autonomous vehicles sold in 2035 went from 100 million to 1.5 billion
  • Six years ago Gerd advised a German auto company about the driverless car and the implications of a sharing economy — and they laughed. Think of what’s happened in just six years — can’t imagine anyone is laughing now. He witnessed something similar as a veteran of the music business where he tried to guide the industry through digital disruption; an industry that shifted from selling $20 CDs to making a fraction of a penny per play. Gerd’s experience in the music business is a lesson we should learn from: you can’t stop people who see value from extracting that value. Protectionist behavior did not work, as the industry lost 71% of their revenue in 12 years. Streaming music will be huge, but the winners are not traditional players. The winners are Spotify, Apple, Facebook, Google, etc. This scenario likely plays out across every industry, as new businesses are emerging, but traditional companies are not running them. Gerd stressed that we can’t let this happen across these other industries
  • Anything that can be automated will be automated: truck drivers and pilots go away, as robots don’t need unions. There is just too much to be gained not to automate. For example, 60% of the cost in the system could be eliminated by interconnecting logistics, possibly realized via a Logistics Internet as described by economist Jeremy Rifkin. But the drive towards automation will have unintended consequences and some science fiction scenarios could play out. Humanity and technology are indeed intertwining, but technology does not have ethics. A self-driving car would need ethics, as we make difficult decisions while driving all the time. How does a car decide to hit a frog versus swerving and hitting a mother and her child? Speaking of science fiction scenarios, Gerd predicts that when these things come together, humans and machines will have converged:
  • Gerd has been using the term “Hellven” to represent the two paths technology can take. Is it 90% heaven and 10% hell (unintended consequences), or can this equation flip? He asks the question: Where are we trying to go with this? He used the real example of Drones used to benefit society (heaven), but people buying guns to shoot them down (hell). As we pursue exponential technologies, we must do it in a way that avoids negative consequences. Will we allow humanity to move down a path where by 2030, we will all be human-machine hybrids? Will hacking drive chaos, as hackers gain control of a vehicle? A recent Jeep recall of 1.4 million jeeps underscores the possibility. A world of super intelligence requires super humanity — technology does not have ethics, but society depends on it. Is this Ray Kurzweil vision what we want?
  • Is society truly ready for human-machine hybrids, or even advancements like the driverless car that may be closer to realization? Gerd used a very effective Video to make the point
  • Followers of my Blog know I’m a big believer in the coming shift to value ecosystems. Gerd described this as a move away from Egosystems, where large companies are running large things, to interdependent Ecosystems. I’ve talked about the blurring of industry boundaries and the movement towards ecosystems. We may ultimately move away from the industry construct and end up with a handful of ecosystems like: mobility, shelter, resources, wellness, growth, money, maker, and comfort
  • Our kids will live to 90 or 100 as the default. We are gaining 8 hours of longevity per day — one third of a year per year. Genetic engineering is likely to eradicate disease, impacting longevity and global population. DNA editing is becoming a real possibility in the next 10 years, and at least 50 Silicon Valley companies are focused on ending aging and eliminating death. One such company is Human Longevity Inc., which was co-founded by Peter Diamandis of Singularity University. Gerd used a quote from Peter to help the audience understand the motivation: “Today there are six to seven trillion dollars a year spent on healthcare, half of which goes to people over the age of 65. In addition, people over the age of 65 hold something on the order of $60 trillion in wealth. And the question is what would people pay for an extra 10, 20, 30, 40 years of healthy life. It’s a huge opportunity”
  • Gerd described the growing need to focus on the right side of our brain. He believes that algorithms can only go so far. Our right brain characteristics cannot be replicated by an algorithm, making a human-algorithm combination — or humarithm as Gerd calls it — a better path. The right brain characteristics that grow in importance and drive future hiring profiles are:
  • Google is on the way to becoming the global operating system — an Artificial Intelligence enterprise. In the future, you won’t search, because as a digital assistant, Google will already know what you want. Gerd quotes Ray Kurzweil in saying that by 2027, the capacity of one computer will equal that of the human brain — at which point we shift from an artificial narrow intelligence, to an artificial general intelligence. In thinking about AI, Gerd flips the paradigm to IA or intelligent Assistant. For example, Schwab already has an intelligent portfolio. He indicated that every bank is investing in intelligent portfolios that deal with simple investments that robots can handle. This leads to a 50% replacement of financial advisors by robots and AI
  • This intelligent assistant race has just begun, as Siri, Google Now, Facebook MoneyPenny, and Amazon Echo vie for intelligent assistant positioning. Intelligent assistants could eliminate the need for actual assistants in five years, and creep into countless scenarios over time. Police departments are already capable of determining who is likely to commit a crime in the next month, and there are examples of police taking preventative measures. Augmentation adds another dimension, as an officer wearing glasses can identify you upon seeing you and have your records displayed in front of them. There are over 100 companies focused on augmentation, and a number of intelligent assistant examples surrounding IBM Watson; the most discussed being the effectiveness of doctor assistance. An intelligent assistant is the likely first role in the autonomous vehicle transition, as cars step in to provide a number of valuable services without completely taking over. There are countless Examples emerging
  • Gerd took two polls during his keynote. Here is the first: how do you feel about the rise of intelligent digital assistants? Answers 1 and 2 below received the lion share of the votes
  • Collectively, automation, robotics, intelligent assistants, and artificial intelligence will reframe business, commerce, culture, and society. This is perhaps the key take away from a discussion like this. We are at an inflection point where reframing begins to drive real structural change. How many leaders are ready for true structural change?
  • Gerd likes to refer to the 7-ations: Digitization, De-Materialization, Automation, Virtualization, Optimization, Augmentation, and Robotization. Consequences of the exponential and combinatorial growth of these seven include dependency, job displacement, and abundance. Whereas these seven are tools for dramatic cost reduction, they also lead to abundance. Examples are everywhere, from the 16 million songs available through Spotify, to the 3D printed cars that require only 50 parts. As supply exceeds demand in category after category, we reach abundance. As Gerd put it, in five years’ time, genome sequencing will be cheaper than flushing the toilet and abundant energy will be available by 2035 (2015 will be the first year that a major oil company will leave the oil business to enter the abundance of the renewable business). Other things to consider regarding abundance:
  • Efficiency and business improvement is a path not a destination. Gerd estimates that total efficiency will be reached in 5 to 10 years, creating value through productivity gains along the way. However, after total efficiency is met, value comes from purpose. Purpose-driven companies have an aspirational purpose that aims to transform the planet; referred to as a massive transformative purpose in a recent book on exponential organizations. When you consider the value that the millennial generation places on purpose, it is clear that successful organizations must excel at both technology and humanity. If we allow technology to trump humanity, business would have no purpose
  • In the first phase, the value lies in the automation itself (productivity, cost savings). In the second phase, the value lies in those things that cannot be automated. Anything that is human about your company cannot be automated: purpose, design, and brand become more powerful. Companies must invent new things that are only possible because of automation
  • Technological unemployment is real this time — and exponential. Gerd talked to a recent study by the Economist that describes how robotics and artificial intelligence will increasingly be used in place of humans to perform repetitive tasks. On the other side of the spectrum is a demand for better customer service and greater skills in innovation driven by globalization and falling barriers to market entry. Therefore, creativity and social intelligence will become crucial differentiators for many businesses; jobs will increasingly demand skills in creative problem-solving and constructive interaction with others
  • Gerd described a basic income guarantee that may be necessary if some of these unemployment scenarios play out. Something like this is already on the ballot in Switzerland, and it is not the first time this has been talked about:
  • In the world of automation, experience becomes extremely valuable — and you can’t, nor should attempt to — automate experiences. We clearly see an intense focus on customer experience, and we had a great discussion on the topic on an August 26th Game Changers broadcast. Innovation is critical to both the service economy and experience economy. Gerd used a visual to describe the progression of economic value:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
  • Gerd used a second poll to sense how people would feel about humans becoming artificially intelligent. Here again, the audience leaned towards the first two possible answers:

Gerd then summarized the session as follows:

The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.

My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently

When looking at AI, consider trying IA first (intelligent assistance / augmentation).

My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement

Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.

My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value

We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.

My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice

“The best way to predict the future is to create it” (Alan Kay).

My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens

Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf

Corporate venture building dilemma: investment vs. control – Carlos Borges

Having founded my startup a few years ago, I am familiar to why founders go through the pain & grit to build their own company. The statistics around startup survival rates show that the risk is high, but the potential reward both financially & emotionally is also significant.

In my case, risk was defined by the amount of money I invested in the venture plus the opportunity cost in case the startup goes nowhere. The later relates to the fact that I earned no salary at the beginning & that when I committed to that specific idea I was instantaneously saying “no” to many other opportunities and potential career advancements. The reward was two-fold too; the first one was the attractive financial outcome of a potential exit. The second one was the freedom to chase opportunities as they appear, doing what I want and how I want it.

Once I raised capital from investors, I basically traded reward for reduced risk. I started paying myself a small salary and anticipated that more resources would increase the success likelihood of the startup.

This pattern of weighing risk against rewards was crystal clear in my mind… until I joined the arena of corporate venture building. Directly during one of my first projects, I was tasked with the creation of a startup for a blue-chip corporate client. I was immediately puzzled by the reasoning behind this endeavor.

Ultimately corporate decisions are also guided by risk against reward: if they don’t take risks and innovate they might be left behind and, in some cases, join the once-great-now-extinct corporate hall of shame. That’s why they invest in research and development, spend hard earned cash in mergers and acquisitions and start innovation programs. But my interest was more at a micro level, meaning, which reasoning my corporate client follows to decide if and how to found a specific new venture?

Having thought about it a lot, I believe at micro level corporates weigh investment against control. Investment is the level of capital, manpower & political will provided by the corporate to propel the venture towards exit, break-even or strategic relevance. Control is the possibility to steer the venture towards the strategic goals the leadership team has in mind while defining the boundaries of what can & cannot be done.

In the startup case, the risk/reward is typically shared between the founders and external investors. In a corporate venture building case, the investment/control can be shared between the corporate, an empowered founder team and also external investors.

I am still in the middle of the corporate decision-making process but wanted to share with you the scenarios we are using to guide the discussions on how to structure the new venture. But before I do, I would like to mention that the considerations of investment vs. control takes place at three different stages of the venture’s existence:

• Incubation: develop & validate idea
• Acceleration: validate business model incl. product, operations & customer acquisition (find the winning formula)
• Growth: replicate the formula to grow exponentially

Based on that, three main scenarios are being considered to found the new venture.

Scenario 1: Control & Grow

  • Full investment & control during incubation & acceleration
  • Shared investment & control during the growth stage

Per definition, the incubation and acceleration stages are less capital intensive and is the moment when key strategic decisions that shape the future business are made. In these stages, the corporate is interested in maintaining the full control of the venture while absorbing the whole investment. Only when they enter the capital-intensive growth stage it becomes necessary to “share the burden” with other institutional or strategic investors. This scenario is suitable for ventures of high strategic value, especially the ones leveraging core assets and know-how of the corporate mothership.

Scenario 2: Spread the Bets

  • Lower investment & control during all stages

In this case, the corporate initiator empowers a founder team and joins the project almost like an external investor would do at Seed and Series A of a startup. They agree on a broad vision, provide the funding and retain a part of the shares with shareholder meetings in between to track progress. Beyond that, they let the founder team do their thing. External investors can join at any funding round to share the investment tickets. The corporate would have lower control and investment from the get-go and can increase their influence only when new funding rounds are required or via an acquisition offer. This scenario is suitable for ventures in which the corporate can function as the first client or use their network to manufacture, market or distribute the product or service.

Scenario 3: Build, operate & transfer

  • Lower investment & control during incubation & acceleration
  • Full investment & control during the growth stage

The venture is initially built by a founder team or external partners (often a consultancy). Only once they successfully finalized the incubation and acceleration stages, the corporate has the right or obligation to absorb the business. Differently than scenario 2, the corporate gains stronger control of the trajectory of the business during its initial stages by defining how a “transfer” event looks like. The investment necessary to put together a strong founder team is reduced by the reward of a pre-defined & short term exit event. The initial investment can be further reduced by the participation of Business Angels, also motivated by a clear path to exit and access to a new source of deal flow. This scenario is suitable for ventures closely linked to the core business of the corporate and where speed & excellence of execution is key.

There is obviously no right and wrong. Each scenario can make sense according to the end goal of the corporate. Furthermore, there are surely new scenarios and variations of the above. What is important in my opinion is to openly discuss which road to take. If the client can’t discern the alternatives and consequences, you will risk a “best of both worlds” mindset where expectations regarding investment & control don’t match. If that is the case, you will be up for a tough ride

Source : https://medium.com/@cbgf/a-corporate-venture-building-dilemma-investment-vs-control-a703b9c19c94

Ten Signs You’re Headed for Trouble in 2019 – ITL

gartner-hype

Many of you have seen the Gartner Hype Cycle curve. When a hot technology appears, it gets hyped and hyped until one day enough people become impatient, and sentiment turns against the technology. It then heads into what Gartner calls the Trough of Disillusionment. Eventually, the technology finds its role – often a major one – in the market.

The idea has always struck me as rather obvious (I described the curve to reporter colleagues on the tech beat at the Wall Street Journal years before I ever saw the Gartner chart), but Gartner popularized the notion, which is why it’s known as the Gartner Hype Cycle rather than, say, the Carroll Hype Cycle. Gartner is to be commended, because technologies can be plotted on the curve, and, drawing on history, their futures can be predicted with some confidence.

On the Carroll…er, Gartner Hype Cycle, the idea of technology-driven innovation in insurance seems to be heading into the Trough of Disillusionment (great name) among incumbents. A Lemonade or Trov hasn’t taken over the world. Big Tech is coming to insurance but not really here yet for most insurers. Industry executives seem to have read everything they care to about AI, blockchain, etc., and are starting to describe plans for small-bore improvements rather than truly innovative ones. Not total disillusionment, but headed in that direction.

Which brings me to the warning signs for 2019.

The slide into the Trough of Disillusionment creates real opportunities because prices of insurtechs will start to settle back toward reality. In any case, technologies keep maturing, no matter how we feel about them, so the day of reckoning in the market creeps closer all the time, and the slide toward disillusionment is the last opportunity for companies to position themselves before a host of technologies and startups will shake the insurance market.

If I’m right, 2019 may well be the last chance for insurance industry incumbents to start taking advantage of the opportunities presented by insurtech, or lose out to nimbler competitors. In that spirit, my colleagues and I at ITL pulled some thoughts together for incumbents on:

10 Signs You’re Headed for Trouble in 2019

  • You set up an innovation fund and think that means you’re innovative.
  • Your innovations focus on cutting expenses, to the exclusion of all else, and – worse – you reward executives based on those cuts.
  • You say your legacy IT systems are what is preventing you from innovating.
  • You say your defensive culture is preventing you from innovating.
  • You practice “innovation tourism,” going to Silicon Valley and assuming magic dust will wear off on you. (Related warning sign: You have a ping pong table and coffee bar and think they signify creativity.)
  • You have 6,000 ideas but can’t figure out how to turn one into a product.
  • You can’t name 20 insurtechs that operate in your strategic domain or adjacent ones.
  • You aren’t starting to move your operations into the cloud.
  • You don’t have significant diversity in your management team and board, in terms of gender, race, age and nationality.
  • You can’t quantify and measure how you’re doing on your innovation journey and hope you’re improving.

Bonus warning sign: You make television commercials criticizing innovative companies.

In “The Sun Also Rises,” a character is asked how he went bankrupt. “Two ways,” he says, “gradually, then suddenly.” We’re still in the “gradually” part of innovation driven by insurtech, but “suddenly” is coming. I suggest insurance industry incumbents view 2019 and warning signs like these as a last warning to get moving and avoid innovation bankruptcy.

Source : http://blog.insurancethoughtleadership.com/blog/ten-signs-youre-headed-for-trouble-in-2019

Data-driven transformation of the life sciences industry – RockHealth

Digital health innovation continues moving full-force in transforming the business of healthcare. For pharma and medtech companies in particular, this ongoing shift has pushed them to identify ways to create value for patients beyond the drugs themselves. From new partnerships between digital health and life science companies to revamped commercial models, collecting and extracting insights from data is at the core of these growth opportunities. But navigating the rapidly evolving terrain is no simple task.

To help these companies effectively incorporate and utilize digital health tools, Rock Health partner ZS Associates draws on over 30 years of industry expertise to guide them through the complex digital health landscape. We chatted with Principal Pete Masloski to discuss how he works with clients to help identify, develop, and commercialize digital health solutions within their core businesses—and where he sees patients benefiting the most as a result.

Note: This interview has been lightly edited for clarity.

Where does ZS see the promise of data- and analytics-enabled digital health tools leading to in the next five years, 10 years, and beyond?

Data and analytics will play a central role in the digital health industry’s growth over the next five to ten years. Startups are able to capture larger, novel sets of data in a way that large life science companies historically have not been able to. As a result, consumers will be better informed about their health choices; physicians will have more visibility into what treatment options work best for whom under what circumstances; health plans will have a better understanding of treatment choices; and pharmaceutical and medical device companies will be able to strategically determine which products and services to build.

We see personalized medicine, driven by genomics and targeted therapies, rapidly expanding over the next few years. Pharmaceutical discovery and development will also transition to become more digitally enabled. The ability to match patients with clinical trials and improve the patient experience will result in lower costs, faster completion, and more targeted therapies. The increase in real-world evidence will be used to demonstrate the efficacy of therapeutics and devices in different populations, which assures payers and providers that outcomes from studies can be replicated in the real world.

How is digital health helping life sciences companies innovate their commercial models? What is the role of data and analytics in these new models?

The pharmaceutical industry continues to face a number of challenges, including the increasingly competitive markets, growing biosimilar competition, and overall scrutiny on pricing. We’ve seen excitement around solutions that integrate drugs with meaningful outcomes and solutions that address gaps in care delivery and promote medication adherence.

Solving these problems creates new business model opportunities for the industry through fresh revenue sources and ways of structuring agreements with customers. For example, risk-based contracts with health plans, employers, or integrated delivery networks (IDNs) become more feasible when you can demonstrate increased likelihood of better outcomes for more patients. We see this coming to fruition when pharma companies integrate comprehensive digital adherence solutions focused on patient behavior change around a specific drug, as in Healthprize’s partnership with Boehringer Ingelheim. In medtech, digital health tools can both differentiate core products and create new profitable software or services businesses. Integrating data collection technology and connectivity into devices and adding software-enabled services can support a move from traditional equipment sales to pay-per-use. This allows customers to access the new equipment technology without paying a large sum up front—and ensures manufacturers will have a more predictable ongoing source of revenue.

That said, data and analytics remain at the core of these new models. In some cases, such as remote monitoring, the data itself is the heart of the solution; in others, the data collected helps establish effectiveness and value as a baseline for measuring impact. Digital ambulatory blood pressure monitors capture an individual’s complete blood pressure profile throughout the day, which provides a previously unavailable and reliable “baseline.” Because in-office only readings may be skewed by “white coat hypertension,” or stress-induced blood pressure readings, having a more comprehensive look at this data can lead to deeper understandings of user behaviors or conditions. Continuous blood pressure readings can help with diagnoses of stress-related drivers of blood pressure spikes, for example. These insights become the catalyst for life science companies’ new product offerings and go-to-market strategies.

What are some examples of how data sets gathered from partnerships with digital health companies can be leveraged to uncover new value for patients and address their unmet needs?

As digital health companies achieve a certain degree of scale, their expansive data sets become more valuable because of the insights that can be harnessed to improve outcomes and business decisions. Companies like 23andMe, for example, have focused on leveraging their data for research into targeted therapies. Flatiron Health is another great example of a startup that created a foundational platform (EMR) whose clinical data from diverse sources (e.g., laboratories, research repositories, and payer networks) became so highly valued in cancer therapy development that Roche acquired it earlier this year for close to $2B.

It’s exciting to think about the wide array of digital health solutions and the actionable insight that can be gleaned from them. One reason partnerships are important for the industry is few innovators who are collecting data have the capabilities and resources to fully capitalize on its use on their own. Pharma companies and startups must work together to achieve all of this at scale. Earlier this year, Fitbit announced a new partnership with Google to make the data collected from its devices available to doctors. Google’s API can directly link heart rate and fitness activity to the EMR, allowing doctors to easily review and analyze larger amounts of data. This increase in visibility provides physicians with more insight into how patients are doing in between visits, and therefore can also help with decision pathways.

Another example announced earlier this year is a partnership between Evidation Health and Tidepool, who are conducting a new research study, called the T1D Sleep Pilot, to study real-world data from Type 1 diabetics. With Evidation’s data platform and Tidepool’s device-agnostic consumer software, the goal is to better understand the dynamics of sleep and diabetes by studying data from glucose monitors, insulin pumps, and sleep and activity trackers. The data collected from sleep and activity trackers in particular allows us to better understand possible correlations between specific chronic conditions, like diabetes, and the impact of sleep—which in the past has been challenging to monitor. These additional insights provide a more comprehensive understanding of a patient’s condition and can lead to changes in treatment decisions—and ultimately, better outcomes.

How do you assess the quality and reliability of the data generated by digital health companies? What standards are you measuring them against?

Data quality management (DQM) is the way in which leading companies evaluate the quality and reliability of data sources. ISO 9000’s definition of quality is “the degree to which a set of inherent characteristics fulfills requirements.” At ZS, we have a very robust DQM methodology, and our definition goes beyond the basics to include both the accuracy and the value of the data. Factors such as accuracy and absence of errors, and fulfilling specifications (business rules, designs, etc.), are foundational, but in our experience it’s most important to also include an assessment of value, completeness, and lack of bias because often these factors can lead to misleading or inaccurate insights from analysis of that data.

However, it’s not easy assessing the value of a new data source, which presents an entirely different set of challenges. One very important one is the actual interpretation of the data that’s being collected. How do you know when someone is shaking their phone or Fitbit to inflate their steps, or how do you interpret that the device has been taken off and it’s not tracking activity? How do you account for that and go beyond the data to understand what is really happening? As we get more experience with IOT devices and algorithms get smarter, we will get better at interpreting what these devices are collecting and be more forgiving of underlying data quality.

What are the ethical implications or issues (such as data ownership, privacy, and bias) you’ve encountered thus far, or anticipate encountering in the near future?

The ethical stewardship and protection of personal health data are just as essential for the long-term sustainability of the digital health industry as the data itself. The key question is, how can the industry realize the full value from this data without crossing the line? Protecting personal data in an increasingly digitized world—where we’ve largely become apathetic to the ubiquitous “terms and conditions” agreements—is a non-negotiable. How digital health and life science companies collect, manage, and protect users’ information will remain a big concern.

There are also ethical issues around what the data that is captured is used for. Companies need to carefully establish how to appropriately leverage the data without crossing the line. For example, using de-identified data for research purposes with the goal of improving products or services is aligned with creating a better experience for the patient, as opposed to leveraging the data for targeted marketing purposes.

Companies also face the issue of potential biases that may emerge when introducing AI and machine learning into decision-making processes around treatment or access to care. Statistical models are only as good as the data that are used to train them. Companies introducing these models need to test datasets and their AI model outputs to ensure gaps are eliminated from training data, the algorithms don’t learn to introduce bias, and they establish a process for evaluating bias as the models continue to learn and evolve.

Source : https://rockhealth.com/the-data-driven-transformation-of-the-life-sciences-industry-a-qa-with-zs-associates-pete-masloski/

When, which … Design Thinking, Lean, Design Sprint, Agile? – Geert Claes

Confusion galore!

A lot of people are — understandably so — very confused when it comes to innovation methodologies, frameworks, and techniques. Questions like: “When should we use Design Thinking?”, “What is the purpose of a Design Sprint?”, “Is Lean Startup just for startups?”, “Where does Agile fit in?”, “What happens after the <some methodology> phase?” are all very common questions.

(How) does it all connect?

When browsing the Internet for answers, one notices quickly that others too are struggling to understand how it all works together.

Gartner (as well as numerous others) tried to visualise how methodologies like Design Thinking, Lean, Design Sprint and Agile flow nicely from one to the next. Most of these visualisations have a number of nicely coloured and connected circles, but for me they seem to miss the mark. The place where one methodology flows into the next is very debatable, because there are too many similar techniques and there is just too much overlap.

The innovation spectrum

It probably makes more sense to just look at Design Thinking, Lean, Design Sprint & Agile as a bunch of tools and techniques in one’s toolbox, rather than argue for one over the other, because they can all add value somewhere on the innovation spectrum.

Innovation initiatives can range from exploring an abstract problem space, to experimenting with a number of solutions, before continuously improving a very concrete solution in a specific market space.

Business model

An aspect which often seems to be omitted, is the business model maturity axis. For established products as well as adjacent ones (think McKinsey’s Horizon 1 and 2), the business models are often very well understood. For startups and disruptive innovations within an established business however, the business model will need to be validated through experiments.

Methodologies

Design Thinking

Design Thinking really shines when we need to better understand the problem space and identify the early adopters. There are various flavors of design thinking, but they all sort of follow the double-diamond flow. Simplistically the first diamond starts by diverging and gathering lots of insights through talking to our target stakeholders, followed by converging through clustering these insights and identifying key pain-points, problems or jobs to be done. The second diamond starts by a diverging exercise to ideate a large number of potential solutions before prototyping and testing the most promising ideas. Design Thinking is mainly focussed on qualitative rather than quantitative insights.

Lean Startup

The slight difference with Design Thinking is that the entrepreneur (or intrapreneur) often already has a good understanding of the problem space. Lean considers everything to be a hypothesis or assumption until validated …so even that good understanding of the problem space is just an assumption. Lean tends to starts by specifying your assumptions on a customer focussed (lean) canvas and then prioritizing and validating the assumptions according to highest risk for the entire product. The process to validate assumptions is creating an experiment (build), testing it (measure) and learn whether our assumption or hypothesis still stands. Lean uses qualitative insights early on but later forces you to define actionable quantitative data to measure how effective the solution addresses the problem and whether the growth strategy is on track. The “Get out of the building” phrase is often associated with Lean Startup, but the same principle of reaching out the customers obviously also counts for Design Thinking (… and Design Sprint … and Agile).

Design Sprint

It appears that the Google Venture-style Design Sprint method could have its roots from a technique described in the Lean UX book. The key strength of a Design Sprint is to share insights, ideate, prototype and test a concept all in a 5-day sprint. Given the short timeframe, Design Sprints only focus on part of the solution, but it’s an excellent way to learn really quickly if you are on the right track or not.

Agile

Just like dealing with the uncertainty of our problem, solution and market assumptions, agile development is a great way to cope with uncertainty in product development. No need to specify every detail of a product up-front, because here too there are plenty of assumptions and uncertainty. Agile is a great way to build-measure-learn and validate assumptions whilst creating a Minimum Viable Product in Lean Startup parlance. We should define and prioritize a backlog of value to be delivered and work in short sprints, delivering and testing the value as part of each sprint.

Conclusion

Probably not really the answer you were looking for, but there is no clear rule on when to start where. There is also no obvious handover point because there is just too much overlap, and this significant overlap could be the explanation of why some people claim methodology <x> is better than <y>.

Anyhow, most innovation methodologies can add great value and it’s really up to the team to decide where to start and when to apply which methods and techniques. The common ground most can agree with, is to avoid falling in love with your own solution and listen to qualitative as well as quantitative customer feedback.

Innovation Spectrum

Some great books: Creative Confidence, Lean Startup, Running Lean, Sprint, Dual Transformation, Lean UX, Lean Enterprise, Scaling Lean … and a nice video on Innovation@50x

Update: minor update in the innovation canvas, moving the top axis of problem-solution-market to the side

Source : https://medium.com/@geertwlclaes/when-which-design-thinking-lean-design-sprint-agile-a4614fa778b9

Former Google CEO Eric Schmidt listed the ‘3 big failures’ he sees in tech startups today – Business Insider

Former Google CEO Eric Schmidt has listed the three “big failures” in tech entrepreneurship around the world.

Schmidt outlined the failings in a speech he gave at the Centre for Entrepreneurs in London this week. He later expanded on his thoughts in an interview with former BBC News boss James Harding.

Below are the three mistakes he outlined, with quotes taken from both a draft of his speech seen by Business Insider, and comments he delivered on the night.

1. People stick to who and what they know

“Far too often, we invest mostly in people we already know, who are working in very narrow disciplines,” Schmidt wrote in his draft.

In his speech, Schmidt pegged this point closely to a need for diversity and inclusion. He said companies need to be open to bringing in people from other countries and backgrounds.

He said entrepreneurship won’t flourish if people are “going to one institution, hiring only those people, and only — if I can be blunt — only white males.”

During the Q&A, Schmidt specifically addressed the gender imbalance in the tech industry. He said there’s a reason to be optimistic about women’s representation in tech improving, predicting that tech’s gender imbalance will vanish in one generation.

2. Too much focus on product and not on platforms

“We frequently don’t build the best technology platforms to tackle big social challenges, because often there is no immediate promise of commercial return,” Schmidt wrote in his draft.

“There are a million e-commerce apps but not enough speciality platforms for safely sharing and analyzing data on homelessness, climate change or refugees.”

Schmidt’s omitted this mention of socially conscious tech from his final speech, but did say that he sees a lot of innovation coming out of network platforms, which allow people to connect and pool data, because “the barrier to entry for these startups is very, very low.”

3. Companies aren’t partnering up early enough

Finally, Schmidt wrote in his draft that tech startups don’t partner enough with other companies in the modern, hyper-connected world. “It’s impossible to think about any major challenge for society in a silo,” he wrote.

He said in his speech that tech firms have to be ready to partner “fairly early.” He gave the example of a startup that wants to build homecare robots.

“The market for homecare robots is going to be very, very large. The problem is that you need visual systems, and machine learning systems, and listening systems, and motor systems, and so forth. You’re not going to be able to do it with three people,” he said.

After detailing his failures in tech entrepreneurship, Schmidt laid out what he views as the solution. He referred back to the Renaissance in Europe, saying people turned their hand to all sorts of disciplines, from science, to art, to business.

Source : https://www.businessinsider.com/eric-schmidt-3-big-failures-he-sees-in-tech-entrepreneurship-2018-11

What Do Investors Need to Know About the Future of LED Grow Light Technology – Agfund

The horticultural lighting market is growing, and growing rapidly. According to a September press release from Report Linker, a market research firm specializing in agribusiness, the horticultural lighting market is estimated grow from a $2.43 billion market this year to $6.21 billion in 2023.

One of the key factors driving current market sector growth is increased development of LED grow light technology. LEDs (light emitting diodes) were first developed in the 1950s as a smaller and longer-lasting source of light compared to the traditional incandescent light bulb invented by Thomas Edison in 1879.

LEDs last longer, give off less heat, and are more efficient converting energy to light compared to other types of lights, all features that can result in higher yields and profits for indoor growers.

But until recently, LEDs were only used to grow plants indoors experimentally, largely because the cost was still too high for commercial businesses. Many commercial growers still use HID (High Intensity Discharge) lights such as High Pressure Sodium, Metal Halide, and Ceramic Metal Halide; all lights that have a high power output but are less durable than LED lights, generate far more heat, and have less customizable light spectra.

Today, LEDs are fast becoming the dominant horticultural lighting solution. This is due primarily to the one-million fold decrease in fabrication cost of semiconductor chips used to make LED lights since 1954.

For investors more familiar with field-based agriculture, it can certainly be a minefield to know where LED lighting technology for horticulture is going in the future. Although it is no longer the “early days” of LED technology development, current trends are still shaping the future of LED technology.

So what does the intelligent agtech investor need to know about the current state and future of LED grow light technology?

I interviewed Jeff Mastin, director of R&D at Total Grow LED Lighting, to discuss what the future of LED grow light technology for agriculture looks like, and how investors can use current trends to their advantage in the future.

What is your background – how did you get involved in grow light technology at Total Grow?

The company behind TotalGrow is called Venntis Technologies. Venntis has, and still does, specialize in integrating touch-sensing semiconductor technologies into applications.

Most people don’t realize LEDs are semiconductors; you can also use them for touch-sensing technologies, so there’s a strong bridge to agricultural LED technology.

Some of the biggest technical challenges in utilizing LEDs effectively for agriculture include LED glaring, shadowing and color separation.

We have used our expertise in touch-sensing LEDs to expand into horticultural LEDs, and we have developed technology that addresses the above challenges better, giving better control over the spectrum that the LED makes and the directional output of the light in a way that a standard LED by itself can’t do.

My personal background is in biology. When TotalGrow started exploring the horticultural world, that’s where being a biologist was a natural fit to take a lead on the science and the research side of the development process for the product; that was about 7 years ago now.

If you were going to distill your technical focus into trends that you’re seeing in the horticultural lighting space, what are the main trends to keep an eye on?

The horticultural lighting industry is really becoming revolutionized because of LEDs. Less than 10 years ago, LEDs in the horticultural world were mainly a research tool and a novelty.

In the past, they were not efficient enough and they were definitely not affordable enough yet to really consider them an economical general commercial light source.

But that is very quickly changing. The efficiencies are going up and prices down and they are really right now hitting the tipping point where for a lot of applications, but definitely not all applications, the LED world is starting to take over horticulture and indoor agriculture.

How do you view the translation of those trends into actionable points? For investors or technology developers in the agriculture technology space, how do they make sure that the LED light technology they are investing in isn’t going to be obsolete in a year or two?

With LEDs, the key question is still cost-efficiency, and there’s only so far the technology can improve.

Why? There are physical limitations. You can’t make a 100% efficient product that turns every bit of electricity into photons of light. At this point, the efficiency level of the top of line LEDs are up over 50%.

Can we ever get up to 70 or 80%? Probably not any time soon with an end-product, not one that’s going to be affordable and economical generally speaking.

So to answer your question, it’s not a category where you’re going to say, “well this is obsolete, I can get something three times better now.” The performance improvements will be more marginal in the future.

Ten years from now the cost will be cheaper. But that again doesn’t make current LED technologies obsolete. In terms of that fear, I don’t think people have to worry about current LED light technologies becoming obsolete.

In a large commercial vertical farming set up, what is the ballpark cost of horticultural LEDs currently?

To give just an order of magnitude sort of number, you’re probably going to be someplace in the $30 per square foot number for lights for a large facility. It can be half that or it can be double that.

That’s just talking within the realm of common vertical farming plants like greens and herbs, or other plants similar in size and lighting needs.

If you start talking about tomatoes or medicinal plants, then the ability to use higher light levels and have the plants make good use of it skyrockets. You can go four times higher with some of those other plants, and for good reason.

What type of horticultural lighting applications are LEDs still not the best solution for now and in the foreseeable future?

There are at least 3 areas where LEDs still may not make sense now and in the near future.

First, if the LED lights are not used often enough. The more hours per year the lights are used, the more quickly they return on their investment from power savings and reduced maintenance. Some applications only need a few weeks of lighting per year, which makes a cheaper solution appropriate.

Second, in some greenhouse applications, LED’s may not be the best choice for some time to come. Cheaper lights like high-pressure sodium have more of a role in greenhouses where hours of use are less and higher hang heights are possible. (Many greenhouses will still benefit strongly from LEDs, but the economics and other considerations make it important to consider both options in greenhouses.)

Lastly, some plants are not the best in vertical farming styles of growing where LEDs have their most drastic advantages. At least at this point it is not common to attempt to grow larger fruiting plants like tomatoes or cucumbers totally indoors, though when attempted that is still more practical with LEDs than legacy lights.

Source : https://agfundernews.com/what-do-investors-need-to-know-about-the-future-of-led-grow-light-technology.html/

6 Biases Holding You Back From Rational Thinking – Robert Greene

Emotions are continually affecting our thought processes and decisions, below the level of our awareness. And the most common emotion of them all is the desire for pleasure and the avoidance of pain. Our thoughts almost inevitably revolve around this desire; we simply recoil from entertaining ideas that are unpleasant or painful to us. We imagine we are looking for the truth, or being realistic, when in fact we are holding on to ideas that bring a release from tension and soothe our egos, make us feel superior. This pleasure principle in thinking is the source of all of our mental biases. If you believe that you are somehow immune to any of the following biases, it is simply an example of the pleasure principle in action. Instead, it is best to search and see how they continually operate inside of you, as well as learn how to identify such irrationality in others.

These biases, by distorting reality, lead to the mistakes and ineffective decisions that plague our lives. Being aware of them, we can begin to counterbalance their effects.

1) Confirmation Bias

I look at the evidence and arrive at my decisions through more or less rational processes.

To hold an idea and convince ourselves we arrived at it rationally, we go in search of evidence to support our view. What could be more objective or scientific? But because of the pleasure principle and its unconscious influence, we manage to find that evidence that confirms what we want to believe. This is known as confirmation bias.

We can see this at work in people’s plans, particularly those with high stakes. A plan is designed to lead to a positive, desired objective. If people considered the possible negative and positive consequences equally, they might find it hard to take any action. Inevitably they veer towards information that confirms the desired positive result, the rosy scenario, without realizing it. We also see this at work when people are supposedly asking for advice. This is the bane of most consultants. In the end, people want to hear their own ideas and preferences confirmed by an expert opinion. They will interpret what you say in light of what they want to hear; and if your advice runs counter to their desires, they will find some way to dismiss your opinion, your so-called expertise. The more powerful the person, the more they are subject to this form of the confirmation bias.

When investigating confirmation bias in the world take a look at theories that seem a little too good to be true. Statistics and studies are trotted out to prove them, which are not very difficult to find, once you are convinced of the rightness of your argument. On the Internet, it is easy to find studies that support both sides of an argument. In general, you should never accept the validity of people’s ideas because they have supplied “evidence.” Instead, examine the evidence yourself in the cold light of day, with as much skepticism as you can muster. Your first impulse should always be to find the evidence that disconfirms your most cherished beliefs and those of others. That is true science.

2) Conviction Bias

I believe in this idea so strongly. It must be true.

We hold on to an idea that is secretly pleasing to us, but deep inside we might have some doubts as to its truth and so we go an extra mile to convince ourselves — to believe in it with great vehemence, and to loudly contradict anyone who challenges us. How can our idea not be true if it brings out of us such energy to defend it, we tell ourselves? This bias is revealed even more clearly in our relationship to leaders — if they express an opinion with heated words and gestures, colorful metaphors and entertaining anecdotes, and a deep well of conviction, it must mean they have examined the idea carefully and therefore express it with such certainty. Those on the other hand who express nuances, whose tone is more hesitant, reveal weakness and self-doubt. They are probably lying, or so we think. This bias makes us prone to salesmen and demagogues who display conviction as a way to convince and deceive. They know that people are hungry for entertainment, so they cloak their half-truths with dramatic effects.

3) Appearance Bias

I understand the people I deal with; I see them just as they are.

We do not see people as they are, but as they appear to us. And these appearances are usually misleading. First, people have trained themselves in social situations to present the front that is appropriate and that will be judged positively. They seem to be in favor of the noblest causes, always presenting themselves as hardworking and conscientious. We take these masks for reality. Second, we are prone to fall for the halo effect — when we see certain negative or positive qualities in a person (social awkwardness, intelligence), other positive or negative qualities are implied that fit with this. People who are good looking generally seem more trustworthy, particularly politicians. If a person is successful, we imagine they are probably also ethical, conscientious and deserving of their good fortune. This obscures the fact that many people who get ahead have done so by doing less than moral actions, which they cleverly disguise from view.

4) The Group Bias

My ideas are my own. I do not listen to the group. I am not a conformist.

We are social animals by nature. The feeling of isolation, of difference from the group, is depressing and terrifying. We experience tremendous relief to find others who think the same way as we do. In fact, we are motivated to take up ideas and opinions because they bring us this relief. We are unaware of this pull and so imagine we have come to certain ideas completely on our own. Look at people that support one party or the other, one ideology — a noticeable orthodoxy or correctness prevails, without anyone saying anything or applying overt pressure. If someone is on the right or the left, their opinions will almost always follow the same direction on dozens of issues, as if by magic, and yet few would ever admit this influence on their thought patterns.

5) The Blame Bias

I learn from my experience and mistakes.

Mistakes and failures elicit the need to explain. We want to learn the lesson and not repeat the experience. But in truth, we do not like to look too closely at what we did; our introspection is limited. Our natural response is to blame others, circumstances, or a momentary lapse of judgment. The reason for this bias is that it is often too painful to look at our mistakes. It calls into question our feelings of superiority. It pokes at our ego. We go through the motions, pretending to reflect on what we did. But with the passage of time, the pleasure principle rises and we forget what small part in the mistake we ascribed to ourselves. Desire and emotion will blind us yet again, and we will repeat exactly the same mistake and go through the same mild recriminating process, followed by forgetfulness, until we die. If people truly learned from their experience, we would find few mistakes in the world, and career paths that ascend ever upward.

6) Superiority Bias

I’m different. I’m more rational than others, more ethical as well.

Few would say this to people in conversation. It sounds arrogant. But in numerous opinion polls and studies, when asked to compare themselves to others, people generally express a variation of this. It’s the equivalent of an optical illusion — we cannot seem to see our faults and irrationalities, only those of others. So, for instance, we’ll easily believe that those in the other political party do not come to their opinions based on rational principles, but those on our side have done so. On the ethical front, few will ever admit that they have resorted to deception or manipulation in their work, or have been clever and strategic in their career advancement. Everything they’ve got, or so they think, comes from natural talent and hard work. But with other people, we are quick to ascribe to them all kinds of Machiavellian tactics. This allows us to justify whatever we do, no matter the results.

We feel a tremendous pull to imagine ourselves as rational, decent, and ethical. These are qualities highly promoted in the culture. To show signs otherwise is to risk great disapproval. If all of this were true — if people were rational and morally superior — the world would be suffused with goodness and peace. We know, however, the reality, and so some people, perhaps all of us, are merely deceiving ourselves. Rationality and ethical qualities must be achieved through awareness and effort. They do not come naturally. They come through a maturation process.

Source : https://medium.com/the-mission/6-biases-holding-you-back-from-rational-thinking-f2eddd35fd0f

Why Olam is Deploying Tech First, Then Thinking About CVC – AgFunder

Why Olam is Deploying Tech First, Then Thinking About CVC

“We have realized that some companies have gone down the wrong path by adopting the approach of inventing the problem. They find a technology that’s exciting and try to force-fit that technology for a problem that they don’t have. This is why we want to be very deliberate about the problems first, and then come to technology.”

Suresh Sundararajan is president and group head of strategic investments and shared services at Olam International, the Singapore-headquartered agribusiness giant. Sundararajan is speaking to AgFunderNews ahead of a speaking slot at the Rethink AgriFood Innovation Week in Singapore later this month.

“I’ll give you an example of blockchain. There’s so much hype about blockchain around the world. And in our industry, there are a few companies that have done some pilots. But we have not gone down that route, because we have not seen a tangible, scalable use case that could give us significant benefits for adopting blockchain.”

If one company could benefit from the efficiencies new technology can bring, it’s Olam, with a complex supply chain that grows, sources, processes, manufactures, transports, trades and markets 47 different agrifood products across 70 countries. These include commodities like coffee, cotton, cocoa, and palm oil that are farmed by over 4 million farmers globally, most of which are smallholders in developing countries.

In-House Tech

But the third largest agribusiness in the world has been noticeably absent from the agrifood corporate venture capital scene in recent years, instead opting mostly to build its own technology solutions in-house. (It did deploy Phytech’s FitBit for crops in Australia in 2016 as an outside example.)

For traceability, and perhaps an alternative to blockchain-enabled technology, there’s Olam AtSource, with a digital dashboard that provides Olam customers with access to rich data, advanced foot-printing, and granular traceability. Olam hopes AtSource will help its customers “meet multiple social and environmental targets thereby increasing resilience in supply chains.”

Olam has also developed and deployed the Olam Farmer Information System (OFIS), a smallholder farm data collection platform providing smallholders with management tools and Olam customers with information about the provenance of products.

“OFIS solves the information issue by providing a revolutionary tech innovation for collecting and analyzing first mile data,” Brayn-Smith told AgFunderNews when OFIS launched in 2017. “We are able to register thousands of smallholders, GPS map their farms and local infrastructure, collect all types of farm gate level data such as the age of trees, and record every training intervention.”

This product is a clear example of a “transformational technology” that solves a problem for Olam and also gives the business efficiencies that could impact the bottom line, according to Sundararajan.

And Olam has built on top of OFIS to transact directly with cocoa farmers in Indonesia where Olam is publishing prices to around 30,000 farmers and buying cocoa directly from them.

“Before technology was available, it was almost impossible for any company to buy directly from the farmers, just because of the sheer volume and number of farmers. But, with technology, you have a far better reach, which will allow us to directly communicate with them,” Sundararajan tells AgFunderNews.

“Now the farmer can just accept a price and type in that he wants to supply it, and we arrange the complete logistics to pick up the cocoa from the farmer,” he says adding that the company’s country heads in other parts of the world are keen to launch this service in their markets. The company is starting next in Peru, then Guatemala, Colombia, Cote d’Ivoire, Ghana, and Nigeria.

Olam as Disruptor

While Olam deployed OFIS to solve for a problem, it also gives the company the opportunity to be disruptive in the markets it serves, according to Sundararajan.

As well as looking for transformational ways to solve specific problems, Olam also looks at “any ideas we have that will give Olam an opportunity to disrupt our own industry. So, we end up being a disrupter and not be at the risk of being disrupted by a new player,” he says.

“This fundamental shift in terms of Olam getting an opportunity to directly interact and transact with farmers is a starting point of disruption for us. This is a very complex point, which will bring into play several technologies for us to be able to successfully scale it.”

Going down this route, Sundararajan says Olam could end up providing farmers with new services and creating “separate streams of revenue that has nothing to do with what we were doing five or 10 years back.”

In this vein, Olam is working on deploying a technology to detect moisture — and therefore quality — in its commodities. The company is also looking at financial tools for its farmers.

“Looking at our business model, we believe that we have a few very good opportunities at the first mile of the supply chain and the last mile of the supply chain to change the way we compete,” says Sundararajan. “We believe that since we have control of the supply chain end-to-end, we can use technology to differentiate our service to customers in a way that our competitors will find difficult to replicate.”

Informal Startup Interactions

Olam does interact with startups on a selective basis, and Sundararajan’s participation in Rethink’s Singapore conference, as well as a hackathon it took part in with Fujitsu in Australia last year, are two examples. Sundararajan said he is considering an idea like The Unilever Foundry, but the company has yet to create a formal process or framework for these interactions. And the same goes for corporate venture capital.

“We believe that our digital journey has to mature much more, where we should demonstrate success within, by implementing the solutions that we’re developing, before even considering investing in venture capital. We believe that we have a very good strategy and a suite of products, stretching across from farm to the factories, to digitize our operations, whether it is a digital buying model, or whether it is spot factories in terms of predictive maintenance or increasing yield or it’s drone imagery from our own plantations, and productivity apps for employees.”

Source : https://agfundernews.com/why-olam-is-deploying-tech-first-then-thinking-about-cvc.html/

Augmented reality , the state of art in the industry- Miscible

Miscible.io attended The Augmented World Expo in Europe / Munich , October 2018, here is my report.

What a great #AWE2018 show in Munich, with a strong focus on the industry usage and, of course , the german automotive industry was well represented. Some new , simple but efficient, AR devices , and plenty of good use cases with a confirmed ROI. This edition was PRAGMATIC.

Here are my six take aways from this edition. Enjoy it !

1 – The return of investment of the AR solutions

The use of XR by automotive companies, big pharma, and teachers confirmed some good ROI with some “ready to use” solutions, especially in this domains :

2 – This is still the firstfruits of AR and some improvements are expected for drawbacks

  • Hardware : field of view, contrast/brigtness , 3D asset resolutions
  • Some AR headset are heavy to wear, it can have some consequences on the operator confort and security.
  • Accuracy between virtual and reality overlay / recognition
  • Automation process from Authoring software to build an end user solution.

3 – Challenge of the Authoring

To create specific and advanced AR Apps, there is still some challenges with the content authoring and with the integration to the legacy systems to retrieve master data and 3D assets. Automotized and integrated AR app need some ingenious developments.

An interesting use case from Boeing ( using hololens to assist the mounting of cables) shows how they did to get an integrated and automatized AR app. Their AR solution architecture in 4 blocks :

  • A web service to design the new AR app (UX and workflow)
  • A call to legacy systems to collect Master Data and 3D data / assets
  • Creation of an integrated Packaged data = asset bundle for the AR
  • Creation of the specific AR app (Vuforia / Unity) , to be transfered to the stand alone system, the Hololens glass.

4 – concept of 3D asset as a master data

The usage of AR and VR becomes more important in many domains : From conception to maintenance and sales (configurator, catalogs …)

The consequence is that original CAD files can be transformed and used in different processes of your company, where it becomes a challenge to use high polygon from CAD applications into other 3D / VR / AR applications, where there is a need of lighter 3D assets, also with some needs of texture and rendering adjustment.

gIFT can be a solution , glTF defines an extensible, common publishing format for 3D content tools and services that streamlines authoring workflows and enables interoperable use of content across the industry.

The main challenge is to implement a good centralised and integrated 3D asset management strategy, considering them as important as your other key master data.

5 – service company and expert to design advanced AR / VR solutions , integrated in the enterprise information system.

The conception of advanced and integrated AR solutions for large companies needs some new expert combining knowlegde in 3D apps and experience in system integration.

This projects need new types of information system architecture taking in account the AR technologies.

PTC looks like a leader in providing efficient and scalable tools for large companies. PTC, owner of Vuforia is also exceling with other 3D / PLM management solutions like windchill , to smoothly integrate 3D management in all the processes and IT of the enterprise.

Sopra Steria , the french IS integration company, is also taking this role , bringing his system integration experience into the new AR /VR usages in the industry.

If you don’t want to invest in this kind of complex projects, for a first step in AR/VR or for some quick wins at a low budget , new content authoring solutions exist to build your AR app with some simple user interfaces and workflows : skylight by Upskill , worklink by Scope AR

6 – The need for an open AR Cloud

“A real time 3D (or spatial) map of the world, the AR cloud, will be the single most important software infrastructure in computing. Far more valuable than facebook social graph, or google page rank index” say Ori Inbar, Co-Founder and CEO of Augmented Reality.ORG. A promising prediction.

The AR cloud provide a persistant , multiuser and cross device AR landscape. It allows people to share experiences and collaborate. The most known AR cloud experience so far is the famous Pokemon Go game.

So far the AR map works using GPS or image recognition, or local point of cloud for a limited space / a building. The dream will be to copy the world as a point of cloud, for a global AR cloud landscape. A real time systems that could be used by robots, drones etc…

The AWE exhibition presented some interesting AR cloud initiative :

  • The Open AR Cloud Initiative launched at the event and had its first working session.
  • Some good SDK are now available to build your own local AR clouds : Wikitude an Immersal

Source : https://www.linkedin.com/pulse/augmented-reality-state-art-industry-fr%C3%A9d%C3%A9ric-niederberger/

 

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