“If every employee hits their goals, will your organization execute its strategy?”
That was the key question in today’s webinar by Donald Sull from MIT’s Sloan School of Management. It’s an incredible insight, as collectively we’ve all embraced goal-setting in the 100 years or so since General Motors popularized the idea.
But does it work? Is there enough transparency corporately to ensure that each contributors goals are in line with the overall strategy, and that if all the individual goals are met that the executive strategy would actually be executed?
Goals have been SMART (Specific, Measurable, Attainable, Realistic, Timely). Dr Sull et. al. present FAST (Frequently Discussed, Ambitious, Specific, Transparent). FAST goals seem more suited to today’s fast-paced high-tech world. They provide the agility needed for organizations to thrive.
Specific and Measurable are redundant, and together lead to Specific.
Attainable and Realistic are redundant, and perhaps wrong, and are replaced with Ambitious.
Timely is modified slightly to become Frequently Discussed, allowing the freedom for the goals to evolve as the needs evolve.
Transparent is missing from SMART, and helps to ensure individual alignment with the big picture.
FAST (Frequently discussed, Ambitious, Specific, Transparent) deserves more attention as businesses struggle to keep pace with innovation.
AI is fascinating, but most organizations don’t have the magnitude of data or the skills to effectively leverage it.
Watching Qlik’s Qonnections 2018 keynote yesterday opened my eyes to a new application of ai. Qlik claimed that Qonnections was the largest data analytics conference in the world. Not sure about that one, but their existing 45,000 customers is hard to argue. Their CEO make some interesting claims, that 10 of the 10 largest pharma companies use Qlik, along with an impressive list of the top 10 companies across several leading industries.
The progression is always like this: data -> analytics -> insights. I’ve heard it many ways from many suppliers, but the two biggest hurdles with data are always the same. Gathering high-quality data is difficult. There are lots of tools for display once clean data is available. Regardless of the quality of the tools, deriving actionable insights is tricky.
It all starts with data literacy and a data-centric culture.
Qlik’s director of research presented their soon to be released “Cognitive Engine”. The highlight of the cognitive engine is their clever use of AI. With Qlik’s new technology, a researcher can visually drill into apparent anomalies in the data, and Cognitive Engine’s AI will proactively identify statistically significant variances worth exploring.
This, to me, was fascinating. It was clearly one of the quickest and easiest ways a data-centric organization could start to leverage AI.
One of the highlights of attending the GPU Technology Conference in San Jose back in March was the keynote address by NVIDIA CEO Jensen Huang. Of course, there was the predictable push to sell more hardware, but notwithstanding the technology demonstrations were truly impressive.
There was an autonomous car in the parking lot. There was a driver in the conference room. Then, on the big screen, was the driver in the holodeck – the simulated environment for the real car out back. The real driver, in the virtual holodeck, seeing real-time data and imagery, drove the virtual car, which in turn drove the physical car safely into a parking spot. It was impressive. The processing power required to do this all real-time is barely imaginable.
But the biggest insight for me came from NVIDIA’s Director of Developer Programs, William Ramey.
AI is not Magic.
With all the mystery around Machine Learning and Deep Learning in particular, it was insightful to hear such a thing. The problem, of course, is that the hype is so great. It’s easy to assume that because this problem is hard, AI must be the answer.
His statement boiled down to this: If X then Y. Can the problem be defined so simply? Can a human understand, at least conceptually, the process? Is there a defined outcome? If all are true, then AI is likely a viable candidate. If a human can’t articulate the process, then don’t expect AI to provide an answer.
Ultimately it boils down to this: Could a human, with a lot of data and a lot of time, solve this same problem? If not, don’t expect magic from AI.
Why is the real world made up of exchange networks rather than markets? In a word: trust.
Relationships in an exchange network quickly become stable (we go back again and again to the person who gives us the best deal), and with stability comes trust, i.e., the expectation of a continued valuable relationship. This is different than in a typical market, where a buyer may deal with a different seller every day as prices fluctuate. In exchange networks, buyers and sellers can more easily build up the trust that makes society resilient in times of great stress. In markets, one must usually rely on having access to an accurate reputation mechanism that rates all the participants, or to an outside referee to enforce the rules.
Social Physics: How Good Ideas Spread, by Alex Penland
Amazon’s webinar today highlighted several ways that Big Data is being used in the HCLS industry. One of the key insights was the self-realization within one of the featured medical practices that they were “data rich, insight poor.” Having tremendous amounts of data isn’t much value to anyone without the appropriate tools and skills to visualize, interact and explore the data, which of course needs to be coupled with people who are empowered to act on the insights revealed.
Several of the tools demonstrated seemed to use Shiny and R to create interactive graphics. This is a perfect combination of developer skills preparing the right kind of visualizations so that self-service data exploration becomes meaningful. So many self-service reporting tools end up being huge time sinks because the fundamental data organization was incomplete or inconsistent.
One of the featured labs highlighted how expenses were reduced and customer satisfaction increased by the appropriate use of dashboards and educated data exploration. They were able to reduce repetitive testing and quickly identify trends in unreimbursed testing. Then with data at their fingertips they were able to discuss the trade-offs between economic and medical goals.
Day Trading 101 is a great introduction into the world of financial trading.
Trading requires a different mindset—one of getting in and getting out at the right times instead of holding until fundamentals change. Since the goal is so different than investing the rules are different. The book makes it very clear that the number one rule in trading is capital preservation.
The book is designed for people without any investing knowledge but is certainly easier to read if you already understand a bit about investing. There are chapters about interdependencies of currency markets, reading candlestick charts, derivatives and futures, commodities, an explanation of support and resistance, and building trade pyramids to minimize risk. All of these topics are approached in a light and easy to comprehend style, though if you really want to get into trading you’ll likely have to do additional reading to begin.
My interest in day trading is really about trading cryptocurrencies based on data, and this book makes no mention of the cryptocurrency market. However, the insights about capital preservation have been critical in our new trading bot. The safety-oriented model built into the bot largely avoided last week’s carnage in the Bitcoin market. ($11,600 on March 5, 2018 to $7331 on 3/18).
If you have any interest in trading and speculation over investing, you should definitely give Day Trading 101 a quick read.
It makes me cringe when I hear people talk about investing in cryptocurrency.
I have been successfully investing for 25+ years. This involves understanding fundamentals, having a general awareness of the economy while committing to a lot of safe principles like asset allocation, dollar cost averaging, long term perspectives and, most importantly, a disciplined selling strategy.
In short, I am an investor, not a trader.
Bitcoin’s recent tumble is largely related to regulatory concerns and Google’s ban on advertising.
I view cryptocurrencies as a massive social experiment with the fundamental value propositions still developing. Clearly there is tremendous hype. Beyond the hype, there are stories of people in high-inflation countries exchanging their local fiat currency for crypto as quickly as possible to preserve value. In a geopolitical culture of growing mistrust of governments and institutions, the immutable and pseudonymous transparency of the blockchain, unable to be stopped by a single entity, is compelling.
Bitcoin, presently, is useless as a currency. Why would someone want to spend $30,000 on a car today when that same car could be worth $50,000, or $17,000, tomorrow? Currencies need stability and trust to be useful. Most cryptocurrencies have neither right now.
However, I do like data, big data, AI, and algorithms. The massive price swings, the changes in sentiment, the lack of fundamentals, the 24×7 trading platform and the involvement of tech all make cryptocurrencies compelling to my inner engineer. There is a tremendous amount of raw data available. It all has to be organized and mined.
The euphoria and incredible volatility create a perfect environment for quants trading.
I decided to programmatically trade in the most reputable cryptocurrencies. The first problem was choosing an exchange, and did I want to hold long positions only, or also short positions and do margin trading?
The reputable cryptocurrency exchanges in the U.S. are all regulated, and, thankfully, that means there is some difficulty in shorting Bitcoin.
Most platforms for margin trading, shorting, and derivative trading of cryptocurrencies are not readily available to U.S. investors. Avatrade, BitMEX, IQ Option and Plus500 appear the be the most reputable, but a foreign bank account is required to trade on these platforms and that’s well beyond the effort level that most people desire.
In the U.S., cryptocurrency margin trading can be done at Bitfinix and LedgerX, though you must be an accredited investor to do so. If you don’t know what that means, you aren’t.
The two safest options for trading cryptocurrency in the U.S. appear to be Coinbase’s GDAX and Kraken. GDAX suspended all margin trading for non-grandfathered accounts in late 2017. Kraken appears to be the only platform in the U.S. for regular people to trade cryptocurrency on margin and has a reputation for excellence along with security, though was plagued with technical problems during Bitcoin’s astronomical December rise (possibly now resolved).
For safety and clear and obvious compliance with U.S regulators, I’ve chosen to stay with GDAX, and forego margin trading until the market stabilizes.
If you are still interested in shorting Bitcoin, check out:
The question didn’t even make sense until I read Mindset: the new psychology of success by Carol S. Dweck, Ph.D. Dweck’s thesis is that internal beliefs about one’s and other’s ability to learn is the key to lifelong success. Do you believe that people are born smart or that high achieving people worked hard to attain their position? Do you believe that with time and effort any person can improve themselves? Do you believe that athletic talent is something you are born with? These types of questions reveal your mindset.
In parenting, praising the outcome instead of praising the effort seems like the natural thing to do. Who wouldn’t be proud of Johnny for his straight A’s, and who wouldn’t call him smart? Who wouldn’t want to brag about Jane’s incredible and natural appetite to devour books well beyond her grade level? However, these things can indirectly convince our children that their success is based on how they were born instead of how they worked. This can inadvertently lead to a “fixed mindset” and a fear to take on new challenges because the limits of their natural abilities may be discovered if they fail. The book offers incredible insights about underachievers and some significant insights into bullying and our nation’s epidemic of school shootings.
The most insightful chapter to me was about leadership in business. Several well-known CEOs were studied to see if they have the growth mindset or the fixed mindset. Fixed mindset CEOs tend to think they are smarter and better than their employees. Growth mindset CEOs recognize that people can grow and learn throughout their careers and that tapping into that potential helps their organization shine. In both cases, the CEOs did quite well for themselves. However, the difference in the organizations is stunning.
America has a wide-spread performance culture. Accomplishments certainly matter but we all need to be careful in how we honor them. True greatness rarely comes from either effort or talent alone, but on a person’s ability to honestly understand their strengths and weaknesses and their willingness to take the effort needed to rise above them, with the belief that they can rise above them. Much of what we celebrate reinforces the idea that people will only be as good as their natural-born abilities allow them to be. This thinking is detrimental to our children, our businesses, our competitors, and our relationships. Changing how we view ourselves and others is paramount to achieving our true potential.
I highly recommend Mindset. This has been the most influential book I’ve read in a decade.
Today I joined IBM’s Machine Learning Everywhere: Build Your Ladder to AI webinar to hear about some of the latest trends in adopting Machine Learning into an organization, and of course plugs for several of IBM’s products in that space.
Rob Thomas is General Manager of Analytics at IBM. His main topic was that there is a ladder that must be climbed for the effective use of Machine Learning and Artificial Intelligence. Per Rob, the technology has finally caught up and “2018 is the year to implement Machine Learning.” Part of that is instilling a data-driven culture.
The first rung is that AI is not possible if your data is not simplified and collected into a data lake. Traditional business analytics with a solid data strategy is essentially the prerequisite for a shift into ML. As part of this, the point was made that AI is perceived is being only for hyper-scaling big players like Uber, Airbnb and Facebook but that recent advancements really make it available to all. An insight that really resonated with me was that 80% of the world’s data is sitting unused and neglected behind corporate firewalls.
The second rung is that data must be accessible. Policy and governance must be in place so that data is at the fingertips of the users that need it most. Of course, there are complications with financial data and HIPPA protected data. The idea is to install the data driven culture by making as much data available to anyone who needs it in a self-service manner.
The top rung is built upon traditional analytics and business intelligence—the actual utilization of Machine Learning in every decision. Once data is widely accessible, decisions based on hunches become less viable and people learn to interact with their data using ever more sophisticated tools. This is the destination of all successful companies in the future: data driven decision making.
Rob mentioned the 40/40/20 rule about companies adopting a data driven culture:
Stage 1: 40% are data aware and are building out incremental efficiencies in their data processing, using both proprietary and open-source tools to create their data lakes. Data collection and data cleansing are the current investments.
Stage 2: 40% are actively examining their data looking for new ways to grow. Regardless of their actual industry, they are becoming a technology company.
Stage 3: 20% are exploring Machine Learning with the intention of being data led. It’s the end of opinions. They are evolving to use ML to automate tasks they never wanted to do in the first place.
Vitaly Tsivin of AMC Networks and Alissa Hess of USAA both shared examples of how Machine Learning and data science are being used to transform their companies from the inside out. However, both noted that the biggest barrier to success is finding talent. The combination of understanding data, understanding technology, and understanding business is rare in a person and difficult to hire.
The webinar ended with a presentation by Garry Kasparov, the first chess grand master to be defeated by a computer, and his opinions on the limitations and direction of artificial intelligence.
Technostology 