On a demo of our application to a prospective customer, the instant feedback was "this looks easier to use than Alteryx". We'll take that sort of compliment any day of the week.
During an interview between Shane Parrish and Daniel Kahneman, one of the many interesting comments made was around how to make better decisions. Kahneman said that despite studying decision-making for many years, he was still prone to his own biases. Knowing about your biases doesn't make them any easier to overcome.
His recommendation to avoid bias in your decision making is to devolve as many decisions as you can to an algorithm. Translating what he is saying to analytical and statistical jobs suggests that no matter how hard we try, we always approach analysis with biases that are hard to overcome. Sometimes our own personal biases are exaggerated by external incentive models. Whether you are evaluating your bosses latest pet idea, or writing a research report for a paying client, delivering the wrong message can be costly, even if it is the right thing to do.
Knowledge Leaps has an answer. We have built two useful tools to overcome human bias in analysis. The first is a narrative builder that can be applied to any dataset to identify the objective narrative captured in the data. Our toolkit can surface a narrative without introducing human biases.
The second tool we built removes bias by using many pairs of eyes to analyze data and average out any potential analytical bias. Instead of a single human (i.e. bias prone analyst) looking at a data set our tool lets lots of people look at it simultaneously and share their individual interpretation of the data. Across many analysts, this tool will remove bias through collaboration and transparency.
Get in touch to learn more. email@example.com.
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For the past five years we have been building our app Knowledge Leaps, an agile market research tool. We use it to power our own business serving some of the most demanding clients on the planet.
To build an innovative market research tool I had leave the industry. I spent 17 years working in market research and experienced an industry that struggled to innovate. There are many reasons why innovation failed to flourish, one of which lies in the fact that it is a service industry. Service businesses are successful when they focus their human effort on revenue generation (as it should be). Since the largest cost base in the research are people, there is no economic incentive to invest in the long term especially as the industry has come under economic pressure in recent years. The same could be said of many service businesses that have been disrupted by technology. Taxi drivers being a good example of this effect.
This wouldn't be the first time market research innovations have come from firms that are outside of the traditional market research category definition. For example, SurveyMonkey was founded by a web developer with no prior market research experience. While, Qualtrics was founded by a business school professor and his son, again with no prior market research industry experience.
Stepping outside of the industry and learning how other types of businesses are managing data, using data and extracting information from it has been enlightening. It has also helped us build an abstracted-solution. While we can focus on market research use-cases, since we have built a platform that fosters analytics collaboration and an open-data philosophy finding new uses for it is a frequent occurrence.
To talk tech-speak what we have done is to productize a service. We have taken the parts of market research process which happen frequently and are expensive and turned them into a product. A product that delivers the story in data with bias. It does it really quickly too. Visit the site or email us firstname.lastname@example.org to find out more.
In a lot of science fiction films one, or more, of the following are true:
- Technology exists that allows you to travel through the universe at the "speed of light."
- Technology that allows autonomous vehicles to navigate complicated 2-D and 3-D worlds exists.
- Technology exists that allows robots to communicate with humans in real-time detecting nuances in language.
- Handheld weapons have been developed that fire bursts of lethal high energy that require little to no charging.
Yet, despite these amazing technological advances the kill ratio is very low. While it is fiction, I find it puzzling that this innovation inconsistency persists in many films and stories.
This is the no-free-lunch theory in action. Machines are developed to be good at a specific task are not good at doing other tasks. This will have ramifications in many areas especially those that require solving multiple challenges. Autonomous vehicles for example need to be good at 3 things:
- Navigating from point A to B
- Complying with road rules and regulations.
- Negotiating position and priority with other vehicles on the road.
- Not killing, or harming, humans and animals.
Of this list 1) and 2) are low level. 3) is challenging to solve as it requires some programmed personality. Imagine if two cars using the same autonomous software meet at a junction at the very same time, one of them needs to give way to the other. This requires some degree of assertiveness to be built. I am not sure this is trivial to solve.
Finally, 4) is probably really hard to solve since it requires 99.99999% success in incidents that occur every million miles. There may never be enough training data.