Human Analysts Guarantee Bias

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. doug@knowledgeleaps.com.

Building An Agile Market Research Tool

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 support@knowledgeleaps.com to find out more.

New Feature: Productization of the Production of Data Products

As we work with more closely with our partner company DecaData, we are building tools and features that help bring data products to market and then deliver them to customers.  A lot of this is repetitive process work, making it ideal for automation. Furthermore, if data is the new oil, we need an oil-rig, refinery and pipeline to manage this new commodity.

Our new feature implements these operations. Users can now create automated, time-triggered pipelines that import new data files and then perform a set of customizable operations before delivering them to customers via SFTP or to an AWS S3 bucket.

A RoboCoworker for Analytics

You can have a pretty good guess at someone’s age based on purely on the number of web domains they have purchased and keep up to date. I have 46 and I bought another one, the other day, RoboCoworker.com. I had in mind an automated coworker that could offer a sense of companionship to freelancers and solo start-up founders during their working day. It’s semi-serious and I put these thoughts to one side as I got back to some real work.

Today, I had a call with a prospect for Knowledge Leaps. I gave them a demo and described the use-cases for their industry and role. It dawned on me, that I was describing and automated coworker, a RoboCoworker if you will.

This wouldn’t be someone you can share a joke¬† or discuss work issues with, but would be another member of your analytics team that does the work while you are in meetings, fielding calls from stakeholders, or selling in the findings from the latest analysis. What I call real work that requires real people.