Why I’ve joined Kensu as Executive Chairman

The next thing in Data Science

© Martin Hack
Data science is … moving from a “wild west” attitude to quickly becoming a crucial part of most Global 2000’s enterprises

I’m excited to announce that I’ve joined the amazingly talented team at Kensu. The team has developed the first of its kind GCP (Governance, Compliance and Performance) solution for data science.

As founding CEO of Skytree we essentially blazed the trail for a whole new era of machine learning and AI startups. Back in 2010/2011 when we hit the fundraising circuit, pretty much the first question from investors was: ”Okay, so what’s machine learning”? Today in 2017 the joke in Silicon Valley is that an .ai domain adds a few millions in valuation. In other words, which startup isn’t doing something with machine learning or AI?

Without a doubt, the advent of ML, AI and Data Science has had a massive impact on our lives over the last couple of years and will continue to do so in the foreseeable future.

Data Science Governance — Why does it matter? Why now?

There are several reasons why data science governance will take off in the very near future:

1. GDPR (European privacy law to be in effect May 1, 2018)
2. Build vs. Buy. Data Governance is highly unlikely to be built in-house
3. “Model-Interpretability” will become a main obstacle for AI with no apparent answer

1.GDPR is the perfect storm of urgency, need and technical complexity. Similar to Y2K and regulations like PCI and HIPAA there’s an actual drop-dead date with draconian fines for non-compliance (4% of annual revenue or €20 Million whatever is higher). Not only does it effect every single corporation within the EU, it also affects a majority of Fortune 500’s (anyone doing business in Europe). While there’s currently little awareness within the US, EU companies with exposure are somewhat in panic mode. Kensu is ideally positioned to provide a one-stop shop for all data science related requirements within GDPR.

Kensu — Adalog, Data Science Governance

2. Build vs buy. In the current eco-system of AI, Machine Learning and Data Science, the in-house do-it-yourselfers are mostly leading the charge today. However, data science or as some call it “decision science”, is also moving from a “wild west” attitude to quickly becoming a crucial part of most Global 2000’s enterprises. As the importance of data science is increasingly recognized, there is a need for software that helps manage the performance of the data science efforts by discovering connections and reporting on KPIs, irrespective of the underlying data science technologies. Virtually anyone using machine learning or AI would want to measure and track efforts. Adalog not only provides a “what’s going on” view of their data science projects but would also monitor past, present and future performance.

Virtually anyone using machine learning or AI would want to measure and track efforts

3. Model Interpretability is already an issue today. The increased use of non-parametric machine learning models and by inclusion every neural (and deep) learning approach have become the de-facto standards for “modern” AI/ML. However, these techniques are entirely “black-box” and usually non-interpretable, meaning humans can not interpret nor understand or follow the decision logic on how a particular answer or result was achieved. This leads to outright banning in some industries[1] or great hesitancy to deploy an otherwise very useful technology such as AI. Since Adalog is “activity” based rather than purely parsing log-files like virtually all other solutions in today’s market, it can offer a “explainer” functionality that will be used by anyone who would like to get an inside view of what their model does.

Best team wins.

If that wasn’t enough. Kensu was founded by data science rockstars, O’Reilly authors and creators of the very popular Spark Notebook — Andy Petrella & Xavier Tordoir. I’ve met them for the first time last year right after they’ve successfully completed their Alchemist cohort and was immediately impressed by their grasp of the market and data science knowledge. We share a common vision and together with an amazing team and I’m very much looking forward to whatever lies ahead.

These things in combination made it a fairly simple decision to join the team, which has already got a great group of supporters and investors like Simon Alexandre @TheFAKTORY. As Executive Chairman I’ll be helping the team to retain focus while collaborating with existing and future users to build on our early lead delivering software addressing critical GCP use cases.

For more check us out at kensu.io

Follow us @mhackster , @kensuio

[1] Fair lending laws in the US makes the use of non-parametric methods for consumer lending and finance difficult to impossible since credit decisions have to be human-reproducible e.g. based on a specific reason code and coefficient.