“Is this the only data you have?” An expression of surprise not uncommonly encountered when evaluating a new opportunity to apply data science. Suitability of available data is a key factor in the abandonment of many otherwise well considered data science initiatives.

"Could the folks who were responsible for the design of the business process and the supporting IT applications not been more forward thinking and captured the more of the relevant data? To make it even worse, for the data that is being captured, the manual entries are not even consistent between the operators."

Well, don't throw up you hands just yet. If you are a relatively newly minted data scientist, you are probably used to data being served to you on a platter! (Kaggle, UCI, Imagenet ..add your favourite platter to the list)

Generally 3 types of challenges are present..

• At one extreme.. They are building a new app. They want to incorporate a recommendation engine. The app is not released ! There is no data, zero, nada, zilch..
• At the other extreme.. I want us to build a up-sell engine. They have a massive database with a huge number of tables. If I just look for revenue related fields, I see 10 different customer revenue fields! Which is the right one to use!!
• The client wants me to build a promotion targeting engine. But they keep changing their offers every month! By the time I have enough data for a promotion, they are ready to kill that promotion move on to some other promotion..
• They want to build a decision support engine. But the available attributes capture only 20-30% of what goes into making the decision. How it this going to be of any help?

Sounds familiar? You are not alone. The speaker using case studies from his own experience will guide the audience on how they can make the best of the situation and deliver a value adding data science solution, or how to decide whether it is more prudent to not pursue it after all.

Data science projects, unlike their software counterparts tend to be uncertain and rarely fit into standardized approach. Each organization has it’s unique processes, tools, culture, data and in-efficiencies and a templatized approach, more common for software implementation projects rarely fits.

In a typical data science project, a data science team is attempting to build a decision support system that will either automate human decision making or assist a human in decision making. The dramatic rise in interest in data sciences means the typical data science project has a large proportion of relatively inexperienced members whose learnings draw heavily from academics, data science competitions and general IT/software projects.

These data scientists learn over time that the real world however is very different from the world of data science competitions. In the real-word problems are ill-defined, data may not exist to start with and it’s not just model accuracy, complexity and performance that matters but also the ease of infusing domain knowledge, interpretability/ability to provide explanations, the level of skill needed to build and maintain it, the stability and robustness of the learning, ease of integration with enterprise systems and ROI.

Human factors play a key role in the success of such projects. Managers making the transition from IT/software delivery to data science frequently do not allow for sufficient uncertainty in outcomes when planning projects. Senior leaders and sponsors, are under pressure to deliver outcomes but are unable to make a realistic assessment of payoffs and risks and set investment and expectations accordingly. This makes the journey and outcome sensitive to various behavioural biases of project stakeholders. Knowing what the typical behavioural biases and pitfalls makes it easier to identify those upfront and take corrective actions.

The speaker brings his nearly two decades of experience working at startups, in R&D and in consulting to lay forth these recurring behavioural biases and pitfalls.

Many of the biases covered are grounded in the speakers first-hand experience. The talk will provide examples of these biases and suggestions on how to identify and overcome or correct for them.