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Data Science Pop-up

Data science leaders and practitioners are on a journey to empower their organizations to become model-driven, solving increasingly complex problems across every facet of business and civil life.

Why Are Data Science Leaders Running for the Exit?

I've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole.

One theme constantly comes up in these conversations: There are a lot of people currently running data science teams at large organizations and the vast majority of them — I believe we are talking about 80-90%— want to leave their jobs.

So what is going on in larger organizations that is causing such a mass exodus?Having worked in and with many large organizations in a data science leadership capacity, I have a few theories.

They were never taught how to run a P&L, manage a team, or deal with people, competitive intel, market assessments, building a business case, etc.

If you’re not willing to give data science managers the training and support they need — or create an organizational structure that brings people with business backgrounds into data science strategy — what will happen is your budget will grow and your results will drop.

PhDs do great work, but the type of work that they excel in isn’t what most companies are asking them to do in this new age of enterprise data science.

The leader of a data science practice needs to focus on data governance, MDM, compliance, legal issues around the use of algorithms, and documentation just in case someone sues for wrongful use.

There are hiring issues and staffing problems to deal with, budget and funding to gain, P&L to run, business cases to build, market research to conduct, vendor meetings to hold, tech life-cycle management, the evangelizing of projects (both internal and external), and turning that work into data products that sell — all this while trying to ensure profit for the company.

When I build a recommendation engine, my cost per unit is pretty much zero, unlike a physical product which certainly has a per unit cost.

A peer-to-peer exchange of leadership, strategy and approach in advancing data science capability Data

Seniors leaders of this new and nascent function now need a professional network to share and benchmark best practices for optimizing value for business. Join

Seniors leaders of this new and nascent function now need a professional network to share and benchmark best practices for optimizing value for business.

Join senior data science leaders at the Data Science Leaders (DSL) East Coast Network in Boston to connect, share learning, solve problems and enhance your management strategy.

Merging Data Science and Business

Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions.

Business people ought to at least understand the different ways that data science is applied, in order to be able to reason about how data science might be used to address some business challenge.

To be more concrete, in a fraud detection scenario we might have: (a) deciding whether a particular account has been defrauded, based on observed account behavior, (b) building a data-driven model that can make those decisions, and (c) assessing the effectiveness of such models.

Both business folk and data scientists (with notable exceptions) generally fall prey to thinking that choosing the right machine learning method is the most important factor–when by and large it is relatively unimportant as compared to formulating the problem well and acquiring/engineering the right data.

It’s good to have at least one person with the specific expertise, but for others I’d recommend looking for smart data scientists with track records showing they can learn new domains.

Rarely do business leaders need to know the technical details of the algorithms, but if the leader is going to be making decisions about investing in data science solutions or talent or platforms, they cannot make truly informed decisions without understanding the fundamental processes involved, how to evaluate appropriately, how to invest in appropriate data assets, and generally what are the different sorts of things that data science can do.

because credit card fraud detection and medicare fraud detection are completely different from the point of view of data science solutions, as are targeting an offer you’ve made before and targeting a brand-new offer.

In order for managers to be involved in making crucial decisions about investing in data science, they need to understand the fundamentals–to ask the right questions and understand the answers.

Tom: We deliberately organized the book around general principles of data science, relying as little as possible on specific current technology, so little needs to be changed.

Regarding new material, my view is that there are a handful of additional topics that either were very relevant, but we chose not to include, or have become very relevant—Tom noted deep learning and causality as two examples.

AI is a very broad field, but when you dig in to what people are talking about now, much of the interest is focused on three areas of AI: (i) drawing business-relevant inferences automatically from data, (ii) machine learning, and (iii) natural language processing (NLP).

Big Data Analytics

Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions.

With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast.

Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

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