An extension of the Data Science process – OSEMIC

One of the most famous taxonomies of data science is OSEMN pronounched ‘Awesome’.

It stands for Obtain, Scrub, Explore, Model, Interpret.

I was recently chatting to some data scientists on twitter and they pointed out that shouldn’t it be OSEMIC?

Obtain, Scrub, Explore, Model, Interpret and Communicate!!!

I hadn’t thought of this, but I agree it is part of the process, interpretation by a specialist like myself isn’t the full battle, it needs to be translated into something that business stakeholders can understand. And the challenge is to not lose them with ‘this is the R^2 part’.

I think this ‘last mile’ problem of data science is a real challenge, how do you get something complicated as a Machine Learning model or a differential equation model into something that stakeholders can act on. And I suspect that this is even harder than just learning the mathematics or the programming. I think data scientists can also learn a lot from storytellers such as journalists and designers.

Thanks to everyone who contributed ideas for this post.

The challenge of Data Science

I recently saw this – https://dartthrowingchimp.wordpress.com/2015/03/19/data-science-takes-work-too/ which is basically an article about the workload of Data Science.

This is a personal and opinionated piece, and all my views are my own and do not reflect anyone else’s. Yet I feel strongly as a working Data Analyst that one of the real unseen challenges is communicating or having people communicate the hard work aspect of it. So I welcome articles like this.

I have seen personally the situation where confusion about what a ‘model’ was led to a very difficult work environment for me. These miss-calibrated expectations that it would just be ‘magic’ or like a feature put unrealistic load on me.

Now maybe one of the things that data scientists must do is ‘explain’ the difficulty and the challenge. Today for instance it took me 3 hours to do a relatively simple bar chart – partly because of the difficulty in finding the data and adjusting the axes etc.

This was not an automated, scripted, process this was a bespoke data visualization developed by me to help share with colleagues and stakeholders the story of the current department I am in. And their challenges and key performance indicators.

I think what is often not acknowledged is just how complicated software and data analysis is – it takes an mixture of hard work, domain expertise, data visualization and modelling – and all these things are changing. I’ve built complicated models and reporting that need changed after 3 months because an API or database changes!

So I think we should share more of our challenges, and our frustrations and our success stories. Our success stories should also not be explained as if we are geniuses – we are just humans with rare and valuable skills.

So this should be explained constantly to stakeholders, and perhaps one of the things we can do is to get our colleagues to sit with us through a data analysis project or mini-project. Rather than just barking unrealistic expectations at us :)

I’m still thinking about this, but as Jay says I suspect the biggest problem is that ‘I think most people who don’t do this work simply have no idea.’

Perhaps the lesson here is the following – never underestimate the skill and craft of those you work with, and learn how valuable that is without making lots of assumptions.

A Bayesian Hierarchical model for the Six Nations.

I’m a data scientist and a massive Rugby fan. I recently built a Bayesian model, based on some papers I found on Bayesian models in soccer. The basic idea is to simulate the results of the 6 teams based on historical data, and the model takes into account home advantage. I suggest you read it and I’ll stick the code on github soon.

The sad fact as an Irish fan is that England win the 6 nations in the majority of the cases based on this model and simulation.

Enjoy! I’ll write up a tutorial in the future about this probably but I found this a useful exercise.

You can see the IPython Notebook here

And blog post here http://springcoil.github.io/Bayesian_Model.html

Interview with a Data Expert – Kevin Hillstrom

This interview with is Kevin Hillstrom who I’ve found illuminating since I’ve followed him on Twitter. He’s a analyst who stepped up the corporate ladder a bit and now helps companies with their data strategy and understand their data better. I emailed him a few weeks ago with these interview questions and I’ve lightly edited them.

What I liked about this interview was that Kevin focused on the soft skills – I feel we as a data science community speak too much about the technical skills.

What is the biggest misunderstanding in “big data” and “data science”?

  • To me, it is the “we’re going to save the world with data” mentality. I like the optimism, that’s good! I do not like the hype.


Describe the three most underrated skills of a good analyst and how does an analyst learn them?

  • The first underrated skill is selling. An analyst must learn how to sell ideas. My boss sent me to Dale Carnegie training, a course for sales people. The skills I learned in that class are invaluable.
  • The second underrated skill is accuracy. I work with too many analysts who do all of the “big data” stuff, but then run incorrect queries and, as a result, lose credibility with those they are analyzing data for.
  • The third underrated skill is business knowledge. So many analysts put their heart and soul into analyzing stuff. They could put some of their heart and soul into understanding how their business behaves. Knowledge of the business really influences how one approaches analyzing issues.

How do you clearly explain the context of a data problem to a skeptical stakeholder?

  • · To me, this is where knowledge of the business is really important. So many of my mistakes happened when I cared about the data and the analysis, and did not care enough about the business. I once worked for a retail business that only had twenty-four months of data. That was a big problem, given that the company had been in business for fifty years. Nobody, and I mean nobody, cared. I explained repeatedly how I was unable to perform the work I wanted to perform. Nobody cared. When I shifted my message to what I was able to do for a competing retailer who had ten years of data, then people cared. They cared because their business was not competitive with a business they all knew. Then folks wanted to compete, and we were able to build a new database with many years of data.

What is the best question you’ve ever been asked in your professional career?

  • A high level Vice President once listened to a presentation, and then said to me, “Who cares?” The executive went on to say that I was only sharing trivia. He told me that unless I had facts and information that he could act upon, he didn’t want me to share anything. This is a good lesson. Too often, we share information because we were able to unearth an interesting nugget in the database. But if the information is “nice to know”, it doesn’t help anybody. It is better to share a simple fact that causes people to change than to share interesting facts that nobody can use to improve the business.

What is the best thing – in terms of career acceleration – you’ve ever been told in your professional career?

  • Ask to be promoted to your next job. I had a boss who, in the 9th year of my career, asked me what I wanted to do next? So I told my boss – the job was outside of my area of experience, to be honest, and the job was a major promotion. I described why I wanted the job, I described how I would do the job differently, and I described my vision for how I would make the company more profitable. Within twelve months, I was promoted into the job. My goodness, were people upset! But it was a major lesson. When somebody asks you what you want to do next in your career, be ready to offer a credible answer. Maybe more important, be ready to share your answer even if nobody asks you the question! Tell people what your next job looks like, tell people your vision for that job, tell people how the company benefits, and then do work that proves you are ready for an audacious promotion!

About Kevin: Kevin is President of MineThatData, a consultancy that helps CEOs understand the complex relationship between Customers, Advertising, Products, Brands, and Channels. Kevin supports a diverse set of clients, including internet startups, thirty million dollar catalog merchants, international brands, and billion dollar multichannel retailers. Kevin is frequently quoted in the mainstream media, including the New York Times, Boston Globe, and Forbes Magazine.

Prior to founding MineThatData, Kevin held various roles at leading multichannel brands, including Vice President of Database Marketing at Nordstrom, Director of Circulation at Eddie Bauer, and Manager of Analytical Services at Lands’ End.

Data Science tools and processes

I’ve recently been experimenting with some Data Science tools and methodologies.

The first link is
Data Products how do we get there which discusses what methodologies people in the data science world use. I personally use one not used there called OSEMN – Obtain data, Scrub data, Explore data, Model data, Interpret results. Still the link is interesting. I’ve use CRISP-DM in a project as well, I found CRISP-DM suited a more report based and process based culture, whereas OSEMN allowed you to work in a more agile environment.

One of the challenges I find is finding the right tools to disseminate your ideas. So recently I’ve been learning how to use Flask and Jinja2 (for emails and automated reports) but I also came across an easier solution which is
runipy which can be used for report automation as well. This integrates well into my Ipython reporting workflow, and together with a cron job this could be very powerful. For say if you need to produce regularly a report for a metrics deck or something similar. An advantage of this sort of workflow is that it is reproducible and debuggable.

Python is getting a lot better tooling for these reporting challenges, and a sign that the python stack is getting even better. Unfortunately we’re not quite at Shiny or Kitnr level, but we’re getting there.

Stories in Data Analytics

As a Data Analytics professional I made the same mistake everyone else from a strong STEM background makes when first meeting Non -analysts in a work environment.

I talked about R^2 values. And then I lost my audience….

I am reminded about this because a more experienced analyst tweeted this recently.

From who is @minethedata

1.Early in my career, I created beautiful statistical models. Then I’d present my models to non-analytics staff. Those folks were bored. –

Sexism in Tech conferences

Writing about sexism in tech conferences is hard. Especially as a young white male. I can only speak anecdotally – but most women in the Tech industry I speak to, talk a bit about moments of subtle sexism or sometimes out-and-out harassment. As a member of the tech community I’m completely behind any promotion of minorities in the industry, and feel that more can be done. It is interesting that most men I speak to in the industry don’t notice any problem.

Two articles spring to mind:

http://womeninastronomy.blogspot.com/2014/11/its-not-about-that-damn-shirt.html

This was written about STEM but I feel the same rules apply to the Tech community (especially since I personally straddle both communities).

It’s “not a big deal” when someone tells you he came to your talk because you’re attractive.
It’s “not a big deal” when a coworker comments on your appearance.
It’s “not a big deal” when someone makes a “joke” at work demeaning women.
It’s “not a big deal” when you are asked in a job interview if you have or are planning to have kids.
It’s “not a big deal” that you were warned about what professor to avoid basically as soon as you got to school.
It’s “not a big deal” that that same professor was untouchable by the administration because he was too famous.
It’s “not a big deal” when someone assumes you are your own secretary on the phone.
It’s “not a big deal” when someone calls you “Miss” and your male colleague “Doctor.”
It’s “not a big deal” when going to parties at a conference comes with warnings of which of your fellow scientists are dangerous.
It’s “not a big deal” when your boss, adviser, or senior colleague asks you out.
All of this stuff IS a big deal. One of the things I hear about the tech industry – partly because of the passive agression that Hackers sometimes adopt is that as a community we need to grow up and become more professional AND inclusive. I agree wholeheartedly with this and applaud the conferences that encourage more female participation and more female speakers. Diversity is a good thing and I think it makes us smarter :).
The other link I saw was http://adainitiative.org/2012/08/defcon-why-conference-harassment-matters/ about Defcon a famous security conference. I found the following paragraph to be very powerful.
When you say, “Women shouldn’t go to DEFCON if they don’t like it,” you are saying that women shouldn’t have all of the opportunities that come with attending DEFCON: jobs, education, networking, book contracts, speaking opportunities – or else should be willing to undergo sexual harassment and assault to get access to them. Is that really what you believe?
I am glad things are getting better but there are still a number of actions that we can all take. I think this is a subproblem of the larger problem that Pete Warden commented about. I consider his article to be self-recommending http://petewarden.com/2014/10/05/why-nerd-culture-must-die/
Comments are welcome. The articles I linked to, contain some excellent resources on how to enforce or come up with policies in regards harassment – which is a legal issue. Lots of us like to avoid legal issues like this – but an advantage of policies and ‘processes’ is that they are transparent and fair. Some of us consider these things to be too formal – but as I get older I see that some of these ‘formalities’ that we have in corporations and other organizations are useful and save a lot of hassle.