Socially Responsible Algorithms at Data Science DC

Socially Responsible Algorithms at Data Science DC

Troubling instances of the mosaic effect — in which different anonymized datasets are combined to reveal unintended details — include the tracking of celebrity cab trips and the identification of Netflix user profiles. Also concerning is the tremendous influence wielded by corporations and their massive data stores, most notoriously embodied by Facebook’s secret psychological experiments

The future of social science research: A data science perspective

The future of social science research: A data science perspective

Last week, the Urban Institute hosted a discussion on the evolving landscape of data and the potential impact on social science. “Machine Learning in a Data-Driven World” covered a wide range of important issues around data science in academic research and their real policy applications. Above all else, one critical narrative emerged:

Data are changing, and we can use these data for social good, but only if we are willing to adapt to new tools and emerging methods.

Thoughts on the INFORMS Business Analytics Conference

This post, from DC2 President Harlan Harris, was originally published on his blog. Harlan was on the board of WINFORMS, the local chapter of the Operations Research professional society, from 2012 until this summer. Earlier this year, I attended the INFORMS Conference on Business Analytics & Operations Research, in Boston. I was asked beforehand if I wanted to be a conference blogger, and for some reason I said I would. This meant I was able to publish posts on the conference's WordPress web site, and was also obliged to do so!

Here are the five posts that I wrote, along with an excerpt from each. Please click through to read the full pieces:

Operations Research, from the point of view of Data Science

  • more insight, less action — deliverables tend towards predictions and storytelling, versus formal optimization
  • more openness, less big iron — open source software leads to a low-cost, highly flexible approach
  • more scruffy, less neat — data science technologies often come from black-box statistical models, vs. domain-based theory
  • more velocity, smaller projects — a hundred $10K projects beats one $1M project
  • more science, less engineering — both practitioners and methods have different backgrounds
  • more hipsters, less suits — stronger connections to the tech industry than to the boardroom
  • more rockstars, less teams — one person can now (roughly) do everything, in simple cases, for better or worse

What is a “Data Product”?

DJ Patil says “a data product is a product that facilitates an end goal through the use of data.” So, it’s not just an analysis, or a recommendation to executives, or an insight that leads to an improvement to a business process. It’s a visible component of a system. LinkedIn’s People You May Know is viewed by many millions of customers, and it’s based on the complex interactions of the customers themselves.

Healthcare (and not Education) at INFORMS Analytics

[A]s a DC resident, we often hear of “Healthcare and Education” as a linked pair of industries. Both are systems focused on social good, with intertwined government, nonprofit, and for-profit entities, highly distributed management, and (reportedly) huge opportunities for improvement. Aside from MIT Leaders for Global Operations winning the Smith Prize (and a number of shoutouts to academic partners and mentors), there was not a peep from the education sector at tonight’s awards ceremony. Is education, and particularly K-12 and postsecondary education, not amenable to OR techniques or solutions?

What’s Changed at the Practice/Analytics Conference?

In 2011, almost every talk seemed to me to be from a Fortune 500 company, or a large nonprofit, or a consulting firm advising a Fortune 500 company or a large nonprofit. Entrepeneurship around analytics was barely to be seen. This year, there are at least a few talks about Hadoop and iPhone apps and more. Has the cost of deploying advanced analytics substantially dropped?

Why OR/Analytics People Need to Know About Database Technology

It’s worthwhile learning a bit about databases, even if you have no decision-making authority in your organization, and don’t feel like becoming a database administrator (good call). But by getting involved early in the data-collection process, when IT folks are sitting around a table arguing about platform questions, you can get a word in occasionally about the things that matter for analytics — collecting all the data, storing it in a way friendly to later analytics, and so forth.

All in all, I enjoyed blogging the conference, and recommend the practice to others! It's a great way to organize your thoughts and to summarize and synthesize your experiences.

Elements of an Analytics "Education"

This a guest post by Wen Phan, who will be completing a Master of Science in Business at George Washington University (GWU) School of Business.  Wen is the recipient of the GWU Business Analytics Award for Excellence and Chair of the Business Analytics Symposium, a full-day symposium on business analytics on Friday, May 30th -- all are invited to attend. Follow Wen on Twitter @wenphan.

GWU Business Analytics Symposium, 5/30/14, Marvin CenterWe have read the infamous McKinsey report. There is the estimated 140,000- to 190,000-person shortage of deep analytic talent by 2018, and an even bigger need - 1.5 million professionals - for those who can manage and consume analytical content. Justin Timberlake brought sexy back in 2006, but it’ll be the data scientist that will bring sexy to the 21st century. While data scientists are arguably the poster child of this most recent data hype, savvy data professionals are really required across many levels and functions of an organization. Consequently, a number of new and specialized advanced degree programs in data and analytics have emerged over the past several years – many of which are not housed in the traditional analytical departments, such as statistics, computer science or math. These programs are becoming increasingly competitive and graduates of these programs are skilled and in demand. For many just completing their undergraduate degrees or with just a few years of experience, these data degrees have become a viable option in developing skills and connections for a burgeoning industry. For others with several years of experience in adjacent fields, such as myself, such educational opportunities provide a way to help with career transitions and advancement.

I came back to school after having worked for a little over a decade. My undergraduate degree is in electrical engineering and at one point in my career, I worked on some of the most advanced microchips in the world. But I also have experience in operations, software engineering, product management, and marketing. Through it all, I have learned about the art and science of designing and delivering technology and products from ground zero - both from technical and business perspectives. My decision to leave a comfortable, well-paid job to return to school was made in order to leverage my technical and business experience in new ways and gain new skills and experiences to increase my ability to make an impact in organizations.

There are many opinions regarding what is important in an analytics education and just as many options to pursuing them, each with their own merits. Given that, I do believe there are a few competencies that should be developed no matter what educational path one takes, whether it is graduate school, MOOCs, or self-learning. What I offer here are some personal thoughts on these considerations based on my own background, previous professional experiences, and recent educational endeavor with analytics and, more broadly, using technology and problem solving to advance organizational goals.

Not just stats.

For many, analytics is about statistics and a data degree is just slightly different from a statistics one. There is no doubt that statistics plays a major role in analytics, but it is still just one of the technical skills. If you are a serious direct handler of data of any kind, it will be obvious that programming chops are almost a must. For more customized and sophisticated processing, even substantial computer science knowledge – data structures, algorithms, and design patterns – will be required. Of course, even this idea has been pretty mainstream and is nicely captured by Drew Conway’s Data Science Venn Diagram. Other areas not as obvious to data competency are that of data storage theory and implementation (e.g. relational databases and data warehouses), operations research, and decision analysis. The computer science and statistics portions really focus on the sexy predictive modeling aspects of data. That said, knowing how to effectively collect and store data upstream is tremendously valuable. After all, it is often the case that data extends beyond just one analysis or model. Data begets more data (e.g. data gravity). Many of the underlying statistical methods, such as maximum likelihood estimation (MLE), neural networks and support vector machines, all rely on principles and techniques of operations research. Further, operations research, also called optimization, offers a prescriptive perspective on analytics. Last, it is obvious that analytics can help identify trends, understand customers, and forecast the future. However, in and of themselves those activities do not add any value; it is the decisions and resulting actions taken on those activities that deliver value. But, often, these decisions must be made in the face of substantial uncertainty and risk - hence the importance of critical decision analysis. The level of expertise required in various technical domains must align with your professional goals, but a basic knowledge of the above should allow you adequate fluency across analytics activities.


I consider analytics an applied degree similar to how engineering is an applied degree. Engineering applies math and science to solve problems. Analytics is similar this way. One importance of applied fields is that they are where the rubber of theory needs to meet the road of reality. Data is not always normally distributed. In fact data is not always valid or even consistent. Formal education offers rigor in developing strong foundational knowledge and skills. However, just as important are the skills to deal with reality. It is no myth that 80% of analytics is just about pre-processing the data; I call it dealing with reality. It is important to understand the theory behind the models, and frankly, it’s pretty fun to indulge in the intricacies of machine learning and convex optimization. In the end though, those things have been made relatively straightforward to implement with computers. What hasn’t (yet) been nicely encapsulated in computer software is the judgment and skill required to handle the ugliness of real-world data. You know what else is reality? Teammates, communication, and project management constraints. All this is to say that so much of an analytics education includes other areas that are not the theory, and I would argue that the success of many analytics endeavors are limited not by the theoretical knowledge, but rather by the practicalities of implementation whether with data, machines, or people. My personal recommendation to aspiring or budding data geeks is to cut your teeth as much as possible in dealing with reality. Do projects. As many of them as possible. With real data. And real stakeholders. And, for those of you manager types, give it a try; it’ll give you the empathy and perspective to effectively work with the hardcore data scientists and manage the analytics process.

Working with complexity and ambiguity.

The funny thing about data is that you have problems both when you have too little and too much of it. With too little data, you are often making inferences and assessing the confidence of those inferences. With too much data, you are trying not to get confused. In the best case scenarios, your objectives in mining the data are straightforward and crystal clear. However, that is often not the case and exploration is required. Navigating this process of exploration and value discovery can be complex and ambiguous. There are the questions of “where do I start?” and “how far do I go?” This really speaks to the art of working with data. You pick up best practices along the way and develop some of your own. Initial exploration tactics may be as simple as profiling all attributes and computing correlations among a few of thing, seeing if anything looks promising or sticks. This process is further exacerbated with “big data”, where computational time is non-negligible and limits feedback delays during any kind of exploratory data analysis.

You can search the web for all kinds of advice on skills to develop for a data career. The few tidbits I include above are just my perspectives on some of the higher order bits in developing solid data skills. Advanced degree programs offer compelling environments to build these skills and gain exposure in an efficient way, including a professional network, resources, and opportunities. However, it is not the only way. As with all professional endeavors, one needs to assess his or her goals, background, and situation to ultimately determine the educational path that makes sense.


[1] James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers. “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” McKinsey Global Institute. June 2011.

[2] Thomas H. Davenport, D.J. Patil . “Data Scientist: The Sexiest Job of the 21st Century.” Harvard Business Review. October 2012.

[3] Quentin Hardy. "What Are the Odds That Stats Would Be This Popular?" The New York Times. January 26, 2012. 

[4] Patrick Thibodeau. “Career alert: A Master of analytics degree is the ticket – if you can get into class”. Computerworld. April 24, 2014. 

[5] Drew Conway. “The Data Science Venn Diagram.” 

[6] Kristin P. Bennett, Emilio Parrado-Hernandez. “The Interplay of Optimization and Machine Learning Research.” Journal of Machine Learning Research 7. 2006. 

[7] Mousumi Ghosh. “7 Key Skills of Effective Data Scientists.” Data Science Central. March 14, 2014.

[8] Anmol Rajpurohit. “Is Data Scientist the right career path for you? Candid advice.” KDnuggets. March 27, 2014. 


Where are the Deep Learning Courses?

This is a guest post by John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. He presented an introduction to Deep Learning at the March Data Science DC.

Why aren't there more Deep Learning talks, tutorials, or workshops in DC2?

It's been about two months since my Deep Learning talk at Artisphere for DC2. Again, thanks to the organizers (especially Harlan Harris and Sean Gonzalez) and the sponsors (especially Arlington Economic Development). We had a great turnout and a lot of good questions that night. Since the talk and at other Meetups since, I've been encouraged by the tidal wave of interest from teaching organizations and prospective students alike.

First some preemptive answers to the “FAQ” downstream of the talk:

  • Mary Galvin wrote a blog review of this event.
  • Yes, the slides are available.
  • Yes, corresponding audio is also available (thanks Geoff Moes).
  • A recently "reconstructed" talk combining the slides and audio is also now available!
  • Where else can I learn more about Deep Learning as a data scientist? (This may be a request to teach, a question about how to do something in Deep Learning, a question about theory, or a request to do an internship. They're all basically the same thing.)
  • It's this last question that's the focus of this blog post. Lots of people have asked and there are some answers out there already, but if people in the DC MSA are really interested, there could be more. At the end of this post is a survey—if you want more Deep Learning, let DC2 know what you want and together we'll figure out what we can make happen.

There actually was a class...

Aaron Schumacher and Tommy Shen invited me to come talk in April for General's Data Science course. I did teach one Deep Learning module for them. That module was a slightly longer version of the talk I gave at Artisphere combined with one abbreviated “hands on” module on unsupervised feature learning based on Stanford's tutorial. It didn't help that the tutorial was written in Octave and the class had mostly been using Python up to that point. Though feedback was generally positive for the Deep Learning module, some students wondered if they could get a little more hands on and focus on specifics. And I empathize with them. I've spent real money on Deep Learning tutorials that I thought could have been much more useful if they were more hands on.

Though I've appreciated all the invitations to teach courses, workshops, or lectures, except for the General course, I've turned down all the invitations to teach something more on Deep Learning. This is not because the data science community here in DC is already expert in Deep Learning or because it's not worth teaching. Quite the opposite. I've not committed to teach more Deep Learning mostly because of these three reasons:

  1. There are already significant Deep Learning Tutorial resources out there,
  2. There are significant front end investments that neophytes need to make for any workshop or tutorial to be valuable to both the class and instructor and,
  3. I haven't found a teaching model in the DC MSA that convinces me teaching a “traditional” class in the formal sense is a better investment of time than instruction through project-based learning on research work contracted through my company.

Resources to learn Deep Learning

There are already many freely available resources to learn the theory of Deep Learning, and it's made even more accessible by many of the very lucid authors who participate in this community. My talk was cherry-picked from a number of these materials and news stories. Here are some representative links that can connect you to much of the mainstream literature and discussion in Deep Learning:

  • The tutorials link on the page
  • NYU's Deep Learning course course material
  • Yann LeCun's overview of Deep Learning with Marc'Aurelio Ranzato
  • Geoff Hinton's Coursera course on Neural Networks
  • A book on Deep Learning from the Microsoft Speech Group
  • A reading list list from Carnegie Mellon with student notes on many of the papers
  • A Google+ page on Deep Learning

This is the first reason I don't think it's all that valuable for DC to have more of its own Deep Learning “academic” tutorials. And by “academic” I mean tutorials that don't end with students leaving the class successfully implementing systems that learn representations to do amazing things with those learned features. I'm happy to give tutorials in that “academic” direction or shape them based on my own biases, but I doubt I'd improve on what's already out there. I've been doing machine learning for 15 years, so I start with some background to deeply appreciate Deep Learning, but I've only been doing Deep Learning for two years now. And my expertise is self-taught. And I never did a post-doc with Geoff Hinton, Yann LeCun or Yoshua Bengio. I'm still learning, myself.

The investments to go from 0 to Deep Learning

It's a joy to teach motivated students who come equipped with all the prerequisites for really mastering a subject. That said, teaching a less equipped, uninvested and/or unmotivated studentry is often an exercise in joint suffering for both students and instructor.

I believe the requests to have a Deep Learning course, tutorial, workshop or another talk are all well intentioned... Except for Sean Gonzalez—it creeps me out how much he wants a workshop. But I think most of this eager interest in tutorials overlooks just how much preparation a student needs to get a good return on their time and tuition. And if they're not getting a good return, what's the point? The last thing I want to do is give the DC2 community a tutorial on “the Past” of neural nets. Here are what I consider some practical prerequisites for folks to really get something out of a hands-on tutorial:

  • An understanding of machine learning, including
    • optimization and stochastic gradient descent
    • hyperparameter tuning
    • bagging
    • at least a passing understanding of neural nets
  • A pretty good grasp of Python, including
    • a working knowledge of how to configure different packages
    • some appreciation for Theano (warts and all)
    • a good understanding of data preparation
  • Some recent CUDA-capable NVIDIA GPU hardware* configured for your machine
    • CUDA drivers
    • NVIDIA's CUDA examples compiled

*hardware isn't necessarily a prerequisite, but I don't know how you can get an understanding of any more than toy problems on a CPU

Resources like the ones above are great for getting a student up to speed on the “academic” issues of understanding deep learning, but that only scratches the surface. Once students know what can be done, if they’re anything like me, they want to be able to do it. And at that point, students need a pretty deep understanding of not just the theory, but of both hardware and software to really make some contributions in Deep Learning. Or even apply it to their problem.

Starting with the hardware, let's say, for sake of argument, that you work for the government or are for some other arbitrary reason forced to buy Dell hardware. You begin your journey justifying the $4000 purchase for a machine that might be semi-functional as a Deep Learning platform when there's a $2500 guideline in your department. Individual Dell workstations are like Deep Learning kryptonite, so even if someone in the n layers of approval bureaucracy somehow approved it, it's still the beginning of a frustrating story with an unhappy ending. Or let's say you build your own machine. Now add “building a machine” for a minimum of about $1500 to the prerequisites. But to really get a return in the sweet spot of those components, you probably want to spend at least $2500. Now the prerequisites include a dollar investment in addition to talent and tuition! Or let’s say you’re just going to build out your three-year-old machine you have for the new capability. Oh, you only have a 500W power supply? Lucky you! You’re going shopping! Oh, your machine has an ATI graphics card. I’m sure it’s just a little bit of glue code to repurpose CUDA calls to OpenCL calls for that hardware. Let's say you actually have an NVIDIA card (at least as recent as a GTX 580) and wanted to develop in virtual machines, so you need PCI pass-through to reach the CUDA cores. Lucky you! You have some more reading to do! Pray DenverCoder9's made a summary post in the past 11 years.

“But I run everything in the cloud on EC2,” you say! It's $0.65/hour for G2 instances. And those are the cheap GPU instances. Back of the envelope, it took a week of churning through 1.2 million training images with CUDA convnets (optimized for speed) to produce a breakthrough result. At $0.65/hour, you get maybe 20 or 30 tries doing that before it would have made more sense to have built your own machine. This isn't a crazy way to learn, but any psychological disincentive to experimentation, even $0.65/hour, seems like an unnecessary distraction. I also can't endorse the idea of “dabbling” in Deep Learning; it seems akin to “dabbling” in having children—you either make the commitment or you don't.

At this point, I’m not aware of an “import deeplearning” package in Python that can then fit a nine layer sparse autoencoder with invisible CUDA calls to your GPU on 10 million images at the ipython command line. Though people are trying. That's an extreme example, but in general, you need a flexible, stable codebase to even experiment at a useful scale—and that's really what we data scientists should be doing. Toys are fine and all, but if scale up means a qualitatively different solution, why learn the toy? And that means getting acquainted with the pros and cons of various codebases out there. Or writing your own, which... Good luck!

DC Metro-area teaching models

I start from the premise that no good teacher in the history of teaching has ever been rewarded appropriately with pay for their contributions and most teaching rewards are personal. I accept that premise. And this is all I really ever expect from teaching. I do, however, believe teaching is becoming even less attractive to good teachers every year at every stage of lifelong learning. Traditional post-secondary instructional models are clearly collapsing. Brick and mortar university degrees often trap graduates in debt at the same time the universities have already outsourced their actual teaching mission to low-cost adjunct staff and diverted funds to marketing curricula rather than teaching them. For-profit institutions are even worse. Compensation for a career in public education has never been particularly attractive, but still there have always been teachers who love to teach, are good at it, and do it anyway. However, new narrow metric-based approaches that hold teachers responsible for the students they're dealt rather than the quality of their teaching can be demoralizing for even the most self-possessed teachers. These developments threaten to reduce that pool of quality teachers to a sparse band of marginalized die-hards. But enough of my view of “teaching” the way most people typically blindly suggest I do it. The formal and informal teaching options in the DC MSA mirror these broader developments. I run a company with active contracts and however much I might love teaching and would like to see a well-trained crop of deep learning experts in the region, the investment doesn't add up. So I continue to mentor colleagues and partners through contracted research projects.

I don't know all the models for teaching and haven't spent a lot of time understanding them, but none seem to make sense to me in terms of time invested to teach students—partly because many of them really can't get at the hardware part of the list of prerequisites above. This is my vague understanding of compensation models generally available in the online space*:

  • Udemy – produce and own a "digital asset" of the course content and sell tuition and advertising as a MOOC. I have no experience with Udemy, but some people seemed happy to have made $20,000 in a month. Thanks to Valerie at Feastie for suggesting this option.
  • – Typically a few thousand for four sessions that then sells; I believe this must be a “work for hire” copyright model for the digital asset that buys from the instructor. I assume it's something akin to commissioned art, that once you create, you no longer own. [Editor’s note: is a sponsor of Data Science DC. The arrangement that John describes is similar to our understanding too.]
  • Myngle – Sell lots of online lessons for typically less than a 30% share.

And this is my understanding of compensation models locally available in the DC MSA*:

  • General – Between 15-20% of tuition (where tuition may be $4000/student for a semester class).
  • District Data Labs Workshop – Splits total workshop tuition or profit 50% with the instructor—which may be the best deal I've heard, but 50% is a lot to pay for advertising and logistics. [Editor's note: These are the workshops that Data Community DC runs with our partner DDL.]
  • Give a lecture – typically a one time lecture with a modest honorarium ($100s) that may include travel. I've given these kinds of lectures at GMU and Marymount.
  • Adjunct at a local university – This is often a very labor- and commute-intensive investment and pays no better (with no benefits) than a few thousand dollars. Georgetown will pay about $200 per contact hour with students. Assuming there are three hours of out of classroom commitment for every hour in class, this probably ends up somewhere in the $50 per hour range. All this said, this was the suggestion of a respected entrepreneur in the DC region.
  • Tenure-track position at a local university – As an Assistant Professor, you will typically have to forego being anything but a glorified post-doc until your tenure review. And good luck convincing this crowd they need you enough to hire you with tenure.

*These are what I understand to be the approximate options and if you got a worse or better deal, please understand I might be wrong about these specific figures. I'm not wrong, though, that none of these are “market rate” for an experienced data scientist in the DC MSA.

Currently, all of my teaching happens through hands-on internships and project-based learning at my company, where I know the students (i.e. my colleagues, coworkers, subcontractors and partners) are motivated and I know they have sufficient resources to succeed (including hardware). When I “teach,” I typically do it for free, and I try hard to avoid organizations that create asymmetrical relationships with their instructors or sell instructor time as their primary “product” at a steep discount to the instructor compensation. Though polemic, Mike Selik summarized the same issue of cut rate data science in "The End of Kaggle." I'd love to hear of a good model where students could really get the three practical prerequisites for Deep Learning and how I could help make that happen here in DC2 short of making “teaching” my primary vocation. If there's a viable model for that out there, please let me know. If you still think you'd like to learn more about Deep Learning through DC2, please help us understand what you'd want out of it and whether you'd be able to bring your own hardware.

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The Evils of Git

It would appear to many that Git and, as a result, GitHub, have taken over the world of code versioning. Having used git substantially more this past year, I can say that I am not blown away by all aspects of this powerful tool and it would appear that at least some would agree. If you are interested in reading an excellent rant about the evils of git, I highly recommend the following article. Enjoy!

Keep it Simple: How to Build a Successful Business Intelligence/Data Warehouse Architecture

Is your data warehouse architecture starting to look like a Rube Goldberg machine held together with duct tape instead of the elegant solution enabling data driven decision making that you envisioned? If your organization is anything like the ones I’ve worked in, then I suspect it might. Many businesses say they recognize that data is an asset, but when it comes to implementing solutions, the focus on providing business value is quickly lost as technical complexities pile up.


How can you recognize if your data warehouse is getting too complicated?

Does it have multiple layers that capture the same data in just a slightly different way? An organization I worked with determined that they needed 4 database layers (staging, long term staging, enterprise data warehouse, and data marts) with significant amounts of duplication. The duplication resulted from each layer not having a clear purpose, but even with more clarity on purpose, this architecture makes adding, changing and maintaining data harder at every turn.

Are you using technologies just because you have used them in the past? Or thought they would be cool to try out? An organization I worked with implemented a fantastically simple data warehouse star schema ( with well under 500 GB of data. Unfortunately, they decided to complicate the data warehouse by adding a semantic layer to support a BI tool and an OLAP cube (which is some ways was a second semantic layer to support BI tools). There is nothing wrong with semantic layers or OLAP cubes. In fact, there are many valid reasons to use them. But, if you do not have said valid reason, they become just another piece of the data architecture that requires maintenance. Has someone asked for data that “should” be easy to get, but instead will take weeks of dedicated effort to pull together? I frequently encounter requests that sound simple, but the number of underlying systems involved and the lack of consistent data integration practices expands the scope exponentially.

Before I bring up too many bad memories of technical complexities taking over a BI/DW project, I want to get into what to do to avoid making things overcomplicated. The most important thing is to find a way that works for your organization to stay focused on business value.

If you find yourself thinking…

“The data is in staging, but we need to transform it into the operational data store, enterprise data warehouse and update 5 data marts before anyone can access the data.”


“I am going to try to because I want to learn more about it.”


“I keep having to pull together the customer data and it takes 2 weeks just to get an approved list of all customers.”

Stop, drop and roll, oh wait, you’re not technically on fire, so just stopping should do. Take some time to consider how to reset so that the focus is on providing business value. You might try using an approach such as the 5 Whys which was developed for root cause analysis by Sakichi Toyoda for Toyota . It forces reflection on a specific problem and helps you drill down into the specific cause. Why not try it out to see if you can find the root cause of complexity in a BI/DW project? It might just help you reduce or eliminate complexities when there is no good reason for the complexity in the first place.

Another suggestion is to identify areas of complexity from a technical perspective, but don’t stop there. The crucial next step is to determine how the complex technical environment impacts business users. For example, a technical team identifies two complex ETL processes for loading sales and HR data. Both include one off logic and processes that make it difficult to discern what is going on so it takes hours to troubleshoot issues that arise. In addition, the performance of both ETL processes has significantly degraded. The business users don’t really care about all that, but they have been complaining more and more about delays in getting the latest sales figures. When you connect the growing complexity to the delays in getting important data, the business users can productively contribute to a discussion on priority and business value. In this case, sales data would take clear precedence over HR data. Both can be added to a backlog, along with any other areas of complexity identified, and addressed in priority order.

Neither of these is a quick fix, but even slowly chipping away at overly complex areas will yield immediate benefits. Each simplification makes understanding, maintaining and extending the existing system easier.


Sara_Handel Sara Handel, Co-founder of DC Business Intelligentsia, Business Intelligence Lead at Excella Consulting ( - I love working on projects that transform data into clear, meaningful information that can help business leaders shape their strategies and make better decisions. My systems engineering education coupled with my expertise in Business Intelligence (BI) allows me to help clients find ways to maximize their systems' data capabilities from requirements through long term maintenance. I co-founded the DC Business Intelligentsia meetup to foster a community of people interested in contributing to the better use of data.

Political Tech: Predicting 2016 Headlines

Mark Stephenson is a Founding Partner at Cardinal Insights, a data analysis, modeling and strategy firm.  Cardinal Insights provides accessible and powerful data targeting tools to Republican campaigns and causes of all sizes.  Twitter:  @markjstephenson

The reliance on data in politics comes as no surprise to those who watch trends in technology.  Business and corporate entities have been making major investments in data analysis, warehousing and processing for decades, as have both major political parties.  As the strategic, tactical and demographic winds shift for political operatives, so too has the need to become more effective at building high quality datasets with robust analysis efforts.

Recent efforts by both Republican and Democrat organizations to outpace each other in the analytical race to the top have been well documented by the press[1].  With the 2016 Presidential election cycle already underway (yes...really), I decided to make some headline predictions for what we will see after our next President is elected, as it relates to data, technology and organizational shifts over the next three years.


"Data Crunchers Analyze Their Way Into the White House"


A similar version of the headlines we saw in 2012, the growth in the reliance and seniority of data science staff will continue.  Senior members of both party's Committee and Presidential campaign staff will be technologists (this is already happening), and data science will be integrated in all aspects of those campaigns (ie. fundraising, political, digital, etc.).


"Digital Targeting Dominates the Targeting Playbook"


Studies continue to show shifts in how voters consume advertising content, including political messaging.  Television still remains a core tactical tool, but voters of all ages are increasingly unplugged from traditional methods[2].  One-to-one data and digital targeting will grow in the scope and budget it receives and both vendors and campaigns will shift tactical applications to respond to demands.


"Scaled Data: State Races Take Advantage of National Tools"


In 2016, not only will national, big budget races use data and analytics to glean insights, but these tools will scale to lower level, state-based campaigns.  Along with more widely available, cheap (even free) technology, companies like Cardinal Insights and efforts like "Project Ivy"[3] are turning what used to be expensive and time-consuming data analysis into scalable, accessible products.  These will have lasting efforts on the profile of many state House and Senate legislatures and as a result, state and local political outcomes.


"Business Takes Notice: Political Data Wizards Shift Corporate Efforts"


Just as many political operatives took skills learned and applied during the 2012 election and focused them on entrepreneurship, the same will happen to a higher degree after 2016.  Innovators in the political data, digital and television spaces will prove the effectiveness of these new tools and as a result, corporate marketing and advertising will seek them out.


"Shift from "The Gut" to "The Numbers" for Decision Making and Targeting"


Many decisions made by political operatives in the past were made from the gut:  their intuition told them that a certain choice was the right one, not necessarily a proven method, backed by data.  In 2016, there will continue to be a dynamic shift towards data-driven efforts throughout campaigns, with an emphasis on testing, metrics and fact-based decision making.  This will permeate all divisions of campaigns, from fundraising to operations to political decisions.

Just as companies like Amazon, Coca Cola and Ford build massive data and analysis infrastructures to capitalize on sales opportunities, political campaigns will do the same to capitalize on persuading voters.  As trends in data analysis, targeting, statistical modeling and technology continue to reveal themselves, you will read many headlines in late November 2016 that are similar to the ones above.  Keep an eye on the press to see what campaigns do in 2014 and watch the growth of a booming analytical industry continue to distill itself throughout American politics.





Will big data bring a return of sampling statistics? And a review of Aaron Strauss's talk at DSDC

This guest post by Tommy Jones was originally published on Biased Estimates. Tommy is a statistician or data scientist -- depending on the context -- in Washington, DC. He is a graduate of Georgetown's MS program for mathematics and statistics. Follow him on Twitter @thos_jones.

Some Background

What is sampling statistics?

Sampling statistics concerns the planning, collection, and analysis of survey data. When most people take a statistics course, they are learning "model-based" statistics. (Model-based statistics is not the same as statistical modeling, stick with me here.) Model-based statistics uses a mathematical function to model the distribution of an infinitely-sized population to quantify uncertainty. Sampling statistics, however, uses a priori knowledge of the size of the target population to inform quantifying uncertainty. The big lesson I learned after taking survey sampling is that if you assume the correct model, then the two statistical philosophies agree. But if your assumed model is wrong, the two approaches give different results. (And one approach has fewer assumptions, bee tee dubs.)
Sampling statistics also has a big bag of other tricks, too many to do justice here. But it provides frameworks for handling missing or biased data, combining data on subpopulations whose sample proportions differ from their proportions of the population, how to sample when subpopulations have very different statistical characteristics, etc.
As I write this, it is entirely possible to earn a PhD in statistics and not take a single course in sampling or survey statistics. Many federal agencies hire statisticians and then send them immediately back to school to places like UMD's Joint Program in Survey Methodology. (The federal government conducts a LOT of surveys.)
I can't claim to be certain, but I think that sampling statistics became esoteric for two reasons. First, surveys (and data collection in general) have traditionally been expensive. Until recently, there weren't many organizations except for the government that had the budget to conduct surveys properly and regularly. (Obviously, there are exceptions.) Second, model-based statistics tend to work well and have broad applicability. You can do a lot with a laptop, a .csv file, and the right education. My guess is that these two factors have meant that the vast majority of statisticians and statistician-like researchers have become consumers of data sets, rather than producers. In an age of "big data" this seems to be changing, however.

Much ado about response rates

Response rates for surveys have been dropping for years, causing frustration among statisticians and skepticism from the public. Having a lower response rate doesn't just mean your confidence intervals get wider. Given the nature of many surveys, it's possible (if not likely) that the probability a person responds to the survey may be related to one or a combination of relevant variables. If unaddressed, such non-response can damage an analysis. Addressing the problem drives up the cost of a survey, however.
Consider measuring unemployment. A person is considered unemployed if they don't have a job and they are looking for one. Somebody who loses their job may be less likely to respond to the unemployment survey for a variety of reasons. They may be embarrassed, they may move back home, they may have lost their house! But if the government sends a survey or interviewer and doesn't hear back, how will it know if the respondent is employed, unemployed (and looking), or off the job market completely? So, they have to find out. Time spent tracking a respondent down is expensive!
So, if you are collecting data that requires a response, you must consider who isn't responding and why. Many people anecdotally chalk this effect up to survey fatigue. Aren't we all tired of being bombarded by websites and emails asking us for "just a couple minutes" of our time? (Businesses that send a satisfaction survey every time a customer contacts customer service take note; you may be your own worst data-collection enemy.)

In Practice: Political Polling in 2012 and Beyond

In context of the above, Aaron Strauss's February 25th talk at DSDC was enlightening. Aaron's presentation was billed as covering "two things that people in [Washington D.C.] absolutely love. One of those things is political campaigns. The other thing is using data to estimate causal effects in subgroups of controlled experiments!" Woooooo! Controlled experiments! Causal effects! Subgroup analysis! Be still, my beating heart.
Aaron earned a PhD in political science from Princeton and has been involved in three of the last four presidential campaigns designing surveys, analyzing collected data, and providing actionable insights for the Democratic party. His blog is here. (For the record, I am strictly non-partisan and do not endorse anyone's politics though I will get in knife fights over statistical practices.)

In an hour-long presentation, Aaron laid a foundation for sampling and polling in the 21st century, revealing how political campaigns and businesses track our data, analyze it, and what the future of surveying may be. The most profound insight I got was to see how the traditional practices of sampling statistics were being blended with 21st century data collection methods, through apps and social media. Whether these changes will address the decline is response rates or only temporarily offset them remains to be seen.Some highlights:

  • The number of households that have only wireless telephone service is reaching parity with the number having land line phone service. When considering only households with children (excluding older people with grown children and young adults without children) the number sits at 45 percent.
  • Offering small savings on wireless bills may incentivize the taking of flash polls through smart phones.
  • Reducing the marginal cost of surveys allows political pollsters to design randomized controlled trials, to evaluate the efficacy of different campaign messages on voting outcomes. (As with all things statistics, there are tradeoffs and confounding variables with such approaches.)
  • Pollsters would love to get access to all of your Facebook data.

Sampling Statistics and "Big Data"

Today, businesses and other organizations are tracking people at unprecedented levels. One reason rationale for big data being a "revolution" is that for the first time organizations have access to the full population of interest. For example, Amazon can track the purchasing history of 100% of its customers.I would challenge the above argument, but won't outright disagree with it. Your current customer base may or may not be your full population of interest. You may, for example, be interested in people who don't purchase your product. You may wish to analyze a sample of your market, to figure out how who isn't purchasing from you and why. You may have access to some data on the whole population, but you may not have all the variables you want.More importantly, sampling statistics has tools that may allow organizations to design tracking schemes to gather the most relevant data to their questions of interest. To quote R.A. Fisher "To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: He may be able to say what the experiment died of." The world (especially the social-science world) is not static; priorities and people's behavior are sure to change.Data fusion, the process of pulling together data from heterogeneous sources into one analysis, is not a survey. But these sources may represent observations and variables in proportions or frequencies differing from the target population. Combining data from these sources with a simple merge may result in biased analyses. Sampling statistics has methods of using sample weights to combine strata of a stratified sample where some strata may be over or under sampled (and there are reasons to do this intentionally).

I am not proposing that sampling statistics will become the new hottest thing. But I would not be surprised if sampling courses move from the esoteric fringes, to being a core course in many or most statistics graduate programs in the coming decades. (And we know it may take over a hundred years for something to become the new hotness anyway.)

The professor that taught the sampling statistics course that I took a few years ago is the chief of the Statistical Research Division at the U.S. Census Bureau. When I last saw him at an alumni/prospective student mixer for Georgetown's math/stat program in 2013, he was wearing a button that said "ask me about big data." In a time when some think that statistics is the old school discipline only relevant for small data, seeing this button on a man whose field even within statistics is considered so "old school" that even most statisticians have moved on  made me chuckle. But it also made me think; things may be coming full circle for sample statistics.

Links for further reading

A statistician's role in big data (my source for the R.A. Fisher quote, above)

Moderating The World IA Data Viz Panel

This weekend was my introduction to moderating an expert panel since switching careers and becoming a data science consultant. The panel was organized by Lisa Seaman of Sapient and consisted of Andrew Turner of Esri, Amy Cesal of Sunlight Foundation, Maureen Linke of USA Today, and Brian Price of USA Today. We had roughly an hour to talk, present information, and engage the audience. You can watch the full panel discussion thanks to the excellent work of Lisa Seaman and the World IA Day organizers, but there's a bit of back-story that I think is interesting.

DataViz-BW-AgencyFB Bold DataViz-Rivers-AgencyFB BoldIn the spring of 2013 Amy Cesal helped create the DVDC logo (seen on the right), so it was nice to have someone I'd already worked with. Similarly, Lisa had attended a few DVDC and asked me to moderate because she'd enjoyed them so much. By itself it's not exactly surprising that Lisa attended some DVDC events and went with who she'd met, but common sense isn't always so common. If you google "Data Viz" or "Data Visualization" and focus on local DC companies, experts, speakers, etc. you'll find some VERY accomplished people, but there's more to why people reach out. You have to know how people work together, and you can only know by meeting them and discussing common interests, which is a tenant of all the DC2 Programs.

Now that the sappy stuff is out of the way, I wanted to share some thoughts on running the panel. I don't know about you, but I fall asleep whenever the moderator simply asks a question and each panelist answers in turn. The first response can be interesting, but each subsequent response builds little on the one before, there's no conversation. This can go on for one, maybe two go-rounds, but any more than that and the moderator is just being lazy, doesn't know the panelists, doesn't know the material, or all of the above. A good conversation builds on each response, and if that drifts away from the original question the moderator can jump in, but resetting too much by effectively re-asking the question is robotic and defeats the purpose of having everyone together in one place.

Heading this potential disaster off at the pass, Lisa scheduled a happy hour, hopefully to give us a little liquid courage and create a natural discourse. I did my homework, read about everyone on the panel, and starting imagining how everyone's expertise and experience overlapped. Accuracy vs communicating information; Managing investigative teams vs design iteration; building industry tools vs focused and elegant interfaces; D3js vs Raphael. The result: a conversation, which is what we want from a panel, isn't it?