Oct 072013

When taking on an analytics project, or designing a reporting system (dashboard or otherwise) a core component to superior execution is to properly understand the question(s) the vehicle is expected to answer.  This may seem like an obvious statement, but it is amazing how often the metrics of focus are done so for convenience rather than impact. Additionally, dashboards and reports are often (at least initially) put together by individuals with little training in design and business reporting. A monkey can make a graph, but it takes a bit of thought and planning to make something impactful.  I would argue that the state of business intelligence in general suffers from this issue – people undervalue the opportunities for using data to make great business decisions because they have learned that the data available to them is not useful for doing so. Instead of insisting that the metrics and reports be useful for business decisions, they instead write off the full potential of the data and go back to the inefficiencies of traditional gut based decision making. What they fail to realize or are not made aware of, is the wide variety of data available that is not being used. Empowering decision makers to utilize data is a core purpose of an analyst.

While assigning fault for the poor state of BI affairs isn’t particularly helpful, it’s worth noting that it is a systemic issue based on the past delivery of inferior metrics and reports coupled with limited decision making timeframes. This can also be compounded by the general ignorance of what data is available. The analyst’s job is to right these wrongs and must retrain the organization around how data is approached, assembled, and utilized for business decisions. This reorganization begins with the most basic step in the analytics chain: Defining the right questions.

The reason that the question definition is key is because all further analytical planning actions stem from it. Question definition, while the job of the analyst, requires input from the consumers of the information. Interviewing those who will use the analysts’ output is key to deciding what any given analytical product will contain including how many metrics are needed, in what format, and how frequently they will need to be updated.  The unsophisticated organization derives metrics by default, in a post-hoc manner based around whatever data has happened to be collected. This approach is not likely to have the impact that a more carefully planned set of information tailored to the business needs will.

Additionally, some decision makers will believe they know exactly what data they want and need, and it is important that the analyst probe and make sure this is correct. Finding out what a particular metric will be used for, or why a specific piece of information is useful can uncover unintended interpretation mistakes (e.g. when a unique-id is not indicative of a unique user due to cookie expiration).  It is safe to say that while business owners often do not understand the data used to create particular metrics, they often have strong opinions about what the metrics mean. It is the job of the analyst to educate and redefine based around these shortcomings. Furthermore, the analyst should be aware of the myriad of data sources that are available for creating metrics from, helping to aid the business owner through discovery. This is a major reason it is critical to get the BI consumer/business decision maker to talk about what the questions they are trying to answer are rather than to expect them to rattle off a full list of needed metrics. The analyst defines the metrics based on the needs of the business owner. It is crucial that the analyst take an active, participatory role in the design stage rather than a passive “fast food order” style of design. You are a data educator – act like one.

In closing, there are a number of additional steps to defining the mechanism necessary for answering the questions settled upon. Some questions require multiple metrics, new data collection, derivative metrics (e.g. time between purchases is derived from last purchase timestamp minus previous purchase timestamp), or a re-statement of the question due to limitations in available data. This layer of the design lies in between the initial question definition and the visual display of data, but again is key to successful execution of an analytic output. The analyst is an artist, a teacher, a designer, a data counselor, and a storyteller as well as being the one who knows the data. You can’t design an output mechanism if you don’t know what the input question is.

Question –> translates to data –> summarized by metrics –> interpreted by analysis –> converted to display –> answers or informs question

May 132013

There are a number of often unspoken jobs that the analyst must perform in order to be useful, helpful and thereby successful.  These include being a question definer, a translator, a data expert, a storyteller, and an anticipator of post analysis questions. Without approaching a business question (which should inform a specific business action) carefully, the opportunity for error or underwhelming findings is greatly increased.

Definer of THE QUESTION – When I think about analysis I always start with “what is the question we are trying to answer?” The question to be answered is never as simple as whatever my boss has asked me; data is messy, the question is complex, and more often than not, the initial question is wrong. By wrong, I mean that it is too general, makes assumptions about the answer that may be wrong, or just does not make sense from a business point of view. One way to think about whether the question is a good one or not is to come up with a fake answer and see if it would change anything. “How many women in their 40’s are tweeting about our product?” the boss asks you… the answer, by itself, is probably pretty useless – “135”. What the boss really wants to know is “what are the demographics of people tweeting about our products, I think it’s mostly demographic X?” Assuming you have access to the demographics, you can present a plethora of data – by product, by cohort, etc. That approach makes your answer to the initial (not good) question make more sense “Looks like 135, which is 32% of all folks tweeting about us at all.” Engaging in a Socratic dialogue with the data consumer (or yourself for that matter) can help you to understand the kernel, the impetus for the question and thereby redefine it in a way that extends the usefulness of the answer and guides additional analyses necessary for understanding more deeply the phenomenon you are investigating.

Rosetta Stone – The analyst must be able to translate fluidly between many entities. Business and marketing oriented groups will not speak the same language as data miners and data scientists. The audience for consuming data is constantly changing. The analyst must anticipate these differences, speak the different languages and be sensitive to the fact that her job is to increase understanding (rather than expecting their customers, the data consumers, to do that research after the data is presented). Again, if the analyst was not needed to act as a translator, she would be easily replaced by a tool at some point. Once the question to be answered has been defined, the analyst will most likely have to interact with entities to extract the raw data. One man’s “how many” is another man’s “SELECT * FROM table_name”.

Master of Data – At the end of the day, the analyst must know the data they are transforming and interpreting. They must understand the structures, the nuances, and the quality. This means a part of an analyst’s job is to investigate, on their own, the data sources they interact with. Blindly extracting data is a great way to create false conclusions due to poor quality data, null fields, strange conventions and more. I recall running an analysis whereby I found the “gender” column of a master table contained the values “male”, “female”, and “child”. Imagine if I had tried to do some sort of deductive analysis whereby I got a count of total uniques and total males, and then took a shortcut (uniques – males) to derive females. Oops. There is no universal taxonomy, there is no universal ontology. When it comes to data, you have to check and double check the sources to be sure you understand the nature of the data you are extracting value from.

Storyteller – The analyst needs to take data, in its raw form, and mold it into something that can be understood and easily retained by the audience. Answers to business questions are rarely straightforward (and if they were, the analyst would be replaced by a dashboard) and often require a contextual back story. The analyst must determine, to the best of his or her ability, the relevant context for the answers presented. This context often goes beyond the data and requires (gasp) talking to others or (shudder) using the product that generated the data. Without the appropriate data context and the ability to describe that context to the audience, the analyst is nothing more than an overpaid calculator moving numbers into tables and charts for someone else to interpret. A great interview on the storytelling aspect of data analysis was given by Cole Nussbaumer (whose blog I am adding to the blogroll on the front page) for the website klevr.org.

Anticipator of Questions – I believe that a successful presentation of data provides clear information about a specific set of business questions such that decisions can be made. I also believe that a successful analysis generates more questions. While that may seem counter intuitive  (if you answered the original questions why are people asking more?) in my experience, the questions asked after a successful analysis are those that build upon the insights you presented (rather than being unrelated or confrontational/doubtful). If you get no questions, I fear you have bored or confused your audience. That said, anticipating the most likely follow up questions and running the analyses on a few is generally low cost with high reward as it shows you have defined the question space well, translated it in the appropriate manner, retrieved the data to answer those questions above and beyond expectations, have mastered the data and are now weaving it into the epilogue of your story, delighting your audience and giving them valuable information. If nobody asks the questions you anticipated, you can always present them (quickly, as you have spent most of your time talking about the core question) as bonus material “I was curious about…” this also leaves your audience with the assurance that you actually care about unearthing insights and frankly, if you really don’t, you should find a new job.

May 062013

In business, as in life, there are a number of unknowns that we constantly have to notice, interpret, and react to. A superior strategy includes prediction or some sort of range of expectation(s) for key actions (e.g. product releases, announcements), the accuracy of which are the result of factors like good intuition, observation, and pattern recognition. As such, the strategic minded individual should be collecting information from multiple sources in order to make the transition from expectation to outcome as seamless as possible as often as possible. There will be colossal misses, due to misinterpretation of given data, failure to collect appropriate data, or a high degree of chance in the actual outcome. Example: The cult film Donny Darko was released to major theaters around the time of the September 11 attacks. Part of its failure as a major release has been attributed, post-hoc, to the particularly sensitive aspect of one of the movie’s main events – an airplane engine falling from the sky and destroying Donny’s house. Estimating the success of this movie when it was being prepared for its release would never have included a “terrorist airplane attack” factor. That said, reasonable ranges of expectations can be provided most of the time.

A huge advantage to narrowing the range of possibilities of a particular forecast or outcome while also maintaining a decent accuracy rate is to engage in lateral thinking and converging on answers through the use of techniques like proxy variables. One place that I have seen analysts and non-analysts falter when trying to predict a business outcome is their failure to engage in creative thinking around ways to estimate unknowns.  Rarely is an estimating technique as simple as plugging in a few values to a known formula – especially when tacking innovative solutions. Frankly, if it was this easy, then analysts and strategic thinkers would have a very short shelf-life as they would come in, set up the magic eight ball, and be done forever.   An analyst’s job is to explore, research, and create (art + science) answers to the right questions. Note that I didn’t say all questions. An additional part of the analyst’s job is to act as a noise filter by taking and refining the key pieces of business requests and squeezing them down to basic elements of what needs to be known.

In the following posts I plan to tackle a number of specific topics revolving around approaches to making good predictions and providing superior answers, not from a cookbook style point of view but from a higher level. Let’s get meta and call it a strategy around creating strategy. The approach I take is that of the analyst as a curator, a gardener, and a scientist. The analyst is proactive, inquisitive, provides unique insight and through knowledge of the data, surfaces questions that have never been asked.

Every business is different, but often the core remains the same: a good or service is being offered to a consumer. Working from that core, business questions around directions to take an offering, how it will fare in the open market, how to improve it, minimum viable product requirements and other more mundane day to day curiosities will arise. The analyst should be able to tackle these as a matter of course, and recognize the larger questions implied by the smaller ones and vice versa. Rarely is there a single question, and rarely is there a single answer to any given question. In the following posts I plan to explore the world of the analyst from my own personal lens, providing an overarching description and then digging into specific topics. The following is my off-the-cuff laundry list of expected posts.  Hope you enjoy them.

  • The role of the strategist and analyst
  • Answers 101: Defining the right questions
  • Using proxy variables to improve estimates and answers
  • Information sources and intelligent approaches to information
  • Core data needs: Quality, Breadth, and Volume
  • Skunkwork Analytics: your often undefined job
Jan 022013

The data buzz-phrase of the current century, “Big Data”, is often approached as a magical construct that one might lash themselves to and, like Odin to the Yggdrasil, walk away with great knowledge after a time – maybe just by being near it. The idea being that using this toolset is THE way for extracting value from your data. I’m not the first to say it, but this is similar to how relational data bases have been sold for years, only now the promise extends out to unstructured and semi-structured data. Pro tip – you still have to manipulate the data to get anything worthwhile from it, and that assumes you collected the right stuff to begin with.


It’s unfortunate that a lot of people in the organizational position to make investments in data infrastructures, technologies, and tools get stuck playing a game of mad libs instead of figuring out what each tool can do and more importantly what they need each tool to do to be useful.  By that I mean that they have a sentence that goes something like “If only I had _____ technology all my _____ problems would be solved”. On the flip side, companies trying to sell Big Data services love these kinds of decision makers, promising them that “cloud based, big data solutions” solve all data problems. I mean, take any kind of data (structured, unstructured, semi-structured) upload it to the cloud, throw it into HBASE, run a map/reduce job against it in Hadoop and BAM! Cool… then what? Cloud storage is infinitely sized, safe, and depending on how much you rent it for, geo-redundant. Problems solved, right? Or are they?


Let’s back up and start…at the beginning. If you have a business that can potentially generate a lot of data (transactional, operational, etc.) you fall into one of two camps: you currently have a ton of data you are warehousing/archiving or you do not have a ton of data (for one or more of several reasons) but now you can once you instrument your systems to spit out proper logs.


Let’s assume you are in the first camp and have a ton of data. What kind of data have you gathered, and in what format? How much data do you generate every day? Lastly – could you vastly shrink the amount of useful data you gather by applying simple ETL jobs? I’d argue that most organizations (not all) that are looking into big data solutions are actually doing so very prematurely. Just because you can suddenly collect and infinitely store every piece of data your servers generate, the output from your web logs, and all public mentions of your organization on Twitter and Facebook is probably more a curse than a blessing – the concept of infinite storage for cheap promotes an unthinking “dump it in here and we will sort it out later” approach to data collection. It’s true, storage is cheap, but paying developers to pick through the garbage later (often over and over again) is mind-numbingly expensive. A better solution is to structure your data collection intelligently, write ETL jobs that make your data compact and accessible and let your developers spend their time using the data to improve your business instead of (potentially over and over again).


Now switch to the second camp – no data now, but lots ASAP.  What kinds of data can you and should you gather? How should it be structured? What will you do with it? The nature of these questions suggest that trying to choose the tools you will use without an initial grounding in what you can have and what will use the tools for makes the choices premature at best. But the experts you talk to may suggest you just start collecting as much as fast as you can, since storage is cheap and…


This “I have a hammer and everything looks like a nail” approach to capturing and deriving value from data (or data exhaust, as it is sometimes called) by using a particular tool alone is really shortsighted, and a recipe for expensive failure as you hire expensive experts to troll through your piles of garbage looking for gold rather than setting up your organization for successful insights ahead of time. Use the current fixation on big data to promote your data strategy, to get developers instrumenting your products and services deeply, in the hopes that you will soon have a high quality data asset that screams out for some tool to tame it. This may be a big data tool like Hadoop, or it may be a set of perl scripts, or (gasp) an Excel spreadsheet. The point is that Hadoop and the rest are tools to be pointed and fired at specific issues in specific situations. You are not Google, and you probably don’t need the tools Google uses. You do need to be smart about data, which is something the big data buzz has highlighted. The beauty of the current landscape is that if you actually need massive scale processing that fits in the map/reduce paradigm, you can have it. In other words, you are no longer limited (or forced to sample) when you have a large set of data. All the other issues with data quality that have plagued us forever are still present, important (maybe moreso), and in need of attention. Don’t be lulled into a false sense of security just because you have a larger bucket for use in panning – you still have to sift through it all to find the gold, IF you captured enough of the right types of data to begin with.

Nov 252012

So Nate Silver is the stats nerd of the year for his great (or lucky, if you hate science) methodology around poll aggregation and the poll weighting algorithm he employed regarding the prediction of the outcome of the recent national elections. Congratulations Nate, if I didn’t live in a country with Byzantine banking laws, I would have made a tidy sum using your leg work (among others – I firmly believe in leveraging the wisdom of crowds of experts) to invest on “Obama to win” via the event based market InTrade. I haven’t been able to find any apologies by the demonizers who suggested Nate was just another political hack (like them?) who was rooting for the wrong team and trying to hide it behind some sort of magical thinking in the guise of science, but I can’t say I looked too hard.

While the disappointing part of the whole Nate Silver predicting the elections bit lies in the constant misinterpretation of what Nate actually did to come by his numbers due to the general publics’ pseudounderstanding of statistics, the beauty of the press he received both before and after the election has elevated the role of data in decision making – even messy social data like poll results (essentially surveys, with all their inherent issues). The age old “gut feeling” as the sole driver of decision making (i.e. guessing) is coming under needed scrutiny in an age where having current and historical information is finally possible. Those who fail to incorporate data, especially data that is readily available or easily gathered, will be left behind or when successful in their guesses (expertise does have its place) will be less efficient.

It is my firm opinion that gut feeling is a garnish best placed on top of data driven analysis where the depth of gut needed is (roughly) inversely proportionate to the data available. Nate doesn’t use gut feelings, he uses data, which can then then be handed to those responsible for making decisions.

So how does Nate Silver make my job easier? As Silver commented to Jon Stewart on the Daily Show after being asked about what it would mean if his model had been wrong, Nate responded “It would have been bad, I think, because for some reason 538 became invested with this symbolic power, and you know symbolic power isn’t particularly rational, right, but it became this symbol for people who were believing in hey, let’s look at the polls. Let’s do some empirical research, right.” Empirical research was shown to best guts. This research was contrary to a huge contingent of, not surprisingly, biased observers but was shown to be superior to all other estimations, guesses, scrying stone proclamations, etc. even those made by individuals with a vested interest in Obama winning. His. Model. Won. Data won.  As data of such a highly scrutinized, over-thought, expensive contest won over individual “expert” opinion, my job got easier. The hugely symbolic power of that specific use of data helps serve as a powerful example of what data can do. When talking to organizations about the value of data, the value of quality data, and the usefulness of measurement to drive business decisions I now have an example that everyone knows, and in some small way, understands. Am I comparing myself to Nate Silver? Not particularly – we come from very different backgrounds, education, approaches etc. But one thing is certain – he has just made the human interaction part of my job a lot easier – that part where I am convincing a client to invest in data resources, to care about data quality, data completeness, and data driven decision making. Thanks Nate.

Jul 062011

I have an intern right now, which allows me the opportunity for a lot of teaching opportunities which I love. In order to give her a real piece of work to chew on and master, I handed her the analysis of a new product my company has launched into the Android market.  It’s a mobile coupon aggregation service called Lotza, and the idea behind it is that with all the daily deal sights that are popping up everywhere like Groupon and Tippr, it would be nice if there was something that a) showed all the deals in one place and b) only showed the most likely deals that would be of interest and c) stored all purchased offers in a single wallet, for simple (virtual) retrieval. There’s a lot more to it in the development pipeline, but at beta, that is the core functionality. Using our analytics backend to power a direct-to-consumer product is a great opportunity for us to own the data we generate, and to experiment freely with layout, design, and analytic algorithms. Being the complete masters of our own system comes with all the accompanying responsibilities you might expect.

As per the norm, my Business Intelligence team drove product instrumentation and logging requirements to capture the user experience to the fullest. As is often true in software development, after all was said and done everything was not perfect (but was close) making the derivation of metrics less straightforward than simple aggregations across fields. Multiple tables with specialized information about certain aspects of a user profile, in session experiences, and behaviors make for a great multidimensional ball of potential confusion.

My intern had begun working with me at a time after these requirements had been written, after the schemas were defined, and after most of the logging was already implemented. To bring her up to speed I had her create a logging guide document showing every page in the product and all user actions possible per page along with the associated logging. In running actual tests on a phone, and tailing the logs live, she found several small bugs, proving to me that she was paying attention and was understanding the structure of the data (even though it was complex in parts). Once this document existed, a set of reports was defined for a presentation she is expected to perform to the internal stakeholders next week. This presentation will be a general overview of the product, and a discussion of usage so far including important KPIs such as sessions per user, the conversion funnel, and page abandonment rates. None of these metrics are as straightforward as they could be in a perfect product world, which is one point of this post. The important stuff is often under the initial layer of data, and requires special filters along with an intimate understanding of both what the logging requirements and definitions were, and how they have actually been implemented.

The second point is that the analyst has to know these nuanced methods for counting what would otherwise be a simple sum or count across unique values. The analyst who isn’t able to roll up her sleeves and dive into the (potential) logging morass is really trusting the insights derived to the gods of perfect logging. My intern has recognized anomalies, and worked around them. When she gives her talk next week, she will be armed with the ability to answer almost any question thrown at her – in other words, the “why” to her “what”. Even though this data wading has eaten up a lot of her time (she worked over July 4th and the 5th, a company holiday) she thanked me for the opportunity. She is confident in her findings, has learned a lot, and knows more about the logging system for our product than I currently do (and I wrote the logging spec and defined the original schema!). Her approach of looking at logs, reading documents (that are sometimes out of date), and tailing actual logs to identify examples and verify accurate data capture (and her own understanding of what is happening) represent the many hats I believe an analyst should wear. With so much data being generated every day, and the complexity of that data increasing, the analyst must be a data generalist. I’m not proposing that all analysts must be masters of SQL and statistics and technical writing and math and the art of visual presentation – I’m suggesting that the best analysts will utilize whatever tools they need (including engineers) to get the right data in the right format to the right people. It is my opinion that anyone so lopsided in their training as to only know one of the above mentioned skills is likely to underutilize what they find – the data will be inaccurate, confusing, mysterious, overwhelming. In short, it will be a disservice to their organization, and due to their focus, they might not even realize the problem. Come on analysts, diversify!

In closing: Data is messy. Roll up your sleeves. Question your results. Triangulate to verify. Cross-reference values. Perform sniff tests. As an analyst you should be the most engaged and knowledgeable person with the data you own – your consumers rely on you to play the role of a translator and to represent your confidence in the accuracy of your findings.  By projecting this confidence and integrity, those times when you find absolutely horrendous data, or unbelievable (in a bad way) information you can either find ways to salvage some useful information from it and/or proclaim with certainty that the data is, for the most part, so dirty that your analyses are unreliable and therefore not worth the effort. Your consumers may not always like the answers you provide, but they will respect you for declaring as much, especially when you know the data so well you can pinpoint the major issues that bring reliability into doubt. Of course, when you find amazing insights you can confidently present them (and show backup verification that you didn’t make some simple error – you know the data that well.)