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