In Data Analysis, the Right Answers Come Only From Asking the Right Questions

Even highly-educated analysts struggle in the beginning of their careers. Hiring managers often wonder why analysts with a 4.0 GPA have hard time delivering valuable results. There are many theories and opinions about that, but allow me to share a simple point of view here. Many young analysts struggle because they don’t know what to do, not because they lack statistical or programmatic skills. It’s because in a school, the professor in charge would provide sets of questions to solve. The work in such cases is really about mathematics and mechanics. In the business world, coming up with the right set of questions is the most challenging part. When you are sitting in a planning meeting, you would hear lots of “we need this” or “we need that." But such expressions do not directly translate into tangible goals or problem statements. A goal such as “Let’s improve click-through rates from email campaigns” is relatively clear, but to an analyst, it is just the first sentence in a homework assignment. Improvement from what point? What has been going on? How many types of email campaigns are we talking about? For what type of products and services? Using what kind of offers? How are we supposed to measure the results? And ultimately, what do you want? More clicks, more conversions or more money? What if the answer is “all of the above”? Now, that surely sounds like an open-ended question. No such thing as a perfect dataset The second reason why a novice analyst struggles is because in real life, there is no such thing as a perfect dataset. For a school assignment, the professor would provide a pristine set of test data on a silver platter. In business settings, datasets are filled with missing, invalid or inconsistent values coming from all kinds of sources. That is why analysts often want minimum two weeks to finish a seemingly simple task. They are buying time, because they know they are the ones who must fix all the mistakes that are made to the dataset before it got to them. In reality, more than 80% of analysts’ time goes into fixing and standardizing provided data. I’m sure they didn’t get a degree in statistics to clean the data all day long, but that is the reality. One of the prerequisites of being a modern-day data scientist is consulting ability with keen business acumen, along with excellent communication skills. They should get a grip of the situation fast, and translate vague and unclear statements into firm project specifications. Only then, they will be able to squeeze monetary values out of compiled data. I’ve seen countless cases where analysts built complex models just because it is something that they are good at. When I ask them why they built such models, the ones whose work holds potential at least try to cite business benefits. Hopeless ones just list a long series of technical terms and brag about their mathematical journeys. To end-users who must show results in dollars and cents, minutia of modeling techniques means nothing. Some fancy machine learning techniques do not bring in cash on their own; appropriately applying them to business cases does. That doesn’t mean that the burden of constructing problem statements and project plans solely falls onto data scientists. In this day and age, all decision makers must have some basic understanding of analytics options, capabilities and limitations. Imagine being a good patient in front of a medical doctor. How decision makers can help Yes, I’ve been training analysts so that they don’t become data plumbers who just move data around without creating any tangible value. I tell them not to be just technology-oriented but to be solution-oriented. They shouldn’t do things just because they can but commit to projects that would bring monetary rewards for the whole organization. No one pays for analysts’ time and expensive toolsets, so that mathematical geeks get to satisfy their intellectual curiosities. In the business world, we employ analytics to increase revenue, decrease cost, and save time. It is really that simple. Nonetheless, modern-day decision makers must change their ways, too: Take data and analytics seriously Remember a baseball movie called Money Ball? Pioneers who introduced data analytics into team management had hard time convincing old-timers who valued irrelevant metrics—to winning, anyway—such as “how well would the player look in a pin-stripe uniform?” Ball clubs nowadays do not question whether they should employ analytics or not; they all do it to obtain consistent results. At times, a gut-check is useful, but no one should solely rely on it year after year. Define the goals first Then align teams, technologies and data flow. Too many organizations start extensive data projects without clear roadmaps. Yes, data scientists should worry about the actual blue prints of projects, but the organizational goals should be firmly set first. For instance, things that we do for prospecting and CRM are very different, as sources and nature of available data that fuel the efforts are different. Drop the "everything, all the time” mentality, and prioritize the goals based on merits. Don’t ever start a data journey without a roadmap designed by a data strategist. Don’t be blinded by machines AI, no matter how advanced they are, require humans to assign the right set of tasks, just like human analysts do. Attitudes like “Just leave it to the machine” often lead to waste of resources and unsatisfactory results. Further, people with that kind of mentality will be the first ones to be replaced by the machines in the near future. To stay relevant in the age of machines, we must be the ones who give purposes to the machines, not the other way around. Be logical, too Maybe not as much as a professional data nerd, but in ubiquitous data environments, everyone must be able to express what they want in a logical fashion. Also, to interpret the results of analytics, you must understand basic elements of data play. Otherwise, you may make a wrong conclusion even with the right answers in front of you. By staying logical, data players will understand you much better, and in return, they will get you to the right answers much faster. Be realistic Don’t forget that data and analytics isn’t some magic that will double the revenue just because you employ them. Set specific goals and continue to build more successes upon small successes. We used to use the term “closed-loop marketing” often in the past. That means each effort must serve as a stepping stone for the next, by continuously examining “what really worked” after each phase. Data and analytics play is an iterative one. Don’t be too disappointed that you didn’t make any big impact in your first trial run. Even if you were successful in the beginning, never stop short, as it is a never-ending game. Consider analytics as a lifetime commitment as a marketer. It is impossible to avoid data and analytics in the digital world. Not everyone has to be a data expert, but to obtain answers that matter, we should start by asking the right set of questions. In a data-driven world, “all of the above” type wish-list will never cut it. Even if such brute force approach works out occasionally, it will turn out to be an extremely expensive way of doing business in the long run.