How to start making sense of your data

Now more than ever, companies are collecting large amounts of data at a high frequency. This is thanks to cheap storage and a multitude of tools available to automate the process (i.e. Google Analytics, Flurry, Mixpanel, etc.).

While collecting data is one thing, finding meaning or actionable insights is another task altogether. Over the past month, I have spent time with some of our portfolio companies talking about how to leverage the power of their data. At first, everyone seemed overwhelmed: “With all this data, where do we even start? We need to hire a data scientist immediately!”

If your startup is drowning in data, the good news is you can actually learn a lot about your company and product with some simple math. In particular, here are two tips that may help you make sense of this incredibly valuable asset.

1) Your data already has the answers. The challenge is asking the right question.

It’s natural to feel overwhelmed by a big question that has no apparent or obvious answer. For example, imagine asking yourself, “What day of the week is best to ramp up our ad campaigns?” The open-ended nature of this question doesn’t provide any direction toward figuring out the answer, no matter how much data you have.

Now, consider rephrasing the question above into a hypothesis: “Our best clickthrough rate is on Fridays in New York City.” As long as you have the data, it is relatively easy to prove (or disprove) this statement. And, if you find out the statement isn’t true, you can continue testing all the days and cities until you land on the right combination.

Use your intuition to come up with a hypothesis with enough granularity to focus your analysis. Don’t worry about your initial hypothesis being correct; a focused statement that’s wrong also brings you that much closer to the truth.

2) Go for the low hanging fruit.

Before applying any machine learning algorithms, it is absolutely critical that you understand your data. Otherwise, you run the risk of violating mathematical assumptions by throwing it into a black box and drawing the wrong conclusions.

The best place to start is with descriptive statistics which can offer interesting insights without any complex calculations. Here, we’re essentially referring to univariate analysis and bivariate analysis.

a) Univariate analysis

Start by exploring each variable individually. For example, you might want to look at the age of your users, the price of listings on your marketplace, or the revenue generated by each customer.

  • For each variable, calculate the mean, median, mode, and variance, and know the minimum, maximum, and range. Tip: if your mean and median are very different, you likely have an outlier.
  • Plot a histogram to visualize the distribution. In addition to looking at the average, consider percentiles and standard deviations from the mean. You can learn a lot from the shape of the distribution via skewness (a measure of symmetry, Figure 1) and kurtosis (a measure of “peakedness”, Figure 2). In reality, you may actually prefer a non-normal distribution – either behavior that leans toward one action over the other (skewness) or is more or less common than the other (kurtosis). Visualizing distributions can help you identify which variable you actually need to target for change or improvement.


Figure 1: General forms of skewness


Figure 2: General forms of kurtosis

 b) Bivariate analysis

After looking at each variable separately, consider running statistical tests on pairs of variables to find out whether there are significant correlations. Tip: if there is a strong relationship, you likely only need to include one of the two variables in your mathematical models to avoid over-representation or biasing.

  • When you have two numerical variables (i.e. age and page visits), you can calculate the Pearson coefficient or the Spearman coefficient (Figure 3). The Pearson coefficient assumes that the relationship between the variables is linear (i.e. think back to high school math with y = mx + b), while the Spearman coefficient has more “relaxed” assumptions and describes the relationship as a monotonic function. In order to determine which statistical test is better, compare the R-squared values and pick the one that is closer to 1.


Figure 3: Difference between Pearson and Spearman correlations

  • When you have two categorical variables, you can run a chi-squared test. Suppose you know the location of each user as well as the level of SaaS subscription they are paying for. A chi-squared test can help you determine whether there is a significant correlation between geography and the need for to use more features of your product.
  • When you have one categorical variable (i.e. gender) and one numerical variable (i.e. age), you can determine the significance between the two with a z-test, t-test, f-test, or ANOVA, depending on means and variances. There are many references on the web that can help you figure out which test is most suitable.

For those of you who have heard me carry on about my love for data, you know this post is a long time coming. I hope it serves as a prelude to a series of data-related posts to help startups navigate the world of big data. Stay tuned!

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