The Basics Of Data Modeling

This is part five of a series on launching a data science project.

At this point, we have done some analysis and cleanup on a data set.  It might not be perfect, but it’s time for us to move on to the next step in the data science process:  modeling.

Modeling has five major steps, and we’ll look at each step in turn.  Remember that, like the rest of the process, I may talk about “steps” but these are iterative and you’ll bounce back and forth between them.

Feature Engineering

Feature engineering involves creating relevant features from raw data.  A few examples of feature engineering include:

  • Creating indicator flags, such as IsMinimumAge: Age >= 21, or IsManager: NumberOfEmployeesManaged > 0.  These are designed to help you slice observations and simplify model logic, particularly if you’re building something like a decision tree.
  • Calculations, such as ClickThroughRate = Clicks / Impressions.  Note that this definition doesn’t imply multicollinearity, though, as ClickThroughRate isn’t linearly related to either Clicks or Impressions.
  • Geocoding latitude and longitude from a street address.
  • Aggregating data.  That could be aggregation by day, by week, by hour, by 36-hour period, whatever.
  • Text processing:  turning words into arbitrary numbers for numeric analysis.  Common techniques for this include TF-IDF and word2vec.

Feature Selection

Once we’ve engineered interesting features, we want to use feature selection to winnow down the available set, removing redundant, unnecessary, or highly correlated features.  There are a few reasons that we want to perform feature selection:

  1. If one explanatory variable can predict another, we have multicollinearity, which can make it harder to give credit to the appropriate variable.
  2. Feature selection makes it easier for a human to understand the model by removing irrelevant or redundant features.
  3. We can perform more efficient training with fewer variables.
  4. We reduce the risk of an irrelevant or redundant feature causing spurious correlation.

For my favorite example of spurious correlation:

The only question here is, which causes which?

Model Training

Now that we have some data and a clue of what we’re going to feed into an algorithm, it’s time to step up our training regimen.  First up, we’re going to take some percentage of our total data and designate it for training and validation, leaving the remainder for evaluation (aka, test).  There are no hard rules on percentages, but a typical reserve rate is about 70-80% for training/validation and 20-30% for test.  We ideally want to select the data randomly but also include the relevant spreads and distributions of observations by pertinent variables in our training set; fortunately, there are tools available which can help us do just this, and we’ll look at them in a bit.

First up, though, I want to cover the four major branches of algorithms.

Supervised Learning

The vast majority of problems are supervised learning problems.  The idea behind a supervised learning problem is that we have some set of known answers (labels).  We then train a model to map input data to those labels in order to have the model predict the correct answer for unlabeled records.

Going back to the first post in this series, I pointed out that you have to listen to the questions people ask.  Here’s where that pays off:  the type of algorithm we want to choose depends in part on the nature of those questions.  Major supervised learning classes and their pertinent driving questions include:

  • Regression — How many / how much?
  • Classification — Which?
  • Recommendation — What next?

For example, in our salary survey, we have about 3000 labeled records:  3000(ish) cases where we know the salary in USD based on what people have reported.  My goal is to train a model which can then take some new person’s inputs and spit out a reasonable salary prediction.  Because my question is “How much money should we expect a data professional will make?” we will solve this using regression techniques.

Unsupervised Learning

With unsupervised learning, we do not know the answers beforehand, so we’re trying to derive answers within the data.  Typically, we’ll use unsupervised learning to gain more insight about the data set, which can hopefully give us some labels we can use to convert this into a relevant supervised learning problem.  The top forms of unsupervised learning include:

  • Clustering — How can we segment?
  • Dimensionality reduction — What of this data is useful?

Typically your business users won’t know or care about dimensionality reduction (that is, techniques like Principal Component Analysis) but we as analysts can use dimensionality reduction to narrow down on useful features.

Self-Supervised Learning

Wait, isn’t self-supervised learning just a subset of supervised learning?  Sure, but it’s pretty useful to look at on its own.  Here, we use heuristics to guesstimate labels and train the model based on those guesstimates.  For example, let’s say that we want to train a neural network or Markov chain generator to read the works of Shakespeare and generate beautiful prose for us.  The way the recursive model would work is to take what words have already been written and then predict the most likely next word or punctuation character.

We don’t have “labeled” data within the works of Shakespeare, though; instead, our training data’s “label” is the next word in the play or sonnet.  So we train our model based on the chains of words, treating the problem as interdependent rather than a bunch of independent words just hanging around.

Reinforcement Learning

Reinforcement learning is where we train an agent to observe its environment and use those environmental clues to make a decision.  For example, there’s a really cool video from SethBling about MariFlow:

The idea, if you don’t want to watch the video, is that he trained a recurrent neural network based on hours and hours of his Mario Kart gameplay.  The neural network has no clue what a Mario Kart is, but the screen elements below show how it represents the field of play and state of the game, and uses those inputs to determine which action to take next.

“No, mom, I’m playing this game strictly for research purposes!”

Choose An Algorithm

Once you understand the nature of the problem, you can choose the form of your destructor algorithm.  There are often several potential algorithms which can solve your problem, so you will want to try different algorithms and compare.  There are a few major trade-offs between algorithms, so each one will have some better or worse combination of the following features:

  • Accuracy and susceptibility to overfitting
  • Training time
  • Ability for a human to be able to understand the result
  • Number of hyperparameters
  • Number of features allowed.  For example, a model like ARIMA doesn’t give you many features—it’s just the label behavior over time.

Microsoft has a nice algorithm cheat sheet that I recommend checking out:

It is, of course, not comprehensive, but it does set you in the right direction.  For example, we already know that we want to predict values, and so we’re going into the Regression box in the bottom-left.  From there, we can see some of the trade-offs between different algorithms.  If we use linear regression, we get fast training, but the downside is that if our dependent variable is not a linear function of the independent variables, then we won’t end up with a good result.

By contrast, a neural network regression tends to be fairly accurate, but can take a long time to finish or require expensive hardware to finish in any reasonable time.

Once you have an algorithm, features, and labels (if this is a supervised learning problem), you can train the model.  Training a model involves solving a system of equations, minimizing a loss function.  For example, here is an example of a plot with a linear regression thrown in:

This plot might look familiar if you’re read my ggplot2 series.

In this chart, I have a straight line which represents the best fitting line for our data points, where best fit is defined as the line which minimizes the sum of the squares of errors (i.e., the sum of the square of the distance between the dot and our line).  Computers are great at this kind of math, so as long as we set up the problem the right way and tell the computer what we want it to do, it can give us back an answer.

But we’ve got to make sure it’s a good answer.  That’s where the next section helps.

Validate The Model

Instead of using up all of our data for training, we typically want to perform some level of validation within our training data set to ensure that we are on the right track and are not overfitting our model.  Overfitting happens when a model latches onto the particulars of a data set, leaving it at risk of not being able to generalize to new data.  The easiest way to tell if you are overfitting is to test your model against unseen data.  If there is a big dropoff in model accuracy between the training and testing phases, you are likely overfitting.

Here’s one technique for validation:  let’s say that we reserved 70% of our data for training.  Of the 70%, we might want to slice off 10% for validation, leaving 60% for actual training.  We feed the 60% of the data to our algorithm, generating a model.  Then we predict the outcomes for our validation data set and see how close we were to reality, and how far off the accuracy rates are for our validation set versus our training set.

Another technique is called cross-validation.  Cross-validation is a technique where we slice and dice the training data, training our model with different subsets of the total data.  The purpose here is to find a model which is fairly robust to the particulars of a subset of training data, thereby reducing the risk of overfitting.  Let’s say that we cross-validate with 4 slices.  In the first step, we train with the first 3/4 of the data, and then validate with the final 1/4.  In the second step, we train with slices 1, 2, and 4 and validate against slice 3.  In the third step, we train with 1, 3, and 4 and validate against slice 2.  Finally, we train with 2, 3, and 4 and validate against slice 1.  We’re looking to build up a model which is good at dealing with each of these scenarios, not just a model which is great at one of the four but terrible at the other three.

Often times, we won’t get everything perfect on the first try.  That’s when we move on to the next step.

Tune The Model

Most models have hyperparameters.  For example, a neural network has a few hyperparameters, including the number of training epochs, the number of layers, the density of each layer, and dropout rates.  For another example, random forests have hyperparameters like the maximum size of each decision tree and the total number of decision trees in the forest.

We tune our model’s hyperparameters using the validation data set.  With cross-validation, we’re hoping that our tuning will not accidentally lead us down the road to spurious correlation, but we have something a bit better than hope:  we have secret data.

Evaluate The Model

Model evaluation happens when we send new, never before seen data to the model.  Remember that 20-30% that we reserved early on?  This is where we use it.

Now, we want to be careful and make sure not to let any information leak into the training data.  That means that we want to split this data out before normalizing or aggregating the training data set, and then we want to apply those same rules to the test data set.  Otherwise, if we normalize the full data set and then split into training and test, a smart model can surreptitiously  learn things about the test data set’s distribution and could train toward that, leading to overfitting our model to the test data and leaving it less suited for the real world.

Another option, particularly useful for unlabeled or self-learning examples, is to build a fitness function to evaluate the model.  Genetic algorithms (for a refresher, check out my series) are a common tool for this.  For example, MarI/O uses a genetic algorithm to train a neural network how to play Super Mario World.

He’s no Evander Holyfield, but Mario’s worth a genetic algorithm too.


Just like with data processing, I’m going to split this into two parts.  Today, we’ve looked at some of the theory behind modeling.  Next time around, we’re going to implement a regression model to try to predict salaries.


Data Processing: An Example

This is part four of a series on launching a data science project.

An Example Of Data Processing

Last time around, I spent a lot of time talking about data acquisition, data cleansing, and basic data analysis.  Today, we’re going to walk through a little bit of it with the data professional salary survey.

First, let’s install some packages:

if(!require(tidyverse)) {
  install.packages("tidyverse", repos = "")

if(!require(XLConnect)) {
  install.packages("XLConnect", repos = "")

if(!require(caret)) {
  install.packages("caret", repos = "")

if(!require(recipes)) {
  install.packages("recipes", repos = "")

if(!require(data.table)) {
  install.packages("data.table", repos = "")

if(!require(devtools)) {
  install.packages("devtools", repos = "")

if(!require(keras)) {
  install_keras(method = "auto", conda = "auto", tensorflow = "default", extra_packages = NULL)

The tidyverse package is a series of incredibly useful libraries in R, and I can’t think of doing a data science project in R without it. The XLConnectpackage lets me read an Excel workbook easily and grab the salary data without much hassle. The caret library provides some helpful tooling for working with data, including splitting out test versus training data, like we’ll do below. The recipes package will be useful for normalizing data later, and we will use data.table to get a glimpse at some of our uneven data. We need the devtools package to install keras from GitHub. Keras is a deep learning library which implements several neural network libraries, including TensorFlow, which we will use later in this series. We need to install TensorFlow on our machine. Because this is a small data set, and because I want this to run on machines without powerful GPUs, I am using the CPU-based version of TensorFlow. Performance should still be adequate for our purposes.

Once we have the required packages loaded, we will then load the Excel workbook. I have verified the Excel worksheet and data region are correct, so we can grab the survey from the current directory and load it into salary_data.

wb <- XLConnect::loadWorkbook("2018_Data_Professional_Salary_Survey_Responses.xlsx")
salary_data <- XLConnect::readWorksheet(wb, sheet = "Salary Survey", region = "A4:Z6015")

We can use the glimpse function inside the tidyverse to get a quick idea of what our salary_data dataframe looks like. In total, we have 6011 observations of 26 variables, but this covers two survey years: 2017 and 2018. Looking at the variable names, we can see that there are some which don’t matter very much (like Timestamp, which is when the user filled out the form; and Counter, which is just a 1 for each record.

Observations: 6,011
Variables: 26
$ Survey.Year                <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017...
$ Timestamp                  <dttm> 2017-01-05 05:10:20, 2017-01-05 05:26:2...
$ SalaryUSD                  <chr> "200000", "61515", "95000", "56000", "35...
$ Country                    <chr> "United States", "United Kingdom", "Germ...
$ PostalCode                 <chr> "Not Asked", "Not Asked", "Not Asked", "...
$ PrimaryDatabase            <chr> "Microsoft SQL Server", "Microsoft SQL S...
$ YearsWithThisDatabase      <dbl> 10, 15, 5, 6, 10, 15, 16, 4, 3, 8, 4, 22...
$ OtherDatabases             <chr> "MySQL/MariaDB", "Oracle, PostgreSQL", "...
$ EmploymentStatus           <chr> "Full time employee", "Full time employe...
$ JobTitle                   <chr> "DBA", "DBA", "Other", "DBA", "DBA", "DB...
$ ManageStaff                <chr> "No", "No", "Yes", "No", "No", "No", "No...
$ YearsWithThisTypeOfJob     <dbl> 5, 3, 25, 2, 10, 15, 11, 1, 2, 10, 4, 8,...
$ OtherPeopleOnYourTeam      <chr> "2", "1", "2", "None", "None", "None", "...
$ DatabaseServers            <dbl> 350, 40, 100, 500, 30, 101, 20, 25, 3, 5...
$ Education                  <chr> "Masters", "None (no degree completed)",...
$ EducationIsComputerRelated <chr> "No", "N/A", "Yes", "No", "Yes", "No", "...
$ Certifications             <chr> "Yes, and they're currently valid", "No,...
$ HoursWorkedPerWeek         <dbl> 45, 35, 45, 40, 40, 35, 40, 36, 40, 45, ...
$ TelecommuteDaysPerWeek     <chr> "1", "2", "None, or less than 1 day per ...
$ EmploymentSector           <chr> "Private business", "Private business", ...
$ LookingForAnotherJob       <chr> "Yes, but only passively (just curious)"...
$ CareerPlansThisYear        <chr> "Not Asked", "Not Asked", "Not Asked", "...
$ Gender                     <chr> "Not Asked", "Not Asked", "Not Asked", "...
$ OtherJobDuties             <chr> "Not Asked", "Not Asked", "Not Asked", "...
$ KindsOfTasksPerformed      <chr> "Not Asked", "Not Asked", "Not Asked", "...
$ Counter                    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...

Our first data cleansing activity will be to filter our data to include just 2018 results, which gives us a sample size of 3,113 participants. There are also results for 2017, but they asked a different set of questions and we don’t want to complicate the analysis or strip out the new 2018 questions.

survey_2018 <- filter(salary_data, Survey.Year == 2018)
nrow(survey_2018) # << 3113 records returned

Looking at the survey, there are some interesting data points that we want:

  • SalaryUSD (our label, that is, what we are going to try to predict)
  • Country
  • YearsWithThisDatabase
  • EmploymentStatus
  • JobTitle
  • ManageStaff
  • YearsWithThisTypeOfJob
  • OtherPeopleOnYourTeam
  • DatabaseServers
  • Education
  • EducationIsComputerRelated
  • Certifications
  • HoursWorkedPerWeek
  • TelecommuteDaysPerWeek
  • EmploymentSector
  • LookingForAnotherJob
  • CareerPlansThisYear
  • Gender

For each of these variables, we want to see the range of options and perform any necessary cleanup. The first thing I’d look at is the cardinality of each variable, followed by a detailed anlaysis of the smaller ones.

PrimaryDatabase is another variable which looks interesting, but it skews so heavily toward SQL Server that there’s more noise than signal to it. Because there are so many platforms with 10 or fewer entries and about 92% of entrants selected SQL Server, we’ll throw it out.

rapply(survey_2018, function(x) { length(unique(x)) })

Survey.Year – 1
Timestamp – 3112
SalaryUSD – 865
Country – 73
PostalCode – 1947
[… continue for a while]

  1. ‘United States’
  2. ‘Australia’
  3. ‘Spain’
  4. ‘United Kingdom’
    [… continue for a while]


  1. ‘Full time employee’
  2. ‘Full time employee of a consulting/contracting company’
  3. ‘Independent consultant, contractor, freelancer, or company owner’
  4. ‘Part time’

We can use the setDT function on data.table to see just how many records we have for each level of a particular factor. For example, we can see the different entries for PrimaryDatabase and EmploymentSector below. Both of these are troublesome for our modeling because they both have a number of levels with 1-2 entries. This makes it likely that we will fail to collect a relevant record in our training data set, and that will mess up our model later. To rectify this, I am going to remove PrimaryDatabase as a feature and remove the two students from our sample.

data.table::setDT(survey_2018)[, .N, keyby=PrimaryDatabase]

To the three MongoDB users: you have my sympathy.

data.table::setDT(survey_2018)[, .N, keyby=EmploymentSector]

In a way, aren’t we all students? No. Only two of us are.

Most of these columns came from dropdown lists, so they’re already fairly clean. But there are some exceptions to the rule. They are:

  • SalaryUSD
  • YearsWithThisDatabase
  • YearsWithThisTypeOfJob
  • DatabaseServers
  • HoursWorkedPerWeek
  • Gender

All of these were text fields, and whenever a user gets to enter text, you can assume that something will go wrong. For example:

survey_2018 %>%
  distinct(YearsWithThisDatabase) %>%
  arrange(desc(YearsWithThisDatabase)) %>%

Some are older than they seem.

Someone with 53,716 years working with their primary database of choice? That’s commitment! You can also see a couple of people who clearly put in the year they started rather than the number of years working with it, and someone who maybe meant 10 years? But who knows, people type in weird stuff.

Anyhow, let’s see how much that person with at least 10 thousand years of experience makes:

survey_2018 %>%
  filter(YearsWithThisDatabase > 10000)

Experience doesn’t pay after the first century or two.

That’s pretty sad, considering their millennia of work experience. $95-98K isn’t even that great a number.

Looking at years of experience with their current job roles, people tend to be more reasonable:

survey_2018 %>%
  distinct(YearsWithThisTypeOfJob) %>%
  arrange(desc(YearsWithThisTypeOfJob)) %>%

Next up, we want to look at the number of database servers owned. 500,000+ database servers is a bit excessive. Frankly, I’m suspicious about any numbers greater than 5000, but because I can’t prove it otherwise, I’ll leave them be.

survey_2018 %>%
  distinct(DatabaseServers) %>%
  arrange(desc(DatabaseServers)) %>%

survey_2018 %>%
  filter(DatabaseServers >= 5000) %>%

500K servers is a lot of servers.

The first entry looks like bogus data: a $650K salary, a matching postal code, and 500K database servers, primarily in RDS? Nope, I don’t buy it.

The rest don’t really look out of place, except that I think they put in the number of databases and not servers. For these entrants, I’ll change the number of servers to the median to avoid distorting things.

Now let’s look at hours per week:

survey_2018 %>%
  distinct(HoursWorkedPerWeek) %>%
  arrange(desc(HoursWorkedPerWeek)) %>%

One of these numbers is not like the others.  The rest of them are just bad.

To the person who works 200 hours per week: find a new job. Your ability to pack more than 7*24 hours of work into 7 days is too good to waste on a job making just $120K per year.

survey_2018 %>%
  filter(HoursWorkedPerWeek >= 168) %>%

What would I do with an extra day and a half per week? Sleep approximately an extra day and a half per week.

As far as Gender goes, there are only three with enough records to be significant: Male, Female, and Prefer not to say. We’ll take Male and Female and bundle the rest under “Other” to get a small but not entirely insignificant set there.

survey_2018 %>%
  group_by(Gender) %>%
  summarize(n = n())

To the one Reptilian in the survey, I see you and I will join forces with Rowdy Roddy Piper to prevent you from taking over our government.

survey_2018 %>%
  group_by(Country) %>%
  summarize(n = n()) %>%
  filter(n >= 20)

Probably the most surprising country on this list is The Netherlands.  India is a close second, but for the opposite reason.

There are only fifteen countries with at least 20 data points and just eight with at least 30. This means that we won’t get a great amount of information from cross-country comparisons outside of the sample. Frankly, I might want to limit this to just the US, UK, Canada, and Australia, as the rest are marginal, but for this survey analysis, I’ll keep the other eleven.

Building Our Cleaned-Up Data Set

Now that we’ve performed some basic analysis, we will clean up the data set. I’m doing most of the cleanup in a single operation, but I do have some comment notes here, particularly around the oddities with SalaryUSD. The SalaryUSD column has a few problems:

  • Some people put in pennies, which aren’t really that important at the level we’re discussing. I want to strip them out.
  • Some people put in delimiters like commas or decimal points (which act as commas in countries like Germany). I want to strip them out, particularly because the decimal point might interfere with my analysis, turning 100.000 to $100 instead of $100K.
  • Some people included the dollar sign, so remove that, as well as any spaces.

It’s not a perfect regex, but it did seem to fix the problems in this data set at least.

valid_countries <- survey_2018 %>%
                    group_by(Country) %>%
                    summarize(n = n()) %>%
                    filter(n >= 20)

# Data cleanup
survey_2018 <- salary_data %>%
  filter(Survey.Year == 2018) %>%
  filter(HoursWorkedPerWeek < 200) %>%
  # There were only two students in the survey, so we will exclude them here.
  filter(EmploymentSector != "Student") %>%
  inner_join(valid_countries, by="Country") %>%
    SalaryUSD = stringr::str_replace_all(SalaryUSD, "\\$", "") %>%
      stringr::str_replace_all(., ",", "") %>%
      stringr::str_replace_all(., " ", "") %>%
      # Some people put in pennies.  Let's remove anything with a decimal point and then two numbers.
      stringr::str_replace_all(., stringr::regex("\\.[0-9]{2}$"), "") %>%
      # Now any decimal points remaining are formatting characters.
      stringr::str_replace_all(., "\\.", "") %>%
    # Some people have entered bad values here, so set them to the median.
    YearsWithThisDatabase = case_when(
      (YearsWithThisDatabase > 32) ~ median(YearsWithThisDatabase),
      TRUE ~ YearsWithThisDatabase
    # Some people apparently entered number of databases rather than number of servers.
    DatabaseServers = case_when(
      (DatabaseServers >= 5000) ~ median(DatabaseServers),
      TRUE ~ DatabaseServers
    EmploymentStatus = as.factor(EmploymentStatus),
    JobTitle = as.factor(JobTitle),
    ManageStaff = as.factor(ManageStaff),
    OtherPeopleOnYourTeam = as.factor(OtherPeopleOnYourTeam),
    Education = as.factor(Education),
    EducationIsComputerRelated = as.factor(EducationIsComputerRelated),
    Certifications = as.factor(Certifications),
    TelecommuteDaysPerWeek = as.factor(TelecommuteDaysPerWeek),
    EmploymentSector = as.factor(EmploymentSector),
    LookingForAnotherJob = as.factor(LookingForAnotherJob),
    CareerPlansThisYear = as.factor(CareerPlansThisYear),
    Gender = as.factor(case_when(
      (Gender == "Male") ~ "Male",
      (Gender == "Female") ~ "Female",
      TRUE ~ "Other"

Now we can pare out variables we don’t need. Some of these, like postal code, are interesting but we just don’t have enough data for it to make sense. Others, like Kinds of Tasks Performed or Other Job Duties, have too many varieties for us to make much sense with a first pass. They might be interesting in a subsequent analysis, though.

survey_2018 <- survey_2018 %>%
  # One person had a salary of zero.  That's just not right.
  filter(SalaryUSD > 0) %>%
  select(-Counter, -KindsOfTasksPerformed, -OtherJobDuties, -OtherDatabases, -Timestamp, -Survey.Year, 
         -PostalCode, -n, -PrimaryDatabase)

Now that we have our salary data fixed, we can finally look at outliers. I’d consider a salary of $500K a year to be a bit weird for this field. It’s not impossible, but I am a little suspicious. I am very suspicious of the part-timer making $1.375 million, the federal employee making $1 million, or the New Zealander making $630K at a non-profit.

I’m kind of taking a risk by removing these, but they’re big enough outliers that they can have a real impact on our analysis if they’re bad data.

survey_2018 %>%
  filter(SalaryUSD > 500000) %>%

I think I’d be willing to accept $1.4 million a year to be a manager of none.

On the other side, there are 12 people who say they earned less than $5K a year. Those also seem wrong. Some of them look like dollars per hour, and maybe some are monthly salary. I’m going to strip those out.

survey_2018 %>%
  filter(SalaryUSD < 5000) %>%

For just over a dollar a week, you can hire a data architect.

survey_2018 <- filter(survey_2018, SalaryUSD >= 5000 & SalaryUSD <= 500000)

Data Analysis

We did some of the data analysis up above. We can do additional visualization and correlation studies. For example, let’s look at a quick distribution of salaries after our cleanup work:

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   5000   70000   92000   95186  115000  486000

We can also build a histogram pretty easily using the ggplot2 library. This shows the big clump of database professionals earning beween $70K and $115K per year. This salary distribution does skew right a bit, as you can see.

ggplot(data = survey_2018, mapping = aes(x = SalaryUSD)) +
  geom_histogram() +
  theme_minimal() +
  scale_x_log10(label = scales::dollar)

Not including that guy making $58 a year.

We can also break this down to look by primary job title, though I’ll limit to a couple of summaries instead of showing a full picture.

survey_2018 %>% filter(JobTitle == "Data Scientist") %>% select(SalaryUSD) %>% summary(.)
 Min.   : 45000  
 1st Qu.: 76250  
 Median :111000  
 Mean   :102000  
 3rd Qu.:122000  
 Max.   :160000
survey_2018 %>% filter(JobTitle == "Developer: App code (C#, JS, etc)") %>% select(SalaryUSD) %>% summary(.)
 Min.   : 22000  
 1st Qu.: 60000  
 Median : 84000  
 Mean   : 84341  
 3rd Qu.:105000  
 Max.   :194000
survey_2018 %>% filter(JobTitle == "Developer: T-SQL") %>% select(SalaryUSD) %>% summary(.)
 Min.   : 12000  
 1st Qu.: 66000  
 Median : 87000  
 Mean   : 88026  
 3rd Qu.:110000  
 Max.   :300000

This fit pretty well to my biases, although the max Data Scientist salary seems rather low.


This is only a tiny sample of what I’d want to do with a real data set, but it gives you an idea of the kinds of things we look at and the kinds of things we need to fix before a data set becomes useful.

In the next post, we will get started with the wide world of modeling.

First We Speak Linux Webinar Is Today!

Today’s the day:  We Speak Linux is hosting its inaugural event.  There’s still time to register for the webinar, unless you’re reading this after 12 PM Eastern, in which case you’ve run out of time and must content yourself with watching the recording after the fact and then seeing the next one live.

You can also join our Slack and follow along in the webinar_chat channel during events for an added interactive bonus.

DBAs: Come Learn R With Me

In conjunction with SQL Saturday Madison, I am giving my first full-day training session entitled Enter the Tidyverse:  R for the Data Professional on Friday, April 6th.  I’m using the term “data professional” in particular because I want to hit a relatively under-served part of the community:  database administrators.  I should note that if you’re a database developer, a budding data scientist, or an application developer curious about R, this is a great training for you too, as the skills you’ll pick up are directly transferable to a variety of other jobs, but my pitch in this blog post is to DBAs, as they’re more likely to wonder why they need anything more than T-SQL and maybe a bit of Powershell.

One of my goals with this training is to show database administrators that they have a lot to gain from R as well.  We will see how to visualize DMV data more usefully, build a simple prediction model for messages in the SQL Server error log, and my capstone example will involve tuning backup performance and estimating the impact of a new database on your backup regimen.

If you sign up for the training in Madison, the cost is only $125 and you’ll walk away with a better knowledge of how you can level up your DBA skills with the help of a language specially designed for analysis.  Below is the full abstract for my training session.  If this sounds interesting to you, sign up today!

Course Description

In this day-long training, you will learn about R, the premiere language for data analysis.  We will approach the language from the standpoint of data professionals:  database developers, database administrators, and data scientists.  We will see how data professionals can translate existing skills with SQL to get started with R.  We will also dive into the tidyverse, an opinionated set of libraries which has modernized R development.  We will see how to use libraries such as dplyr, tidyr, and purrr to write powerful, set-based code.  In addition, we will use ggplot2 to create production-quality data visualizations.

Over the course of the day, we will look at several problem domains.  For database administrators, areas of note will include visualizing SQL Server data, predicting error occurrences, and estimating backup times for new databases.  We will also look at areas of general interest, including analysis of open source data sets.

No experience with R is necessary.  The only requirements are a laptop and an interest in leveling up your data professional skillset.

Intended Audience

  • Database developers looking to tame unruly data
  • Database administrators with an interest in visualizing SQL Server metrics
  • Data analysts and budding data scientists looking for an overview of the R landscape
  • Business intelligence professionals needing a powerful language to cleanse and analyze data efficiently


Module 0 — Prep Work

  • Review data sources we will cover during the training
  • Ensure laptops are ready to go

Module 1 — Basics of R

  • What is R?
  • Basic mechanics of R
  • Embracing functional programming in R
  • Connecting to SQL Server with R
  • Identifying missing values, outliers, and obvious errors

Module 2 — Intro To The Tidyverse

  • What is the Tidyverse?
  • Tidyverse principles
  • Tidyverse basics:  dplyr, tidyr, readr, tibble

Module 3 — Dive Into The Tidyverse

  • Data loading:  rvest, httr, readxl, jsonlite, xml2
  • Data wrangling:  stringr, lubridate, forcats, broom
  • Functional programming:  purrr

Module 4 — Plotting

  • Data visualization principles
  • Chartjunk
  • Types of plots:  good, bad, and ugly
  • Plotting data with ggplot2
    • Exploratory plotting
    • Building professional quality plots

Module 5 — Putting it Together:  Analyzing and Predicting Backup Performance

  • A capstone notebook which covers many of the topics we covered today

Course Objectives

Upon completion of this course, attendees will be able to:

  • Perform basic data analysis with the R programming language
  • Take advantage of R functions and libraries to clean up dirty data
  • Build a notebook using Jupyter Notebooks
  • Create data visualizations with ggplot2


No experience with R is necessary, though it would be helpful.  Please bring a laptop to follow along with exercises and get the most out of this course.

Data Processing: The Other 90%

This is part three of a series on launching a data science project.

The Three Steps Of Data Processing

Data processing is made up of a few different activities:  data gathering, data cleansing, and data analysis.  Most estimates are that data scientists spend about 80% of their time in data processing (particularly in data cleansing).  If anything, I consider this an underestimation:  based on my experiences, I would probably put that number closer to 90%.

As with everything else, these separate concepts tend to jumble together and you’ll bounce back and forth between them as you learn about new resources, determine the viability of sources, and try to link data sources together.  But for the purposes of elucidation, we’ll treat them independently.  So let’s start with data gathering.

Data Gathering

At first, you might not know exactly what data you need, but as you flesh out your models and gain a better understanding of the problem, you will go back to the well several times and probably end up getting data from a variety of sourses.  These sources can include, but are not limited to, the following:

  • Internal proprietary data
  • Open data sources (often governmental or academic, but can also be private sources like our data professional survey)
  • Paid APIs or data sources from third parties
  • Data from surveys that you commission

Data Gathering In Our Example

For our example, we will stick to using just the data professional survey.  But if we wanted to expand our analysis out a bit, there are some places we could take it.  For example, we could use the Penn World Table and calculate Purchasing Power Parity GDP per capita to normalize salaries across countries.  This could help us find if there were countries in which database professionals were relatively higher-paid compared to the norm, and is pretty similar to work I did last year.

We could get a geocoding data set to visualize results on a map, or get a data set which has cost of living by ZIP Code.  This would help us normalize salaries within the United States, as long as the ZIP Code data in our set is good enough (spoilers:  it’s not).  We could also use census information to build out more data by ZIP Code or greater metropolitan area.  Finally, we could use data from other surveys to add more information to the sample.  Our sample is pretty small, at just 6000 entries across two years, so supplementing this data could help us a lot.

Data Cleansing

Once we have relevant (or at least potentially relevant) data sets, we probably will want to join them together to gain insight from the mashup of these data sets.  It is rare, however, to have all of your data sets match up exactly on the first try.  Instead, we’re going to have to work with those data sets to get the puzzle pieces to fit together.

Soft Corinthian Data With A Beautiful Grain

For example, we probably need to change the grain of some of our data sets.  Suppose have a data set which is at the daily grain and another which is at the monthly grain.  We can connect these two together, but we have to make a decision.  Do we take the daily grain and aggregate it up to the monthly level?  Do we take the monthly level and create some kind of daily allocation rule?  Do we figure out the month to which the date belongs and include month-level aggregations for each of those daily values?  Each of these is a viable strategy, but has different implications on what you can do with the data.

Next up, you might have to define join criteria.  There won’t always be an obvious natural join key like a date.  You might have a data set from one source which contains product titles and manufacturer codes, and another data set which contains UPCs and gussied up titles.  In this case, you might be able to map manufacturer codes to UPCs using a third data set, or maybe you can write some regular expressions to get the product titles to match up, but there’s work involved.

We may also need to reshape the data.  As mentioned above with grains, we might need to aggregate or disaggregate data to fit a join.  We may also need to perform operations like pivoting or unpivoting data (or gathering and spreading using the tidyverse parlance).

Dealing With Mislabels, Mismatches, And Just Plain Wrong Data

This next category of data cleansing has to do with specific values.  I want to look at three particular sub-categories:  mislabeled data, mismatched data, and incorrect data.

Mislabeled data happens when the label is incorrect.  In a data science problem, the label is the thing that we are trying to explain or predict.  For example, in our data set, we want to predict SalaryUSD based on various inputs.  If somebody earns $50,000 per year but accidentally types 500000 instead of 50000, it can potentially affect our analysis.  If you can fix the label, this data becomes useful again, but if you cannot, it increases the error, which means we have a marginally lower capability for accurate prediction.

Mismatched data happens when we join together data from sources which should not have been joined together.  Let’s go back to the product title and UPC/MFC example.  As we fuss with the data to try to join together these two data sets, we might accidentally write a rule which joins a product + UPC to the wrong product + MFC.  We might be able to notice this with careful observation, but if we let it through, then we will once again thwart reality and introduce some additional error into our analysis.  We could also end up with the opposite problem, where we have missed connections and potentially drop useful data out of our sample.

Finally, I’m calling incorrect data where something other than the label is wrong.  For example, in the data professional salary survey, there’s a person who works 200 hours per week.  While I admire this person’s dedication and ability to create 1.25 extra days per week that the rest of us don’t experience, I think that person should have held out for more than just $95K/year.  I mean, if I had the ability to generate spare days, I’d want way more than that.

Missing Data

People don’t always fill out the entirety of every form, and when you make fields optional, people are liable not to fill them in.  The flipside to this is that if you make optional fields required, people might input junk data just to get the form to submit, so simply marking fields as required isn’t the answer.

When we are missing data, we have a few options available to us.  First of all, if we’re looking at a small percentage of the total, we might just get rid of those records with important, missing data.  Once we start removing more than 2-3% of the data set for this reason, though, I’m liable to get a bit edgy about removing more.  If we’re missing information from non-vital columns like middle initial or address line 3, I’d just remove those features altogether and save the hassle.

Otherwise, if we can’t remove the feature and don’t want to remove the row, we can use statistical techniques to fill in the blanks.  We might substitute missing values with a dummy value, such as “Unknown” or a reasonable default like 0 (assuming 0 is a reasonable default in this case).  We might substitute with an “average” value like mean, median, or mode.  The default here is probably median, but there are times when you want to use mean or mode.  We could also do a quick regression and replace our missing value with the regressed value, assuming that there’s some correlation (positive or negative) between the filled-in features and our missing feature.

These statistical techniques can help fill in gaps, but I wouldn’t use them for more than a few percent of the data; if you’re seeing 15+ percent of the feature values missing, this is probably a compromised feature and you don’t want to use it in analysis.  Also, by applying these statistical techniques, we are knowingly introducing noise, making our models that much less accurate.  That is, we are saying that the value is X, but it’s probably somewhere in the range { X-y, X+y} and we are giving undue precision based on our own made-up calculations in the hopes that y is small enough that it won’t matter too much.  If this hope turns out to be the case, the benefit to salvaging incomplete records could overcome the cost of adding more noise to our analysis.

Inconsistent Data

As I mentioned earlier in the series, one of my data adages is that independent systems tend to end up with inconsistent data.  Sometimes people forget to fill out both systems the same way.  Sometimes there are typos in one data source but not the other (or the typos are different in each).  Sometimes there are subtle differences in data sets which lead to differing results.  Sometimes one data source is newer than the other, or at least one particular record is newer than what’s in the other system.

Regardless of the reason, we need to be able to handle these differences.  One way to handle differences is to make one data source canonical and trust it over the other when there comes time for a discrepancy.  If there’s an authoritative source, it makes sense to behave this way, and your business team might be able to explain which sources (if any) are authoritative.

More commonly, you will have to institute one or more rules to handle data discrepancies.  For example, if both records have a timestamp, pick the later timestamp…although wall clocks lie.  Another example of a rule might be, if the discrepancy is over a numeric value, go with the lower number as it is more likely to be correct.  These rules will be specific to your data sources and systems, and will often require quite a bit of sussing out.

Misshapen Data

If you happen to have data stored in a textual format, you can end up with misshapen data.  For example, you might have XML or JSON data where some of the records are in an invalid format.  Or maybe you have newlines in the middle of a record.  Maybe you have too many delimiters in that CSV, or maybe not enough.  Maybe the file starts as a CSV and then moves to pipe-delimited in the middle because the source system appended data over time using different output mechanisms.

Regular expressions can help out here sometimes.  You might also be able to get satisfaction from the owner of the source data and have them fix the problem.  Sadly, it’s just as likely that your team becomes the owner of the data and gets the pleasure of figuring it out.  If you store data in relational databases and adhere to first normal form (or better), data shape is already enforced, so you can pat yourself on the back for this one and move on to the next problem.

Data Shaping

Speaking of data shapes, we often need to perform some kinds of data shaping activities to turn our raw data into something useful for modeling.  Examples of data shaping include:

  • Vectorizing words.  That is, turning words in a document into numbers for analysis.
  • Categorizing data.  That is, turning strings (or maybe numeric values) into factors.
  • Normalizing values.  That is, transforming numeric data to have a mean of 0 and a standard deviation of 1.  The benefit of normalization is that you can talk about two features which have radically different scales in the context of the same model.  Let’s say that we want to measure a person’s desire to purchase a car based on its gas mileage and its sticker price.  Gas mileage typically ranges from about 15-50, but sticker price can range from $9000-90,000 (or more).  Both the sheer numbers and the wide range of sticker price will overwhelm gas mileage in many models, so the way we get around this is to normalize both features so that each has a mean of 0 and standard deviation of 1.  That way, one feature’s effect cannot swamp another’s.
  • Binning data.  That is, converting continuous values to discrete.  For example, we might have the age of each person in our data set, but we’re more interested in age cohorts, like 18-25 or 30-34.

Data Analysis

As part of building a data dictionary, I’ve already brought up a couple simple data analysis concepts.  Here, I’ll go into a few, focusing on techniques common in Exploratory Data Analysis (EDA).  The goal with EDA is to summarize and visualize the data in a way that furthers your understanding of the data.

Cardinality Analysis

The first simple technique you can use is finding the cardinality of variables.  For example, in the salary survey, there were four options available for Employment Status.

Not included: use multiverse versions of self to do work, thereby freeing up time to lounge on a beach in Cabo.

Cardinality tells us a few things.  For a text field like employment status, it indicates that we probably got the data from a proscribed list—often a dropdown list or set of radio buttons—and we probably want to treat this as categorical data, as people choose one to the exclusion of others.

For free-form entry, like number of hours worked, looking at the cardinality lets us see how many different values people chose, and if you compare the number selected versus the range from minimum to maximum, you can get a fair idea of how packed this set is.

Summarization And Visualization

Next up, we can run a five-number summary of a feature.

The 5-Number Summary: buy five numbers, get one free.

This summary, which is easy to perform in R, gives us five pertinent values:  the minimum, maximum, 25th percentile, 75th percentile, and 50th percentile (i.e., median).  R also gives you the mean, which isn’t technically part of the five-number summary but is helpful, as the difference between median and mean clues you into how skewed the distribution of your sample data is.

You can also visualize these five-number summaries with a box plot.

A box plot.  Buy five boxes, get one free?

This type of plot will show you min, max, 25th, 50th, and 75th percentiles, where 25-50-75 make up the box and min and max are the furthest points in the plot.  This plot also shows you “expected” values (those within 1.5 * [75th percentile – 25th percentile], shifted to the next data point in each direction), as well as outliers—those which fall outside the interquartile range.  Box plots are great at visualizing differences by category.

When looking at the distribution of data within a category, you can easily build a histogram.

A histogram using base-10 logged values because if you can’t do exponential math in your head, get out of my classroom.

This distribution visualization lets us see the skew—in this case, our plot skews left, meaning that the left-hand side of the median is more filled out than the right-hand side.  This is fairly common when looking at income data, so I wasn’t surprised to see this setup when I created the above graph using the 2017 survey data.

Correlation Checking

Another part of data analysis involves testing for correlation between explanatory variables.  You want correlation between your independent (explanatory) variables and your dependent variable—that’s what data analysis is all about, after all—but having tightly related explanatory variables can lead to analytical problems down the road.  This correlation is called multicollinearity and is one of the bugbears of linear regression analysis.

As an example of this, suppose that we want to regress plant growth rates using two variables:  amount of rainfall in inches and amount of precipitation in centimeters.  There is collinearity between rainfall in inches and precipitation in inches:  precipitation is rainfall + snowfall, so we can easily get from one to the other and their movements over time will be linked.  If we use both of these variables to try to model plant growth, it becomes harder for the algorithm we choose to differentiate between the two variables and assign them the correct rate.

The cor() function in R gives us correlation between two variables.

Diamond dimension correlations.

In this case, depth and table have a mild, negative correlation.  By contrast, the x and y dimensions are highly correlated.  If I were performing a linear regression, I would have no qualms about including both depth and table, but would think twice about including both x and y.

Conclusion (For Now)

In the next post, I will perform some data cleansing & analysis work on the survey, as this post is long enough as-is.  As mentioned, this is by far the longest phase of any data science project; what I’ve touched on in this post is just a brief smattering of the things you should expect to try when working through a project.

Building Business Understanding

This is part two of a series on launching a data science project.

How Is Babby Data Science Project Formed?

Behind each data science project, there is (hopefully) someone higher up on the business side who wants it done.  This person might have been the visionary behind this project or might simply be the sponsor who drives it because of the project’s potential value.  Nevertheless, the data science team needs to seek out and gather as much information about that champion’s vision as possible.  In a perfect scenario, this is the person handing out sacks of cash to you, and you want those sacks of cash because they buy pretty hardware and let you bring in smart people to work with (or for) you.  You might even have several people interested in your project; if so, you’ll want to build a composite vision, one which hopefully includes all of the members’ visions.  Just keep in mind that sometimes you can’t combine everybody’s dreams and get a coherent outcome, so you’ll need to drive the champion(s) toward clarity.  Forthwith are a few clues to help.

Learn The Domain

The first clue is figuring out the domain.  This is asking questions like what the company does, what other companies in the industry do, what kind of jargon people use, etc.  The more you know, the better prepared you are to understand the mind(s) of your champion(s).  But even if you’ve been at that company for a long time and have a detailed understanding of the business, you still want to interview your champion(s) and ask questions which expose the ideal outcome.

Stop, Collaborate, and Listen

The second clue is simple:  listen.  When interviewing people, listen for the following types of questions:

  • How much / how many?
  • Which category does this belong to?
  • How can we segment this data?
  • Is this weird?
  • Which option should I choose?

Each of these is a different type of problem with its own set of statistical techniques and rules.  For example, the “Is this weird?” question relates to anomaly detection:  finding outliers or “weird” results.  Figuring out which of these types of questions is most important to your champion is crucial to delivering the right product.  You can build the absolute best regression ever, but if the person was expecting a recommendation engine, you’re going to disappoint.

As you listen to these types of questions, your goal is to nail down a specific problem with a specific answer.  You want to narrow down the scope to something that your team can achieve, ideally something with a built-in measure for success.  For example, here are a few specific problems that we could go solve:

  • Find a model which predicts quarterly sales to within 5% no later than 30 days into the quarter.
  • Given a title and description for a product, tell me a listing category which Amazon will, with at least 90% confidence, consider valid for this product.
  • Determine the top three factors which most affect the number of years the first owner holds onto our mid-range sedan.

With a specific problem in mind, you can look for relevant data.  Of course, you’ll probably need to modify the scope of this problem over time as you gather new information, but this gives you a starting point for success.  Also, don’t expect something as clear-cut as the above early on; instead, people will hem and haw, not quite sure what they really want.  You can take a fuzzy goal into data acquisition, but as you acquire data, you will want to work with the champion to focus down to a targeted and valuable problem.

Dig For Data

Once you have an interesting question, or even the bones of a question, start looking for data.  Your champion likely has a decent understanding of what is possible given your data, so the first place to look is in-house data sources like databases, Excel, flat files, data accessible through internal APIs, and even reports (generated reports like PDFs or even printed-out copies).  Your champion will hopefully be able to point you in the right direction and tell you where some of this data is located—especially if it’s hidden in paper format or in a spreadmart somewhere—but you’re going to be doing a lot of legwork between here and the data processing phase, and you’ll likely bounce back and forth between the two phases a number of times.

As you gather data resources, you will probably want to build a data dictionary for your data sources.  A great data dictionary will include things like:

  • The data type of each attribute:  numeric, string, categorical, binary.
  • The data format:  CSV, SQL Server table, Hive table, JSON file, etc.
  • The size of the data and number of records.
  • The enumeration of valid categorical values.
  • Other domain rules (if known).

I’d love to say that I always do this…but that’d be a lie.  Still, as the saying goes, hypocrisy is the tribute that vice pays to virtue.

Learn Your Outputs

While you’re looking for data and focusing in on the critical problem, you also need to figure out the endgame for your product.  Will there be a different engineering team (or multiple teams?) expecting to call a microservice API and get results back?  Will you get a set of files each day and dump the results into a warehouse?  What’s the acceptable latency?

The Engineering team should help solve this technical problem, although your champion should have insight here depending upon how the business side will need to use your results.  If the business side is already getting files in once a day, they may be fine with your process running overnight and having results in a system by 8 AM, when the analysts start showing up at work.  By contrast, you may have a fraud detection system which needs to make a decision in milliseconds.  These two systems will have radically different sets of requirements, even if the output looks the same to the business side.

Going Through An Example

My motivating example, as mentioned yesterday, is data professional salaries—figuring out how to get more money does, after all, motivate me!

Let’s suppose we work for Data Platform Specialists, a company dedicated to providing DBAs and other data platform professionals with valuable market knowledge.  We have come into possession of a survey of data professionals and want to build insights that we can share with our client base.

If you haven’t seen the survey yet, I recommend checking it out.

You can tell that this is a quality survey because it’s in blue, which is the most serious of survey result colors.

Once we have the salary data, we want to start building a data dictionary and see what the shape of the data looks like.  We’d get information on the total number of rows, note that this is stored in Excel on a single worksheet, and then make some notes on the columns.  For example, a number of these features are categorical:  for example, TelecommuteDaysPerWeek has six options, ranging from “less than 1” to “5 or more.”  By contrast, hours worked per week is an integer, which ranges from 5 to 200 (umm…).

There are quite a few columns, most of which originally came from dropdowns rather than users typing the data in.  This is good, because users are typically the biggest enemy of clean data.  But even in this example, we can see some interesting results:  for example, about halfway through the image, you can see “111,000” in the SalaryUSD column.  It turns out that this was a string field rather than numeric.  If you simply try to turn it into numeric, it will fix “111,000” but it’d turn a German’s entry of “111.000” from $110,000 to $111 if you’re in the US.  But I’m getting a bit ahead of myself here…

Where I want to go first is, what are the interesting types of questions we can ask given our vast wealth of domain knowledge and this compendium of valuable pricing insight?  (Too obsequious?  Maybe.)

  • How much money does a DBA make?  Similarly, how much does a data scientist make, or a developer who specializes in writing T-SQL?
  • Which category of DBA (e.g., junior, mid-level, senior) does a particular type of work?
  • How can we segment the DBAs in our survey?
  • Suppose I work 80 hours per week.  Compared to my peers, is this many hours per week weird?
  • Which option should I choose as a career path?  DBA?  Data scientist?  BI specialist?

Talking this through with our champion and other stakeholders, we can talk through some of the information we’ve already gathered, prime them toward asking focused questions, and narrow down to our question of interest:

How much money should we expect a data professional will make?

Well, that’s…broad.  But this early on in the process, that’s probably a reasonable question.  We might not want to put hard boundaries on ranges yet, but as we go along, we can narrow the question down further to something like, “Predict, within $5K USD, how much we should expect a data professional to make depending upon country of residence, education level, years of experience, and favorite Teenage Mutant Ninja Turtle (original series only).”

The end product that we want to support is a small website which allows people to build profiles and then takes that profile information and gets an estimate of how much they could make in different roles.  Our job is to build a microservice API which returns a dollar amount based on the inputs we define as important.


Today’s post was all about getting into the business users’ heads.  These are the people handing us big sacks of cash, after all, so we have to give them something nice in return.  In the next post, we’ll go a lot deeper into data processing and ask the question, if data platform professionals are the gatekeepers between good data and bad, why are we so bad at filling out forms?

The Microsoft Team Data Science Process

This is part one of a series on launching a data science project.

This is the beginning of a series of posts around growing a data science project from the germ of an idea to its fruition as a stable oak.  Before I get into the process, I want to start with a few data adages to which I stubbornly subscribe and which drive the need for quality processes.

Data Adages

You may disagree with any of these adages, but they drive my decision-making processes and I believe that they feed at least a little bit of the paranoia necessary to be an effective data architect.

Clean Data Is An Aspiration, Not A Reality

The concept of “clean” data is appealing to us—I have a talk on the topic and spend more time than I’m willing to admit trying to clean up data.  But the truth is that, in a real-world production scenario, we will never have truly clean data.  Whenever there is the possibility of human interaction, there is the chance of mistyping, misunderstanding, or misclicking, each of which can introduce invalid results.  Sometimes we can see these results—like if we allow free-form fields and let people type in whatever they desire—but other times, the error is a bit more pernicious, like an extra 0 at the end of a line or a 10-key operator striking 4 instead of 7.

Even with fully automated processes, we still run the risk of dirty data:  sensors have error ranges, packets can get dropped or sent out of order, and services fail for a variety of reasons.  Each of these can negatively impact your data, leaving you with invalid entries.

Data Source Disagreements Will Happen

If you have the same data stored in two systems, there will inevitably be disagreements.  These disagreements can happen for a number of reasons, including:

  1. Different business rules mean that different subsets of data go into each system.  For example, one system might track data on work items, and a separate system could track data on work items specifically for Cap-X projects.  If you don’t understand the business rules of each system, you might look at the difference in numbers and get confused.
  2. With manual data entry, people can make mistakes, and those mistakes might manifest in different ways in separate systems.  If a person has to type data into two systems, the likelihood of typos affecting each system exactly the same way is fairly low.
  3. Different systems might purport to be the same but actually are based on different data sources.  For example, the Penn World Table has two different mechanisms to calculate GDP:  expenditure-side GDP and output-side GDP.  In an ideal world, these are the same thing, but in reality, they’re a little bit different.  If I build one system based off of expenditure-side GDP and you build another system based off of output-side GDP, our calculations will clash even though they’re supposed to represent the same thing.
  4. Some systems get updated more frequently than others, so one side might have newer data, even if they both come from the same source and have the same rules and calculations applied.
  5. Even in a scenario where you are reading from a warehouse which gets its data from a single source system, there is still latency, meaning that you might get an extract of data from the warehouse which is out of date.  That too can lead to data discrepancies between sources.

You Will Always Have More Questions Than Data

This seems pretty self-explanatory—our ability to collect and process information is finite, whereas the set of questions we could ask is infinite.  You might be able to collect an exhaustive data set about a very particular incident or set of incidents, but there are always more and broader questions a person can ask for which the data is not available.  For example, let’s say that we have a comprehensive set of data about a single baseball game, including lineups, game actions, pitch locations, bat speeds on contact, and so on.  No matter how detailed the data you provide, someone will be able to ask questions that your data cannot answer.  One class of these questions involves trying to discern human behavior:  why the manager picked one reliever over another, why the runner decided to advance from first to third base on a single to left field in the 4th inning, why this pitcher followed up a four-seam fastball inside with a changeup outside, etc.

Decision-Makers Often Don’t Know The Questions They’ll Have

I’ve built a few data warehouses in my time.  The most frustrating part of building a data warehouse is that you have to optimize it for the question that people have, but it’s hard for people to imagine the questions that they will have far enough in advance that you can develop the thing.  Decision-makers tend not to know the types of questions they can ask, including whether those questions are realistic or reasonable, until you prod them in a direction.

The even worse part is, you’ll be able to answer some of their questions, but invariably they will have questions which they cannot answer using your system, meaning either that you extend the system to answer those questions as well, users find some other way to satisfy their curiosity, or they forget about the question and potentially lose a valuable thread.

We have ways of coping with this, like storyboarding, iterative development, and storing vast amounts of semi-structured data, but it’s tough to figure out what to include in your data lake when you don’t have a proscribed set of required questions to answer (like, for example, a set of compliance forms you need to fill out regularly).

Data Abstracts The Particulars Of Time And Place

This is one that I have repeatedly stolen over the years from FA Hayek, who made this point in his essay The Use of Knowledge in Society (which ended up winning him a Nobel Prize three decades later).  We abstract and aggregate data in order to make sense of it, but that data covers up a lot of deeper information.  For example, we talk about “the” price of something, but price—itself an abstraction of information—depends upon the particulars of time and place, so the spot price of a gallon of gasoline can differ significantly over the course of just a few miles or a few days.  We can collect the prices of gasoline at different stations over the course of time and can infer and adduce some of the underlying causes for these levels and changes, but the data we have explains just a fragment of the underlying reality.

The Need For Process

I consider all of the adages above to be true, and yet it’s my job to figure something out.  To deal with these sorts of roadblocks, we build processes which give us structure and help us navigate some of the difficulties of managing this imperfect, incomplete, messy data.

Over the course of this series, I’m going to cover one particular process:  the Microsoft Team Data Science Process.  I don’t follow it strictly, but I do like the concepts behind it and I think it works well to describe how to increase the likelihood of launching a good data science project.

The Team Data Science Project Lifecycle (Source)

There are a couple of things that I like about this process.  First, it hits most of the highlights:  I think the combination of business understanding, data acquisition, modeling, and deployment is probably the right level of granularity.  Each of these has plenty of details that we can (and will) dig into, but I think it’s a good starting point.

The other thing that I like about this process is that it explicitly recognizes that you will bounce around between these items—it’s not like you perform data acquisition once and are done with it.  Instead, you may get into that phase, gather some data, start modeling, and then realize that you need to go back and ask more pointed business questions, or maybe you need to gather more data.  This is an explicitly iterative process, and I think that correctly captures the state of the art.

Our Sample Project

Through this series, I’m going to use the 2018 Data Professionals Salary Survey.  I took a look at the 2017 version when it came out, and then another look at the 2017 survey during my genetic programming series, but now I want to use the latest data.  As we walk through each step of the Team Data Science process, we’ll cover implementation details and try to achieve a better understanding of data professional salaries.  Of course, the key word here is probably try