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I spent the last couple of months analyzing data from sensors, surveys, and logs. No matter how many charts I created, how well sophisticated the algorithms are, the results are always misleading.

Throwing a random forest at the data is the same as injecting it with a virus. A virus that has no intention other than hurting your insights as if your data is spewing garbage.

Even worse, when you show your new findings to the CEO, and Oops guess what? He/she found a flaw, something that doesn’t smell right, your discoveries don’t match their understanding about the domain —

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That’s not bad at all. What if your findings were taken as a guarantee, and your company ended up making a decision based on them?.

You ingested a bunch of dirty data, didn’t clean it up, and you told your company to do something with these results that turn out to be wrong. You’re going to be in a lot of trouble!.

Incorrect or inconsistent data leads to false conclusions. And so, how well you clean and understand the data has a high impact on the quality of the results.

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For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services. In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions. In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers. Garbage in, garbage out.

For these reasons, it was important to have a step-by-step guideline, a cheat sheet, that walks through the quality checks to be applied.

But first, what’s the thing we are trying to achieve?. What does it mean quality data?. What are the measures of quality data?. Understanding what are you trying to accomplish, your ultimate goal is critical prior to taking any actions.

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Frankly speaking, I couldn’t find a better explanation for the quality criteria other than the one on Wikipedia. So, I am going to summarize it here.

Another thing to note is the difference between accuracy and precision. Saying that you live on the earth is, actually true. But, not precise. Where on the earth?. Saying that you live at a particular street address is more precise.

Missing data is going to happen for various reasons. One can mitigate this problem by questioning the original source if possible, say re-interviewing the subject.

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Age, say 10, mightn’t match with the marital status, say divorced. A customer is recorded in two different tables with two different addresses.

The weight may be recorded either in pounds or kilos. The date might follow the USA format or European format. The currency is sometimes in USD and sometimes in YEN.

The workflow is a sequence of three steps aiming at producing high-quality data and taking into account all the criteria we’ve talked about.

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What you see as a sequential process is, in fact, an iterative, endless process. One can go from verifying to inspection when new flaws are detected.

Inspecting the data is time-consuming and requires using many methods for exploring the underlying data for error detection. Here are some of them:

A summary statistics about the data, called data profiling, is really helpful to give a general idea about the quality of the data.

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For example, check whether a particular column conforms to particular standards or pattern. Is the data column recorded as a string or number?.

How many values are missing?. How many unique values in a column, and their distribution?. Is this data set is linked to or have a relationship with another?.

By analyzing and visualizing the data using statistical methods such as mean, standard deviation, range, or quantiles, one can find values that are unexpected and thus erroneous.

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. Some countries have people who earn much more than anyone else. Those outliers are worth investigating and are not necessarily incorrect data.

Several software packages or libraries available at your language will let you specify constraints and check the data for violation of these constraints.

Moreover, they can not only generate a report of which rules were violated and how many times but also create a graph of which columns are associated with which rules.

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The age, for example, can’t be negative, and so the height. Other rules may involve multiple columns in the same row, or across datasets.

Data cleaning involve different techniques based on the problem and the data type. Different methods can be applied with each has its own trade-offs.

Irrelevant data are those that are not actually needed, and don’t fit under the context of the problem we’re trying to solve.

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Similarly, if you were interested in only one particular country, you wouldn’t want to include all other countries. Or, study only those patients who went to the surgery, we wouldn’t include everyone —

Only if you are sure that a piece of data is unimportant, you may drop it. Otherwise, explore the correlation matrix between feature variables.

And even though you noticed no correlation, you should ask someone who is domain expert. You never know, a feature that seems irrelevant, could be very relevant from a domain perspective such as a clinical perspective.

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Make sure numbers are stored as numerical data types. A date should be stored as a date object, or a Unix timestamp (number of seconds), and so on.

This is can be spotted quickly by taking a peek over the data types of each column in the summary (we’ve discussed above).

A word of caution is that the values that can’t be converted to the specified type should be converted to NA value (or any), with a warning being displayed. This indicates the value is incorrect and must be fixed.

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Pad strings: Strings can be padded with spaces or other characters to a certain width. For example, some numerical codes are often represented with prepending zeros to ensure they always have the same number of digits.

This categorical variable is considered to have 5 different classes, and not 2 as expected: male and female since each value is different.

A bar plot is useful to visualize all the unique values. One can notice some values are different but do mean the same thing i.e. “information_technology” and “IT”. Or, perhaps, the difference is just in the capitalization i.e. “other” and “Other”.

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Therefore, our duty is to recognize from the above data whether each value is male or female. How can we do that?.

The second solution is to use pattern match. For example, we can look for the occurrence of m or M in the gender at the beginning of the string.

The third solution is to use fuzzy matching: An algorithm that identifies the distance between the expected string(s) and each of the given one. Its basic implementation counts how many operations are needed to turn one string into another.

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Furthermore, if you have a variable like a city name, where you suspect typos or similar strings should be treated the same. For example, “lisbon” can be entered as “lisboa”, “lisbona”, “Lisbon”, etc.

If so, then we should replace all values that mean the same thing to one unique value. In this case, replace the first 4 strings with “lisbon”.

Watch out for values like “0”, “Not Applicable”, “NA”, “None”, “Null”, or “INF”, they might mean the same thing: The value is missing.Standardize

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The hight, for example, can be in meters and centimetres. The difference of 1 meter is considered the same as the difference of 1 centimetre. So, the task here is to convert the heights to one single unit.

For dates, the USA version is not the same as the European version. Recording the date as a timestamp (a number of milliseconds) is not the same as recording the date as a date object.

It can also help in making certain types of data easier to plot. For example, we might want to reduce skewness to assist in plotting (when having such many outliers). The most commonly used functions are log, square root, and inverse.

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Student scores on different exams say, SAT and ACT, can’t be compared since these two exams are on a different scale. The difference of 1 SAT score is considered the same as the difference of 1 ACT score. In this case, we need re-scale SAT and ACT scores to take numbers, say, between 0–1.

While normalization also rescales the values into a range of 0–1, the intention here is to transform the data so that it is normally distributed.

Depending on the scaling method used, the shape of the data distribution might change. For example, the “Standard Z score” and “Student’s t-statistic” (given in the link above) preserve the shape, while the log function mighn’t.

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Given the fact the missing values are unavoidable leaves us with the question of what to do when we encounter them. Ignoring the missing data is the same as digging holes in a boat; It will sink.

If the missing values in a column rarely happen and occur at random, then the easiest and most forward solution is