- Even the most advanced learning algorithms need some guidance in order to digest complex data.
- Cleaning data can be a colossal endeavor, but with the right tools it can be much easier and cost-effective to do.
- High quality, clean data is a valuable resource that’s costly to acquire and process.
Data powers a lot of our modern world. But most of us who aren’t data scientists or software engineers don’t understand the depth and complexity of the processes like data cleaning that turn mountains of data points into useful business insights.
Data cleaning is the process of taking data as you currently have it, and tidying it up by correcting errors, inaccuracies, duplicate entries, and so forth.
In this article...
What is data cleaning?
Much like metal ores, data has to be refined from its raw state for it to be anything more than a collection of numbers and values. That process is commonly referred to as “data cleaning”
Like everything in the tech industry, there are more nuances to it than that, and a host of other terms with overlapping or adjacent definitions.
What is involved in data cleaning?
Again, like refining precious metal ores, data cleaning is a multiphase process, and it starts with gathering and evaluating the raw material.
Data aggregation and auditing
It’s common for data to be stored in multiple places before the cleaning process begins. Maybe it’s lead contact info scattered across a CRM, a few spreadsheets, and perhaps even a few physical notepads, just for starters. Data aggregation harvests all of that, and pools it into a single “source of truth.”
One of the most straightforward improvements that can be made to a recurring data cleaning process is automating this aggregation step. Human error is a concern with any portion done manually, and automation can facilitate real-time updates if done right.
Once collected, data is audited for quality and “integrity” in a number of areas, including:
- Accuracy—meaning whether or not the data is “true” in a given context. One of the most widely used accuracy systems is the Address Verification System (AVS) that checks credit card info against the billing address during an online purchase.
- Completeness—whether or not every required input has a value. In the case of leads, it’s not much help if the entry has a first name, but no last name (or email/social links/etc.), which can render the entry virtually useless.
- Consistency—some entries can be found in multiple places (say, contact info pulled from more than one source), and conflicts here can cause issues (e.g. “Do I use this email or that one to contact John Doe?”)
- Validity—as in, “statistical validity”; data often must meet certain constraints for it to make sense when compared to other values (like a form with a box that will only accept numerical values). Most validity issues are found in data captured by a legacy system.
- Uniformity—computers often don’t do a great job of comparing numbers and values against each other unless they’re all formatted in the same way. For example, it’s easier to track weather changes if all temperature readings are all in Celsius or Fahrenheit.
Taking inventory of what issues the data has in its raw form provides a starting point for the actual cleaning process. After all, how do you clean it if you don’t know what’s “dirty?”
Data cleaning workflows
Next, all of the issues discovered in the previous step need to be examined. This part is complex, and not every data entry can be salvaged. Some issues can be fixed, some can be partially repaired, and some can’t be remedied at all.
Whether done manually or via automation tools, there’s a bit of triage that happens as data cleaning progresses through the whole list. Some data will need little-to-no cleaning, and some can’t be fixed at all (such as incomplete entries, where there’s no way to accurately fill in the missing values).
Most will require a measure of cleaning before it’s useful.
Different quality issues require different methods of correction, such as:
- Parsing—some values are easier to process than others. Numerical values, for instance, are easier to understand, check for quality, and are generally easier to correct. Some require analysis just to understand the input like with any “unstructured” data input.
- Duplicate elimination—multiple entries, as mentioned above, can create problems, so a lot of effort goes into reconciling inconsistencies between them. Ideally, correct information is identified, and erroneous duplicates are eliminated from the database.
- Statistical analysis—even in business contexts, controlling for anomalies and extreme outliers is an important practice. While some use cases are more necessary than others, applying statistical models to the data can help in preventing a handful of entries from skewing the numbers. And while outliers aren’t always deleted, pinpointing them helps control for them, and allows teams to address them separately.
- Scrubbing for sensitive data—personally identifying information (PII) can be an issue for a number of industries and use cases, and proper handling of that info may be required for legal compliance. In some cases, the data can be removed or otherwise completely hidden. In others, it has to be visible to some, but permission controls have to be put in place to protect privacy.
- Data transformation—even when data is fairly clean to start with, there is usually some amount of reformatting required to enable analytics tools to effectively use it. That’s where data transformation comes in. Often part of an Extract-Transform-Load (ETL) function, this is where data is standardized and aligned with the destination format.
Based on the data errors and the matching cleaning methods, a workflow is specified. The workflow is a battle plan for properly addressing the issues and cleaning the whole data set.
Automation in data cleaning
Automation often plays a part in data cleaning workflows, though the level of automation will depend on a number of factors.
The most advanced workflows accomplish nearly all the work via automation. In fact, scaling workflows is nearly impossible without also increasing the level of implemented automation.
However, it’s not uncommon for parts of the workflow to continue relying on manual processes well after an automation solution is both effective and financially viable.
Also, complete automation is difficult to achieve, and is usually inadvisable. Some level of human oversight provides a critical failsafe to catch errors the machine isn’t able to recognize.
Plus, automation may be incapable of repairing some entries that a human might still be able to correct. Case in point, incomplete entries are nearly impossible for automation to fix, but in a few cases they can be filled in by a person with relative ease.
Setting those considerations aside, deciding how, when, and where to implement automation can be difficult. On one hand, manual processes are time-consuming, tedious, and prone to human error.
On the other, automated workflows can be computationally expensive, difficult to implement and deploy, and someone has to build the automation in the first place.
Ultimately, the “right” answer will depend on the specifics of the use case. Though many organizations find success with outsourcing.
Post-processing, prevention, and policy
Once the workflow is completed, there may be some final details to address—entries which need to be addressed manually, reviewing results for integrity, and so on. Once that’s all handled, the data should be ready to be forged into your business tool of choice.
Taking all of the effort a single workflow requires, it’s easy to see why so many organizations avoid data cleaning. However, with the right systems, processes, and policies, the cost of data cleaning can be reduced dramatically.
Once in place, and when properly implemented, data quality culture can dramatically decrease overhead for the workflow, and boost data effectiveness.
Improving the quality of collected data is part engineering, and part management.
The data cleaning process can be improved through methods like:
- Removing or updating legacy systems
- Choosing technology tools that fit the use case best
- Building the system to support integration and interoperability between apps
- Designing and implementing automation for the most time-consuming and error-prone tasks
Similarly, policy can be used to promote data quality throughout the organization with tactics such as:
- Driving commitment to data quality and leading by example
- Providing needed support and resources to facilitate improvements
- Promoting awareness of the importance of data quality
- Encouraging cooperation between departments
- Measuring data quality efforts, and celebrating successes
Getting started with data cleaning
There are quite a few up front considerations when decided if you need to clean your data.
- What data do you have?
- Where is it being collected?
- Where is it being stored?
- Where are you aggregating it to?
- What cleaning needs to be done?
As mentioned above, there are tools that can help with this, but most are use case-specific, usually divided along industry lines or needed integrations.
It’s also important to note that many of the third-party tools available will focus on a particular part of the process: data aggregation, data cleaning/scrubbing, data analytics, etc. Finding the right tools may be as simple as grabbing an all-in-one solution, or it may require multiple, distinct tools integrated together.
For those looking for a place to start their search, we can help you start on the right foot. Keep in mind, that the best data quality tools don’t just make it easy for the computer to understand the data. It also makes the data easier for humans to parse and visualize.
Ultimately, good, clean data is a valuable resource that’s costly to acquire and process. That’s why so many brands fail to benefit from their data. But with the right refining systems in place, even teams with limited resources can reap the rewards.
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