Guide to data cleaning and processing – process overview and main challenges 

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Guide to data cleaning and processing – process overview and main challenges 
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Data is an invaluable asset for organizations, driving their operational decisions and bringing them closer to customer expectations. That said, the more data you gather and handle, the more susceptible your company can become to mistakes based on false insights. 

The best way to ensure that your data is credible is to incorporate data cleaning and data processing into your business. We cover both of these processes in the following article.  

Data cleaning process – how to clean data?  

Data cleaning is the process of fixing or deleting any errors and inconsistencies in your data, such as duplicates, erroneous formatting and labeling, and corrupted records. This can happen when you merge or integrate data from numerous sources.  

Here’s how you can ensure high data quality and accuracy for your projects with data cleansing.  

Data cleaning – overview  

Let’s now take a look at the steps you should take to enable effective data cleaning.  

Step 1: Remove unwanted entries 

First, take a deep dive into your data set and look for any duplicated or irrelevant data. If you find any, remove it. You can experience data duplication if you collect data from multiple data sources. It creates unnecessary repetitions which might lead to chaos. Irrelevant data is any data that doesn’t help you tackle the problem you’re currently dealing with. For example, if you’re trying to identify the reasons behind a lower retention rate among your US customers, any data that is related to non-US customers is deemed irrelevant. 

Step 2: Fix structural errors 

Sort out any structural errors that might happen due to a lack of consistent naming standards, typos, or capitalization while collecting or transferring data. If you don’t address them early on, then you might end up with mislabeled categories, which will negatively impact your analysis. For instance, if both “error” and “mistake” appear in your data set, but they’re not treated as equal because of inconsistent naming it will lead to issues in your data analysis. Removing structural errors is crucial for maintaining high-quality data. 

Step 3: Filter out outliers 

At this stage, consider what the end goal of your data analysis will be and filter out any data that doesn’t fit the context. By doing so, you can significantly improve your data’s performance and its insightfulness. That said, consider retaining outliers for future reference – remember that outlying data doesn’t necessarily mean that it’s erroneous data. While it may not support the use case you’re currently handling, it might hold immense value for future analyses. 

Step 4: Handle missing data 

Another step in the data cleaning process is spotting and dealing with missing data. Otherwise, the missing data points can interfere with algorithms. There are a few ways to tackle this issue: 

  • remove entries with missing information, but risk information loss 
  • replace missing data points using information from other observations. Just be aware that assumptions might have a negative impact on data integrity. 
  • adjust how you use data to effectively navigate around the missing values.  

Step 5: Validate and enhance quality 

The last step is validating the correctness of your data cleaning. Your objective is to ensure the data’s legibility, logic, and coherence. Here are a few questions to ask yourself: 

  • Does the data comply with its respective field rules?  
  • Does it help provide an answer to the theory you’re tackling? Does it support or invalidate it? 
  • Does it reveal new insights or help you identify trends for any further theories? 

If you spot inconsistencies, work towards addressing any data quality issues before moving on. We discuss the three common data cleaning challenges next. 

Three main challenges to effective data cleaning to maintain high quality data 

Missing data 

One of the most common challenges that companies face while dealing with data is missing information. There can be many reasons behind it, for example, software failure, incomplete surveys, data collection mistakes, or data entry errors. However, irrespective of the motive, it’s vital to know what to do in case of missing data. Should you delete the incomplete records, substitute them, or maybe turn to interpolation to estimate the missing values? Whichever approach you go with, you need to make sure that you don’t put your data integrity at risk – different methods can have different results.    

Inconsistent data 

Data inconsistency is a very broad term – the conflict can lie in the data format, units, and their decimals, or even naming/labeling conventions. 

A good example is how differently dates are recorded across the globe. For instance, May 28th, 2023 might be stored both in the DD/MM/YYYY and MM/DD/YYYY format. If both of these conventions are allowed into the data set, you might struggle to derive all data records from the same day. 

To address the issue, data needs to be standardized. This entails fixing naming conventions, unit formats, and cleaning up any qualitative records like text. This can prove time-consuming, particularly if dealing with a large data set, with multiple co-existing data variables and values. 

Duplicated data 

Duplicates happen when the same data is stored in the data set as multiple records. Similarly to missing data, duplicates can arise from a number of reasons – incorrect data set merging, a disruption to the system, or – simply – human mistakes.  

Retaining data duplicates can compromise your analyses’ accuracy and introduce bias. That said, deduplicating records must also be carried out with consideration. Spotting the difference between genuine duplicates and similar but distinct data requires an expert approach. Removing ‘false’ duplicates could backfire and harm the data set’s accuracy.  

All three of the above data type challenges illustrate why introducing and maintaining data cleaning procedures is of the essence. 

Data cleaning tools you can use 

There are many data cleaning tools, which can help you spot incomplete, missing, or broken data. Here are a few that you can consider using:  

OpenRefine (formerly Google Refine) 

OpenRefine, formerly known as Google Refine, is a powerful open-source tool designed to deal with messy data. It helps with: 

  • finding errors and data inconsistencies 
  • standardizing data and automatically clustering similar text values together  
  • data restructuration  
  • changing data formats.   

It’s free to use.  

Alteryx Designer (formerly Trifacta Wrangler) 

Alteryx Designer is an advanced, but user-friendly data analytics and preparation tool. It allows you to clean and analyze data from a variety of sources. Alteryx offers intelligent suggestions for cleaning and transforming your data. It’s a no-code solution, so you can confidently use it even if you lack technical skills. They offer a free trial.  

Winpure Clean & Match 

Winpure Clean & Match, as the name indicates, is a data cleaning and matching software that helps companies improve the accuracy and reliability of their data sets. Some of its key features include:  

  • identifying data quality issues, such as typos, inconsistent formatting, and missing values 
  • data deduplication 
  • data matching 
  • data enrichment, adding extra information from external sources 
  • and others.  

It connects to all popular files, CRMs, and databases. They offer a free trial.  

Now that you know how to clean data, let’s discuss how you should process it. 

Data processing in the data journey 

Data that has been cleared of obsolete, inconsistent, duplicated, or incomplete data can be greenlighted for processing. It entails uploading the data sets into a data warehouse, where the records are turned into a format that it can understand. 

Here’s where incorporating DataOps practices helps implement automation and continuous integration. At this stage of the data journey, DataOps ensures that data can be transformed into a standardized format, without sacrificing data quality or completeness along the way. 

While data processing can vary across organizations depending on the source of the data (whether it comes from a database, IoT devices, a data lake, or others), it is a cyclical process. Data can be collected ongoingly, resulting in numerous instances of data preparation, processing, and storage. Data continuously moves from the initial collection to storage and interpretation, ultimately powering a company’s business decisions. 

Three main data processing challenges  

Scalability & performance 

The more data you collect, the more challenging it becomes to process it quickly and without errors. Data sets that include huge data volumes can put a heavy load on computational resources. Not only can it lead to downturns, but also cause hindrances in data processing.  

You have to ensure that the tool stack you use is able to handle increasing data volumes. It’s key to use infrastructure that can both scale up and down to support your business needs. This includes using distributed computing frameworks, enhancing algorithms, and embracing storage solutions that are efficient.  

Maintaining data security and privacy 

In terms of security, data processing is a potentially vulnerable stage of the data journey. Since data is entered into a new environment, i.e. the data warehouse or database, it’s critical to ensure sound personal and sensitive data protection. Keeping it secure across all the stages of the processing cycle calls for using the most advanced security means. That’s why it’s worth entrusting the process to data governance experts, who will guarantee not only data accuracy and semantic consistency but also the security of your data assets. 

Data integrity  

Maintaining data integrity is another data processing challenge. It can be addressed by proper data hygiene, i.e., making sure that your data is clean before you start processing it. As mentioned earlier, raw data can include errors, be incomplete, or be in the wrong format. All of which can have a negative impact on your analysis. That’s why it’s so crucial to put the right data cleaning and validation processes in place. They help in maintaining high data quality and reliability.  

Addressing these challenges requires a combination of technical expertise, strategic planning, and the usage of effective tools. We discuss some of the latter next. 

Tools that enable effective data processing  

Here are some of the more advanced tools employed in data processing. 

Apache Spark 

Apache Spark is an open-sourced framework designed for large-scale data processing projects. It’s the most widely-utilized scalable computing engine, used by 80% of Fortune 500 companies. While it’s designed to operate on big data workloads, it can work well for data processing projects of any size. The tool works with a number of programming languages, such as R, Java, Scala, and Python. It comes with numerous APIs and libraries, which facilitate work on data manipulation, machine learning, and graph processing projects, among others. 

Spark also enables teams to use both structured and unstructured data, such as images or JSON files.  

Hadoop MapReduce 

Also part of the Apache suite of tools, Hadoop MapReduce is a framework that lets you write applications and process large, even multi-terabyte data sets on large, distributed data clusters. You can process data in parallel by breaking it down into smaller tasks, i.e., into ‘map’ and ‘reduce’ phases. While extremely powerful in its processing capabilities, Hadoop MapReduce can be more challenging to configure than other tools, including Apache Spark. 

SQL (Structured Query Language) Databases 

Some of the more known SQL databases include Microsoft SQL, PostgreSQL, and MySQL. These databases are commonly used for analytical and transactional processing. Among others, SQL databases are used to join, filter, transform, and retrieve data.  

While both Hadoop MapReduce and Apache Spark are more suitable for large-scale data processing, SQL databases are confined to structured data processing projects. 

Data cleaning and processing are key to data integrity 

Both data cleaning and processing are key steps in the data journey and are essential in maintaining data integrity. The success lies in the right setup of internal practices and tools, which can be challenging. Sometimes the best way to ensure that you undergo both processes properly is by working with a team of experts.  

At C&F, we’ll help you unlock the full potential of your data and ensure that it supports your company’s data-driven decision-making. We come with a unique combination of technology and business expertise, and years of experience supporting companies across multiple sectors. Let’s discuss how we can support you in your data transformation.