The global data cleansing tools market is set to experience a meteoric rise in the coming years, driven by the digitization of international business during the ongoing COVID-19 pandemic.
Also, the universal data cleaning tools market is fragmented, with various manufacturers operating in both developing and developed regions. Learn more about the growing importance of Data Cleaning (data cleansing) in analytics.
Data cleansing tools are needed to remove duplicate and inaccurate data from databases.
The pandemic has become a catalyst for the growing need for data-cleansing tools.
Since businesses globally are now forced to move online, whether in telecom, retail, banking, or even government departments, the need for such tools is felt even more.
What is Data Cleaning?
The data cleaning process can include statistical methods to remove incorrect, incorrectly formatted, or incomplete data from a dataset.
Such data leads to false conclusions, even causing the most sophisticated algorithm to fail. Data cleansing tools use sophisticated frameworks to maintain reliable enterprise data.
Solutions for data quality include master data management, data deduplication, customer contact data verification and correction, geocoding, data integration, and data management.
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Another outcome of data cleaning is the standardization of enterprise data.
When done correctly, it results in information that can be acted upon without any further course correction to another data system or person.
For all of this, your enterprise needs a data quality cleaning strategy that aligns with business goals.
How Do You Clean Data?
Like any such process, data cleaning requires both technique and appropriate tools.
Data cleaning techniques may vary depending on the types of data your enterprise has and the tools you deploy to handle them.
Here are the first steps to tackle poor data:
Inspect, clean, and verify. The first step is to inspect the incoming data to detect inconsistent data.
This is followed by data cleaning, which removes anomalies, and then by inspecting the results to verify correctness.
5 Steps in Data Cleaning
When integrating several data sources, data can be mislabeled or duplicated. When data is inaccurate, results and algorithms become unpredictable, even when they appear correct.
There is no single method for accurately describing the steps in removing irrelevant data or correcting errors, because the process varies across datasets.
However, it is essential to develop a template for the data cleansing process to ensure you do not make a single mistake each time.
The basic step is to identify data that needs cleaning and to remove duplicate observations.
Use your data cleaning strategy to identify the datasets that need to be cleaned. This is the primary responsibility of data stewards, individuals tasked with maintaining the flow and the quality of data.
Among the first steps here is to delete unwanted, irrelevant, and duplicate observations from your datasets.
Deduplication is first on the list because duplicate observations occur most often during data collection.
It's like nipping the problem in the bud. Duplicate data also flows in when you combine datasets from multiple sources, perhaps received via various channels.
Unwanted observations are datasets that may be correct but do not align with the specific problem you are trying to analyze.
So, if you are looking for patterns in young girls' online spending, any data that includes teenage boys is irrelevant.
Fix structural mistakes
Errors in the data structure include weird naming conventions, typos, and other inconsistencies. These can lead to mislabeled categories or classes.
Set data cleansing techniques
Which data cleansing techniques (data cleaning techniques) does your enterprise want to deploy?
For this, you need to discuss with various teams and develop enterprise-wide rules to transform incoming data into a clean state.
This planning includes steps like which part of the process to automate, and which not to.
Filter outliers and fix missing data.
Outliers are one-off observations that do not seem to fit within the data that's being analyzed. Improper data entry could be one reason for it.
While doing so, however, do remember that just because an outlier exists, it doesn't mean it is not true.
Outliers may or may not be false, but they may prove to be irrelevant to your analysis, so consider removing them.
Missing data is another aspect you need to factor in. You may either drop the observations that have missing values, or you may input the missing value based on other observations.
Dropping a value may result in losing information, while adding a presumptive input risks compromising data integrity, so be careful with both tactics.
Implement processes
Once the above is settled, proceed to the next step: implementing the new data cleansing process.
The questions here that need to be asked and answered are:
a. Does your data make complete sense now?
b. Does the data follow the relevant rules for its category or class?
c. Does it prove/disprove your working theory?
Eventually, you need to be confident about your testing methodology and processes, which will be evident in the results.
If adjustments have to be made to the procedure, they must be made, and then the entire process must be "fixed" in place.
Your data stewards or data governance team must periodically re-evaluate the data cleansing processes and techniques, especially when you add new data systems or acquire new businesses.
Call it data cleaning, data munging, or data wrangling, the aim is to transform raw data into a format consistent with your database and use case.
Use Cases of Data Cleaning
Our senior data scientist, Vinay Dabhade, explains the many use cases for data cleaning in detail.
1. ETL developers typically face issues with addressing standardization. For example, the state of California might show up as 'CA', 'California, or' California state'.
2. Dates and timestamps require a lot of cleaning and transformation, as multiple systems with multiple regions can mess up a company's data warehouse.
For example, if you are extracting data from an ERP and storing it in AWS Redshift DW, you will likely have to handle date and timestamp formats.
3. Case-sensitive data, lead and lag spaces in fields, and invalid or bad UTF-8 characters are typical use cases an ETL team looks for when doing data cleaning.
4. Data science teams and analysts also spend a considerable amount of time cleaning data for their use cases.
5. Data scientists have to deal with missing or incomplete data. To prepare the dataset, records are typically filtered out or data imputed to obtain a clean dataset with minimal noise or outliers.
For example, you are building a product inventory demand prediction model using the last 2 years of historical data. Still, the inventory system was changed last year, and you are now receiving different fields or values than in the previous system.
Here, the data scientist must perform data wrangling to create a clean, uniform dataset for building a robust predictive model.
Another example I have observed is that when calculating lifetime value for customers, you come across customers who have generated 10-15 times more revenue than your average customer.
In this case, the data science team must analyze whether this is an outlier and whether it will affect the analysis. If it is found to be an outlier, it must be removed to maintain data quality.
Why is Data Cleaning Required in the First Place? What are the Benefits?
Data cleaning's importance and benefits: The answer, in short, is to obtain a template for handling your enterprise's data.
Not many people realize this: data cleaning is a crucial step in the data analytics chain. Because of its importance, it is often neglected.
The result: an erroneous analysis of your data, which wastes time, money, and other resources.
Having clean data can help in performing the analysis faster, saving precious time.
Data cleaning is required because incoming data is prone to duplication, mislabeling, missing values, and other issues.
The oft-quoted line, "Garbage in means garbage out," explains the importance of data cleansing succinctly.
Obviously, if the input data is unreliable, the output will be undependable as well.
Benefits of data cleaning include:
Deletion of errors in the database
Better reporting to understand where the errors are emanating from
The eventual increase in productivity is due to the supply of high-quality data in your decision-making
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Tools for Data Cleaning
No two data cleaning processes are the same; they differ from enterprise to enterprise, depending on the business goals.
Data cleaning techniques come with their own set of data cleaning tools, some manual, some automated.
These tools are used to manage, analyze, and scrub data from various channels, including email, social media, and website traffic.
Data cleansing tools remove issues such as formatting errors. They are used to support IT teams managing data, sometimes helping transform the data from one format to another.
Software like Tableau Prep, Tibco Clarity, Informatica, and Oracle provide visual and direct ways to combine and clean your data.
Using such data scrubbing tools can save data analysts considerable time and give them greater confidence in their data.
Tibco Clarity, for example, is an interactive data-cleansing platform that uses a visual interface to streamline data-quality improvements.
It also supports deduplication and address checks before moving the data down the line.
Informatica Cloud Data Quality tool: The Informatica Cloud Data Quality tool works on a self-service model.
It is also a tool for automation, empowering almost anyone in your business to fetch the high-quality data they need.
This tool allows you to leverage templated data quality rules for standardization. It helps with data discovery and transformation, among other tasks, and even automates the process with artificial intelligence.
The Oracle Enterprise Data Quality tool is a slightly more advanced option in the market, but it is among the most comprehensive data management tools available.
Its features include address verification, standardization, and profiling.
Data cleaning tools can be used to remove irrelevant data (values), delete duplicate values, avoid typos and similar mistakes, and take care of missing values.
All meaningless or useless data has to be removed from your database. Duplicates, on the other hand, increase the amount of data and must be deleted.
Typos are a result of human error and also need to be fixed. You need to ensure proper spelling and capitalization in your data entries.
After all, what is most important is that data types must be uniform across your dataset. This means numeric values must be numeric and not boolean.
Data quality tools also handle missing values.
What is the Importance of Data Cleaning in Analytics?
What is the importance of data cleaning in analytics? Data cleansing is the first crucial step for any business seeking to gain insights through data analytics.
Clean data enables data analysts and scientists to gain crucial insights before developing a new product or service.
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Data cleaning helps an enterprise address data entry mistakes by employees and systems that occasionally occur.
It helps adapt to market changes by making your information fit changing customer demands. What's more, data cleaning helps your enterprise migrate to newer systems and merge two or more data streams.
Conclusion
There's little doubt that data cleaning is a vital step for any data-centric business. It helps companies stay agile by helping them adapt to changing business scenarios.
A successful data cleaning strategy means your data cleaning choices must align with your data management plans. When all of this is done, data cleaning helps improve data quality within your enterprise data management system.
