Top 10 Data Cleaning Techniques For Better Results
Data cleaning techniques are essential to getting accurate results when you analyze data for various purposes, such as customer experience insights, brand monitoring, market research, or measuring employee satisfaction. In this article, you will see how ten smart ways of cleaning your data for analysis can give you optimal results.
What Are The Top 10 Data Cleaning Techniques?
There are several data cleaning techniques that can be used to ensure that the data being analyzed is impeccably prepped for mining. The combination of all these techniques will give you the best results. Here are the top ten.
1. Clear formatting
The first thing you do with your data is clear the formatting. The data you gather may be from different sources, and each source would have different formatting. This can cause issues such as spacing or incomplete sentences, etc. during data processing as these differences will affect how the algorithms identify and analyze the data. To ensure that all the data has uniform formatting, you must use clear all formatting in your .csv or google files.
2. Remove irrelevant data
It is important to remove all irrelevant data from your file, especially those that you know will have zero effect on your customer feedback back analysis or any other business purpose. Irrelevant data can be anything - from hyperlinks in the text, to tracking numbers, pin codes, HTML tags, spaces between words, and such. Removing HTML tags from your data can also save you precious credits when using a sentiment analysis or text analytics API because HTML tags eat up a lot of space and at the same time are the easiest to get rid of.
3. Remove duplicates
Another important data cleaning method is removing any redundant data you can find. Redundant data can be because of corrupted data in the source itself or due to mistakes made by the person entering the data. Either way, all duplicate data needs to be removed so that the algorithms do not process the same data twice or more times as this will skew the insights.
4. Filter missing values
A critical thing to check while prepping your data is checking for missing values. You can rectify this issue in two ways, you can either delete the observations that have missing values or you can fill in the missing values. This will depend ofcourse on whether you know what the missing values are and how much you think reducing the data will affect the quality of your insights.
There are some of the opinion that large amounts of data are not necessarily important for insights but if you are looking for market insights through consumer research, the size of the sample data does matter.
5. Delete outliers
An outlier is a data item that appears to be random in comparison to other items in the data. Outliers can affect the results of data analysis and therefore, are better eliminated in some cases. However, even though most outliers reflect the variability of the measurement, they may not be a mistake and are sometimes important for insights. That’s why it is important to first check what kind of outlier it is before deleting it.
6. Convert data type
Converting data types is another data cleaning technique that needs to be used to prep data for analysis. All text data needs to be categorized as such, as all numerical values must be categorized with numbers. If this is not done, data mining algorithms will be unable to calculate numerical values for statistical analysis or analyze text through natural language processing (NLP) correctly.
7. Standardize capitalization
Another important thing to remember is that one must have standardized capitalization for all the text. Although this may seem contradictory, especially in certain cases such as social media sentiment analysis, where comments reflect personal style and may contain names of celebrities, businesses, and such.
However, this is not a problem in an ML model. Named Entity Recognition (NER) tasks can identify and analyze millions of entities that appear in data regardless of their capitalization. They can differentiate between terms, for example, the word “violet” and know which “violet” is a person and which “violet” is a color based on context.
8. Structural consistency
Make sure your data has structural consistency. This will greatly affect the accuracy of your insights in a positive way. For example, you must change all items that are related to the same thing to form one consistent fashion. For example, terms like “Not Applicable” and “N/A”, which mean the same thing must be written in a consistent manner. Similarly, “Price to Earnings Ratio”or “P/E” must be the same.
9. Uniform language
Data cleaning also means that there should be consistency in language as well, even if you have collected data from various places. Most text analytics tools use translations for this purpose. But this is not the best way to get optimal results as translations can dilute the meaning and context of the text.
Repustate text analytics API, however, has native speech taggers for each of the 23 languages that it processes, which means that each language is analyzed natively, without translations. This gives you better results.
10. Validate the data
If you think that you have cleaned your data optimally and it is ready for data mining, it serves well to validate your data and do a final quality check. When you put the data through the data processing pipeline, you will be able to see trends and other insights. You need to validate if the results appear logical, whether they have outliers or not, or are in line with the expectations you had. If in doubt, it is good to go back and check the data again for any irregularities you may have missed.
What Is The Importance Of Data Cleaning?
Incorrect data cleaning can have a detrimental effect on your insights. This is regardless of the type of data you are using, which includes text, audio, or video. The quality of data is critical to data mining. Data quality is not only equated to the data source but also to how properly it has been cleaned and prepared for data analysis. Incorrect insights for your marketing, employee analytics, or any other business needs can lead to the risk of faulty strategies that can cost a fortune to rectify.
That’s why it is important to make sure that the data is rigorously cleaned so that there is no redundant, incorrectly formatted, incomplete, corrupted, incorrect or outlier item in your data. It is only then that you will have accurate results, whether you are looking for TikTok insights or for news sentiment analysis, or data mining for any other purpose.
How Do You Select The Best Data Analytics Company?
There are certain parameters to keep in mind while selecting the best data analytics company for your requirements. Their data analysis solution must have the following critical features.
1. Precise entity extraction
A data analysis tool must have a very robust named entity extraction capability. This will allow it to identify, isolate, and extract entities from your data so that they are later measured for the sentiment and in correlation to the data.
2. Granularity of insights
Apart from data cleaning, the other most important element in data analysis is the level of granularity that you can get from the tool you are using. Having an overall sentiment score does not give actionable insights the way aspect-based sentiment scores do. The tool must be able to calculate a sentiment score as in-depth as possible to give you any real advantage.
3. Semantic analysis
Semantic analysis allows the data mining software to recognize the meanings and contexts of key words and phrases. This helps the system to reduce redundancies in analysis by categorizing words similar in meaning and treating them as the same.
Accuracy in insights is a result of many high-precision machine learning tasks such as NER, NLP, semantic clustering, and others. When NLP algorithms can differentiate between incorrect formatting and non-text items such as hashtags, emojis, abbreviations, etc, and use text analysis to understand them, it increases the accuracy of insights. For example, this is particularly important in user-generated data such as social listening on Instagram or any other social media platform where there are a lot of emojis or hashtags.
5. Speed & Scale
The speed and scalability of the model you use is a very important consideration when it comes to choosing a data analytics platform. The tool must be able to analyze thousands of data points in minutes and give you precise results. At the same time, it must be scalable so that you don’t have to keep upgrading as your market grows.
6. Industry Aspect Models
The fact that you should use an industry-specific model for your data analysis is as important as the data cleaning techniques you use to prep your data. Using a text analysis model that is based on your business and industry, helps in categorizing aspects and topics found in your data, which could otherwise be ignored.
For example, aspects like “deposit” and “teller” apply to a bank but will not be found in a restaurant. An industry-specific aspect model extracts these items and analyzes them in context.
7. Multilingual analysis
Multilingual text analysis is important if you are an international brand or if you are located in cosmopolitan cities that are multicultural. The data analytics tool you use must have native natural language processing capabilities so that you get accurate insights devoid of translations.
8. Social Media
Your tool must be able to give you insights from social listening. This is important for many marketing functions such as conducting brand monitoring, product research, or finding TikTok influencers.
9. Video content analysis
The data analysis tool must be able to effortlessly analyze social video content as it does text. Speech-to-text software transcribes data, which enables non-text data like podcasts, social media videos, business videos, radio broadcasts, etc to be analyzed for sentiment insights using NLP.
10. Insights visualization
Apart from data cleaning and processing, the data analysis tool must also have a visualization dashboard to show you the insights. These insights may be in the form of charts, graphs, word clouds, sentiment trends, sentiment scores, and more.
Data cleaning is a vigorous process that is extremely important in order to get the most precise results from data analysis. Once you have mastered the art of deciding which outliers to keep, which incomplete date entries to fill or delete, how to maintain structural consistency in your data, and other such tasks, you can be assured that your data analytics results will be of the highest quality.