What Makes Data Cleaning so Essential?

Data Science, a field about which every geek, businessman, entrepreneur, programmer, and visionaries are talking about. When you will go to Google and search for data science conferences, you will literally get hundreds of listings with thousands of attendees all over the world. It has become the job of the decade with numerous people showing interest towards it, either because of high salary packages or its usefulness. Even with such positive effects, it also hides a darker side that most be unaware of. Despite all this buzz, it is important to know every aspect of data science before making any major decisions. One such essential aspect is data cleaning which is one of the most important parts of the data science work as a whole. Often it takes the majority of the time than compared to any other process involved in data science.

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For a more clear picture, data science is basically a computational field which involves the usage of various tools or programs for collecting or extracting, visualizing, analyzing, managing and storing the data. It helps you get insights from the data so that individuals can make an efficient, powerful, precise and effective data-driven decision for a positive output. It has the potential to change almost all the sectors including banking, healthcare, real estate, stock market and others to the entirety.

Data Science, in very simple terms, is the usage of computer science, math, and statistics for analyzing both structured and unstructured data. So this was a very brief gist about data science. Now coming back to data cleaning, it is one of the most time-consuming but important parts of the overall data science process. Often data scientists spend around 60% of the time in just data cleaning because without this, you will get an ugly set of data that might affect your analysis and results. On a regular basis, data cleaning techniques allow data scientists or statisticians to give a spotless analysis despite irregular or dirty data. Considering all these, let us take a look into some of the questions regarding data cleaning.

What is Data Cleaning?

Data Cleaning is also known as data scrubbing and it is a procedure that involves the identification and removal or correcting of inaccurate data from a given dataset or table or database.

Data cleansing is critical in data entry and data management because it improves data quality and, as a result, overall productivity. Today it is an essential step for any data entry outsourced tasks because when you clean your data, you remove all outdated or incorrect information, leaving you with only the highest quality data.

The best data cleaning techniques should recognize unfinished, unreliable, inaccurate or non-relevant data and then it should restore, remodel or remove the dirty or crude data. It can be done manually by checking several data or can be done by using various software or tools which automate the process. These techniques are generally performed as batch processing via scripting or interactively with all the tools for data cleansing.

In simple words, data cleansing or cleaning involves identifying and replacing incomplete, inaccurate, irrelevant or problematic data or records by using various tools or software for the correct analysis.

Once the dataset is cleaned, it should be uniform with other datasets that are related to it for any particular operation. The inconsistency of data that is identified and eliminated might be due to user entry mistakes or because of corruption during storage/transmission or by several data dictionary descriptions of similar values in different stores. Generally, the process of data cleaning is different than data validation.

The actual process of data cleaning includes the removal of typographical errors or validating and correcting values against a known list of entities. The validation can either be very strict or even fuzzy so some data cleaning solutions clean data by cross-checking it with validated data set.

Data Enhancement, another very common practice for data cleaning wherein, data is corrected or made more complete by adding related or relevant information. Furthermore, the process of data cleaning may also involve other activities like harmonization of data and standardization of the data. Here harmonization refers to converting shortcodes into actual words like “st to street” or “rd to road” and standardization means changing a data set to a new standard.

Why data cleaning is so essential?

To be honest, data cleaning is more of janitor work for all the big data scientists which allows them to get more accurate and effective results or insights. Without data cleaning, no neural networks, no image identification modules or no insights will be as efficient as we want them to be. Today, with the significant rise of data, these cleansing method has become more vital than ever before. Every industry be it banking, retail, hospitality, healthcare, education, and others are dependent on data and as this pool of data is getting bigger, the chances of error are also going to be bigger.

Some of the common examples of problems which may arise because of inaccurate data are as follows:

• Marketing: Imagine for any digital marketing agency or any company that wants to run advertisements with low quality of data which can result in wrong or incorrect user base with irrelevant offers or products. This can affect customer satisfaction in a negative manner with several folds resulting in fewer sales and chances of missing any good sales opportunity.
• Sales: What if any sales representatives approach to the wrong customers or fails to approach the previous customers because of not having the correct or accurate data.
• Compliance: Any online business might receive penalties from different government bodies for not meeting the rules regarding data privacy for their customers.
• Operations: Configuring robots or other automation machines for production which are based or trained with low-quality data can cause major setbacks for manufacturing companies. It can be a disaster and leads to a significant loss to the organization.

If people or data scientists initially used clean and accurate data, then all the above and numerous other situations can be easily avoided.

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What could be the benefits of data cleaning?

Good quality data can impact the decision in every positive manner which is possible. The better the data, the better would be insights and henceforth, the better would be the data-driven decisions. Currently, almost all modern businesses involve data for drawing insights or understanding their customers or for any other reason. On the same note, if these bodies will consider data cleaning an important part of data science then it would lead to a wide range of benefits. Some of the major benefits are:

• Streamlined practices for business: Contrary to the previous section, now imagine if you have a clean set of data without any duplicates, inconsistencies or errors in your records or dataset. How efficient it will make your key daily activities?
• Good productivity: It increases your productivity by allowing you to focus more on key work tasks instead of finding the correct data or wasting time for making corrections because of incorrect or inconsistent data. Access to clean good quality data with sufficient knowledge of data management can be a game-changer for any individual, data scientists or organizations.
• Faster sales cycle: Go to any marketing team of any organization, one thing which you will find common is that the majority of their decisions depend on their data. It could be related to their customers, demographics, products or any other. Any marketing team having the top quality data with accurate information will result in better sales and more leads for your ever tensed sales team which they can convert. It is also similar to B2C relationships.
• Effective decision making: This everyone knows it! Better the data you have, better insights you will get for making better decisions.

All of these and other benefits will generally lead to a profitable business and this will not only be the result of your sales team efforts but also because of efficient, powerful and more importantly clean data.

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What are the Tools for Data Cleaning?

Below are the list of 10 best tools for data cleansing that will help you in keeping your data clean and consistent so that you can analyze it visually and statistically for making data driven decision. Some of them are free whereas others can be priced with the option of free trial for you to get the hands on.

1. OpenRefine

It was formerly known as Google Refine and it is a very powerful tool for working with messy data. This tool comes handy for cleaning the data and then transforming it from one format to another. It can be easily extended with web services and external data. OpenRefine also allows you to explore big data sets without any difficulties, it can reconcile and match data, clean and transform it at a very rapid speed. This tool is a good option form you if you are someone who is looking for free and open-source data cleansing tools or software programs.

2. Drake

This tool is simple to use, is extensible and is a text-based data workflow tool that has the capabilities to organize command execution around data and its dependencies. The steps for data processing are defined along with their inputs and outputs, where Drake automatically resolves their dependencies and calculates 2 things i.e. which commands to be executed based on file timestamps and the order in which commands need to be executed and this is based on dependencies. Especially, this tool was designed for all the data workflow management and organizes command execution around the data.

It is very similar to GNU Make and has HDFS support and also allows multiple inputs and outputs. Moreover, Drake is also equipped with numerous other features that are designed to organize your data processing workflows which otherwise was very chaotic.

3. Trifacta Wrangler

It was created by the makers of Data Wrangler as an interactive tool that can clean data and transform it for better analysis. Among various other features, its capability of less formatting time and a larger focus on data analysis is what users love the most. It will assist you in cleaning and preparing messy and diverse data in a quick time with impeccable accuracy. Trifacta Wrangler also comes with machine learning algorithms which helps data analysts or scientists for preparing their data by predicting and suggesting all the common transformations and aggregations. Above all, this tool is free too!

4. Winpure Data Cleaning Tool

Winpure is the most popular tool for cleaning a large amount of data. This tool is also affordable and has the capability to remove duplicates, correct and standardize big data effortlessly and efficiently. It allows users to clean data from CRMs, spreadsheets, databases, and others. It can work with numerous databases like Dbase, SQL Server, Access and Txt files. Moreover, its advanced data cleansing and fuzzy matching, multi-language availability and superfast data scrubbing are some of the key features which every user uses likes about Wunpure.

5. TIBCO Clarity

This data cleansing tool offers on-demand software services from the web in the form of Software-as-a-service and also allow data scientist of statisticians to validate their data, in deduplication. It has cleansing addresses for identifying trends in no time for making smarter decisions. TIBCO Clarity can also standardize raw data which may be collected from disparate sources for providing good quality data for analysis with utmost accuracy.

6. Data Cleaner

It is a very strong data profiling engine which analyzes the quality of data so that users can make better and precise business decisions. Profiling is one of the most essential activity of any master data management, data governance program or data quality as, if you don’t know what you’re up to then you have poor chances of fixing it. For this situation, this tool is the perfect response.

Finding the missing values or patterns or character sets and other important characteristics in a data set for offering better results are its core features. This strong profiling engine uses fuzzy logic for detecting the duplicates in data and creates a single version of it. With this, you can also build your own cleansing rules and compose them into numerous scenarios to target databases.

7. Data Ladder

It offers products like Data Match and Data Match Enterprise. Data Match is an affordable data cleaning and data quality tool whereas, Data Match Enterprise includes algorithms for advanced fuzzy matching for up to 100 million records. In this whole industry, this tool has the highest matching accuracies and speed. Both the tools are super user-friendly and can help any businesses or industries despite their size for managing their processes of data cleaning with ease.

8. Cloudingo

This sales-force data cleaning tool is capable of eliminating duplicates and cleaning the records for maintaining the quality of data all in one place. It is again suitable for businesses of all sizes where data is updated in bulk and imported files are cleansed before accessing the sales-force. This tool has automation capabilities for ensuring that data is scanned on a regular basis for all the errors. Its simplicity, deletion of unnecessary and stale records, updating records in bulk and automate on a schedule are some of its features.

9. Reifier

Nube technologies are behind this data cleaning tool. Reifier uses Spark for distributed entity resolution, deduplication and record linkage. Its other features include high accuracy, fast deployment, runtime performance, and others. This tool also uses machine algorithms for providing the best entity resolution and matching of fuzzy data with a scale-out distributed architecture.

10. IBM Infosphere Quality Stage

This data cleaning tool is designed for supporting data quality and is one of the most popular cleaning tools or software solutions that supports full data quality. By using this tool, you can easily clean and manage database along with building consistent views of your most important units like customers, products, vendors, locations, and so much more, It is useful for quality data for big data, data warehousing, master data management, business intelligence, and others.

Conclusion

It’s true that today there is an abundance of data and if we can use it wisely, it can give some of the best insights to the professionals from any field. It is also true that with the increase of data, managing and drawing insights from that data are becoming a bigger task and here data science comes in the picture. Indeed, you might have heard the importance of data science from numerous blogs, articles, newspapers, YouTube videos and so on but you should also know about the other essential aspects of data science like data management, data cleaning, data analysis and so on.

In this article, I have tried to cover one such aspect which is less discussed but apparently is more important than any other aspect of data science. If anyone is thinking of skipping this step because of the excessive time consumption or any other reason than you should know that often you’ll run into problems such as inaccurate results and others. Thus, it becomes very important for you to know about data cleaning, its importance and the effective tools which you can use for doing so. With this, I will end the article and hope that you will find all the information useful which you can implement in data cleaning.