5 types of dirty data and 5 AI tools to clean it


by sirisha


April 24, 2022

When you look at the data in its polluted form, it can only leave you in a quagmire of confusion and disillusionment.

Data is only about facts; but corrupted, they no longer remain facts. Dirty data is about exactly that fact. Data is provided in volumes and in many forms. When you start looking at data in its polluted form – not to mention the various biases it must endure – it can only leave you in a quagmire of confusion and disillusionment. And there is not even a tiny bit of exaggeration in this statement. According to a report by Experian, “On average, organizations in the United States believe that 32% of their data is inaccurate, an increase of 28% from last year’s figure of 25%.” Unless you have a clear understanding of data cleansing tools and their applications, the carefully written data-driven strategy will never be useful. Here are the top 5 types of dirty data and data cleaning tools to make data usable in its proper format.

1. Duplicate Data:

Duplicate data is something like having a genetically similar twin that only exists to talk trash. This affects most in different ways including data migration, via data exchanges, data integrations and 3rd part connectors, manual entry and batch imports. This leads to bloated storage counts, inefficient workflows, and data retrieval. Biased metrics and analytics, poor software adoption due to data inaccessibility, diminished ROI of CRM and marketing automation systems.

2. Obsolete data:

People who use GPS pretty much understand what it means to have outdated data. Driving cars through buildings by tracking GPS data is not an experience anyone wants to have. Some data reports fall into this category; visibly promising but largely outdated. It’s almost like having no data at all or much worse. It all depends on how quickly you can identify and eliminate it. Whether it’s individuals changing roles and businesses, rebranding businesses, or improvising systems over time, old data should never be used to gain insight into current situations.

3. Unsecured Data:

With governments rigorously enforcing data privacy laws and offering financial incentives for compliance, businesses quickly become vulnerable to unsecured data. Consumer-centric mechanisms to ensure digital privacy, such as digital consent, opt-ins and privacy notifications, have played an unprecedented role in the process of using data for commercial or social purposes. GDPR in the EU, California’s Consumer Privacy Act (CCPA), and Maine’s Act to Protect the Privacy of Online Consumer Information are a few. For example, when an individual prefers to opt out of a company’s consumer database, failure to comply with the consumer data privacy policies of a portion of the companies makes them liable to legal action. Usually this happens because companies accumulate a lot of data, and that too is disorganized. Complying with data privacy protection laws is easy with the practice of having a clean database.

4. Inconsistent Data:

Similar data stored in different places gives rise to inconsistencies, also known as data redundancy. Unsynchronized data, for example, similar data with different names stored across locations results in inconsistency. A variable that stores data for all CEOs, it takes different names such as CEO, CEO, CEO, etc., would create a gap in data formatting and make segmentation difficult. Having data cleansing best practices in place can help circumvent the problem to a great extent. Businesses need to create a clear blueprint of what an ideal database should look like with appropriate KPIs in place.

5. Incomplete data:

Incomplete data lacks key fields necessary for data processing. For example, if mobile user data is analyzed to promote a sports app, missing the gender variable will have a huge impact on the marketing campaign. The more data points there are on a record, the more information can be obtained. Data processes such as lead routing, scoring, and segmentation depend on a set of key fields for operation. There is no single solution to this anomaly. Either manual cross-checking with data to find missing fields, which in many cases proves unrealistic, or automation of the process is necessary to ensure target and customer profiles are complete.

Data cleaning tools:
1. Open Refine:

By using open refining, you can not only clean up errors, but also inspect data, modify data, and record its history. With this tool, you don’t have to test the functionality of any particular operation and it works across a range of operations. This works for public databases that are provided in a particular form for the public to have access to that form. It also facilitates the support of reconciliation Webservices. This was the analysis part of the dataset. You can also link your dataset to the web in just a few steps. OpenRefine also facilitates support for many reconciliation Webservices.

2. Winpure Clean & Match:

With an intuitive user interface, it can filter, match and deduplicate data, and can be installed locally, without worrying about data security. Security functionality is its main feature, which is why it is used to process data from CRM and mailing lists. The uniqueness of Winpure lies in its applicability on a wide range of databases, including spreadsheets, CSV, SQL servers to Salesforce and Oracle. This cleanup tool comes with useful features like fuzzy matching and rule-based programming.

3. TIBCO Clarity:

TIBCO Clarity is a self-service data cleansing tool available as a cloud service or desktop application. It can clean data for various purposes. For example, cleaning customer data in Spotfire, preparing data for consolidation into a master data management solution, TIBCO Clarity can do it all. It has several applications such as data validation, deduplication, normalization, transformation and data visualization to support data cleansing on different platforms such as cloud, Spotfire, Jaspersoft, ActiveSpaces, MDM , Marketo and Salesforce.

4. Parabola:

It is a no-code data pipeline tool that brings data from external data sources into your data workflow. Using this tool, you can create a node in a sequence and clean up your data. User functions are good enough to work as a paste tool to transfer data from place to place. However, it is difficult to get the right data, cleaned and calculated when you need it. The upside of this tool is the scalability and visibility it provides to employees.

5. Data scale:

A data cleansing tool that connects data from disparate sources such as Excel, TXT files, etc., effectively identifies errors and removes them to consolidate them into a single transparent data set. It is known for data deduplication by checking with different statistical agencies, especially to correct sensitive data in the fields of health and finance, thus detecting fraud and crime. Considered as an accurate cleaning tool, it is quite user-friendly and overall can be considered as a comprehensive data cleaning tool.

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