5 Types of Dirty Data and 5 AI Tools to Clean Them


by sirisha
April 24, 2022

HAVE

When you look at data in its polluted form, it is bound to leave you in a quagmire of confusion and disillusion.

Data is all about facts; but when corrupted, they no more remain facts. Dirty data is exactly about this fact. Data comes in volumes and in many fashions. When you start looking at data in its polluted form—not to speak of the various biases it has to take the blow from—it is bound to leave you in a quagmire of confusion and disillusion. And there is not even a wee bit of exaggeration in this statement. According to a report from Experian, “On average, US organizations believe 32 percent of their data is inaccurate, a 28 percent increase over last year’s figure of 25 percent.” Unless you have a clear understanding of the data cleansing tools and their applications, the carefully drafted data-driven strategy will never come to help. Here are the top 5 types of dirty data and data cleaning tools to make data usable in its right format.

1. Duplicate Data:

Duplicate data is something like having a genetically similar twin who exists only to trash talk. It affects the most in different ways including data migration, through data exchanges, data integrations, and 3rd party connectors, manual entry, and batch imports. It causes inflated storage count, inefficient workflows, and data recovery. Skewed metrics and analytics, poor software adoption due to data inaccessibility, decreased ROI on CRM and marketing automation systems.

2. Outdated Data:

People who use GPS, pretty much understand what it means to have outdated data. Driving cars into buildings following GPS data is not an experience someone wants to have. Some data reports just fall into this category; visibly promising but substantially outdated. It’s almost like having no data at all or much worse. It all depends on how quickly you can identify it and do away with it. Be it the change of roles and companies by individuals, rebranded companies, or systems improvising over time, old data should never be used to draw insights into current situations.

3. Insecure data:

With Governments stringently applying data privacy laws and providing financial incentives for compliance, companies are quickly becoming vulnerable to insecure data. Consumer-centric mechanisms to ensure digital privacy such as digital consent, opt-ins, and privacy notifications have taken an unprecedented role in the process of putting data into some commercial or social use. 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 to name. For example, when an individual prefers to opt out of a company’s consumer database, not adhering to consumer data privacy policies on part of companies makes them liable for legal action. Usually, it happens because companies hoard a lot of data, and that too which is disorganized. Adhering to data privacy protection laws comes easy with the practice of having a clean database.

4. Inconsistent data:

Similar data stored in different places gives rise to inconsistency, which is also called data redundancy. Out of sync data, for example, similar data with different names stored across places gives rise to an inconsistency. A variable that stores data of all chief executives, it takes different names such as CEO, CEO, Ceo, etc, would create a discrepancy in the data formatting and makes segmentation difficult. Having the best data cleaning practices in place can help circumvent the problem to a great extent. Companies should create a clear schema of what an ideal database should be like with proper KPIs in place.

5. Incomplete data:

Incomplete data lack key fields required for data processing. For example, if the data of mobile users are being analyzed for promoting a sports application, missing out on the gender variable will have a huge impact on the marketing campaign. The more the number of data points on a record, the more insights are possible. Data processes like lead routing, scoring, and segmentation depend on a collection of key fields for operation. There is no one solution for this anomaly. Either a manual cross-checks with data to find missing fields, which in many cases proves unrealistic, or automating the process is required to ensure profiles of targets and customers are complete.

Data cleaning tools:
1. Open Refine:

Using open refine, you can not only clean the errors but also inspect the data, amend the data and save its history. With this tool, you do not have to test for the functionality of a particular operation and it works over an entire range of operations. It works for public databases which are provided in a particular form for the public to have access to that form. It also facilitates support for reconciliation Webservices. This was all about 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 a lot of reconciling Webservices.

2. Winpure Clean & Match:

With an intuitive user interface, it can filter, match and deduplicate data, and can be installed locally, not worrying about data security. The security feature is its chief characteristic, a reason why it is used to process CRM and mailing list data. Winpure’s uniqueness lies in its applicability over a wide range of databases including spreadsheets, CSVs, SQL servers to Salesforce, and Oracle. This cleaning tool comes with features such as fuzzy useful 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 a variety of purposes. For example, cleaning customer data in Spotfire, preparing data for consolidating in a master data management solution, TIBCO Clarity can do it all. It has multiple applications like data validation, deduplication, standardization, transforming and visualizing data to support data cleaning over different platforms like 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 your data. The user functions are quite good to work as a glue tool to transfer data from one place to the other. However, it is difficult to get the right data, cleaned and calculated when you need it. The silver lining with this tool lies in the scalability and the visibility it provides to the employees.

5. Data Ladder:

A data cleaning tool that connects data from disparate sources like Excel, TXT files, etc, efficiently identifies errors and removes them to consolidate into one seamless dataset. It is known for deduplication of data by checking with different statistical agencies, particularly for correcting sensitive data in healthcare and finance, thereby detecting fraud and crime. Touted as an accurate cleansing tool, it is pretty much user-friendly and all-in-all, can be counted as a comprehensive data cleansing tool.

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