Data is made up of facts; however, when they are corrupted, they cease to be facts. This is exactly what dirty data is about. Data comes in large quantities and a variety of formats. When you start looking at data in its polluted form, let alone the various biases it has to deal with, you’re bound to be left in a quagmire of confusion and disillusionment. And there isn’t a hint of exaggeration in this statement.
According to Experian, on average, U.S. organizations believe 32% of their data is inaccurate, a 28% increase over last year’s figure of 25%. The carefully crafted data-driven strategy will never be useful unless you have a thorough understanding of the data cleansing tools and their applications. Here are the top 5 types of dirty data and data cleaning tools for restoring data to its proper format.
- Duplicate Data:
Duplicate data is analogous to having a genetically identical twin who exists solely to trash talk. It has the greatest impact in a variety of ways, including data migration, data exchanges, data integrations, third-party connectors, manual entry, and batch imports. It leads to an increase in storage capacity, inefficient workflows, and data recovery. Metrics and analytics that are skewed, poor software adoption due to data inaccessibility, and lower ROI on CRM and marketing automation systems.
- Outdated Data:
People who use GPS are well aware of what it means to have outdated data. Driving cars into buildings based on GPS data is not something anyone wants to experience. Some data reports simply fall into this category; they appear to be promising but are significantly out of date. It’s almost as bad as having no data at all, if not worse. It all depends on how quickly you identify and eliminate it. Old data should never be used to draw insights into current situations, whether it is due to individuals changing roles and companies, rebranded companies, or systems improving over time.
- Insecure Data:
Companies are becoming increasingly vulnerable to insecure data as governments enforce strict data privacy laws and provide financial incentives for compliance. Consumer-centric mechanisms for ensuring digital privacy, like digital consent, opt-ins, and privacy notifications, have played a previously unseen role in the process of putting data to commercial or social use. To name a few, GDPR in the EU, California’s Consumer Privacy Act (CCPA), and Maine’s Act to Protect the Privacy of Online Consumer Information.
For instance, if an individual prefers to opt-out of a company’s consumer database, companies that do not adhere to consumer data privacy policies may face legal action. Typically, this occurs as a result of companies hoarding a large amount of disorganized data. Adhering to data privacy laws is made simple by the practice of maintaining a clean database.
- Inconsistent Data:
Inconsistency, also known as data redundancy, occurs when similar data is stored in multiple locations. Data that is out of sync, like similar data with different names stored in different locations, causes inconsistency. A variable that stores data for all chief executives and goes by different names such as CEO, C.E.O, C.e.o, and so on, would cause inconsistency in data formatting and make segmentation difficult. Having the best data cleaning practices in place can help to mitigate the problem significantly. Companies should develop a clear schema of what an ideal database should look like, complete with appropriate KPIs.
- Incomplete Data:
Incomplete data lacks the critical fields needed for data processing. For instance, if mobile user data is being analyzed to promote a sports application, leaving out the gender variable will have a significant impact on the marketing campaign. The more data points there are on a record, the more insights are possible. Data processes such as lead routing, scoring, and segmentation rely on a set of key fields to function. This anomaly does not have a single solution. Either a manual cross-checks with data to find missing fields is required, which in many cases proves unrealistic, or the process must be automated to ensure target and customer profiles are complete.
Data cleaning tools
- Open Refine:
You can use open refine to not only clean the errors, but also inspect the data, amend it, and save its history. This tool eliminates the need to test for the functionality of a specific operation and works across a wide range of operations. It works for public databases that are provided in a specific format for the public to access. It also makes it easier to support reconciliation Web services. This was all about the dataset’s analysis. In just a few steps, you can also connect your dataset to the internet. OpenRefine also makes it easier to support a wide range of reconciling Web services.
- Winpure Clean & Match:
It can filter, match, and deduplicate data using an intuitive user interface, and it can be installed locally without worrying about data security. Its main feature is security, which is why it is used to process CRM and mailing list data. Winpure’s distinguishing feature is its ability to work with a wide range of databases, including spreadsheets, CSVs, SQL servers, Salesforce, and Oracle. This cleaning tool includes features like fuzzy matching and rule-based programming.
- TIBCO Clarity:
TIBCO Clarity is a data cleansing self-service tool that is available as a cloud service or as a desktop application. It can clean data for a variety of purposes. For instance, TIBCO Clarity can clean customer data in Spotfire and prepare data for consolidation in a master data management solution. It has several applications that support data cleaning across various platforms such as cloud, Spotfire, Jaspersoft, ActiveSpaces, MDM, Marketo, and Salesforce, including data validation, deduplication, standardization, and transforming, and visualizing data.
- Parabola:
It is a no-code data pipeline tool for integrating data from external sources into your data workflow. You can use this tool to create a node in a sequence and clean your data. The user functions are adequate for use as a glue tool to transfer data from one location to another. However, getting the right data, cleaned and calculated, when you need it can be difficult. The benefit of using this tool is the scalability and visibility it provides to employees.
- Data Ladder:
A data cleaning tool that connects data from disparate sources such as Excel, TXT files, and so on and efficiently identifies and removes errors to consolidate into a single seamless dataset. It is well-known for data deduplication through cross-checking with various statistical agencies, particularly for correcting sensitive data in healthcare and finance and thus detecting fraud and crime. It is marketed as an accurate cleansing tool, is very user-friendly, and can be considered a comprehensive data cleansing tool.
