When you look at your data that is fully polluted, it leaves you in a swamp of confusion and disappointment. Data is a collection of those facts that you accumulate with efforts and smartness, but when it is corrupted, they no more remain facts.
You receive data in volumes and different fashions and you want to keep it safe, but when you look at it in its polluted form you certainly feel a concern of risk to your all-important information. According to a report from Experian, “On average, U.S. organizations believe 32 percent of their data is inaccurate, a 28 percent increase over last year’s figure of 25 percent.”
In this article, we will discuss five types of dirty data and then five AI tools for their cleaning. So, you can use your information in the right format.
1. Nonoperational Data
People who utilize GPS know what nonoperational data means. And interestingly, no one wants to have it. This type is perceptibly promising, yet extensively outdated. It means nothing more than having no facts or much worse. Ultimately, it depends on you how quickly you identify and remove it. Thus, you should never use nonoperational information to draw insights into current situations.
2. Matching Data
It is just like having a genetically identical twin who exists only to trash talk. It affects your information in different ways. Matching data includes files migration, through exchange facts, integration, and 3rd party connectors, manual entry, and batch imports. Moreover, it causes bloated storage count, ineffective workflows, and data retrieval. Twisted metrics and analytics, poor software agreement due to files unreachability, reduced ROI on CRM and marketing automation systems.
3. Unpredictable Data
Similar files in different places create inconsistency. You may call it data redundancy. A variable takes different names like C.e.o, CEO, C.E.O, etc., for storing information of all chief executives. This thing develops an inconsistency in the file formatting and makes breakdowns difficult. However, you can avoid the problem to a great extent if you take the best cleaning practices. This is the responsibility of companies to make a plan for creating an ideal database with proper KPIs.
4. Insecure Data
Companies are getting defenseless to insecure facts with privacy laws. Governments forcefully apply these laws and provide financial support for compliance.
It’s important to add that customer-centric mechanisms such as digital agreements, opt-ins, and privacy reports have taken an extraordinary role in the process of using information for marketable or social use. A few examples are GDPR in Europe and California’s Consumer Privacy Act (CCPA). When a company doesn’t adhere to privacy policies and practices, legal action becomes necessary. It can happen because companies hoard a lot of information which is also disorganized within their database. With the practice of having a clean database, abiding by privacy laws becomes easy.
5. Lacking Data
Some data lacks important fields such as gender. If this is being analyzed for marketing purposes, then missing out on the gender variable and the number of fact points will have a huge impact on the campaign. The more variables in a record, the more insights are possible. One solution is to manually cross-check with records to find missing fields, which is unrealistic in many cases. Alternatively, automating the process ensures that profiles of targets and customers are complete.
Now the question is how can you clean up your dirty data. For this purpose, there are also 5 types of cleaning AI tools that may help you get polluted-free data.
How these cleaning tools can improve the quality of your record. Let’s have a look at these AI tools.
1. Open Refine
Open Refine can both clean and analyze your data. It can find errors in the dataset which are both simple to correct and important to keep track of. It also provides a way to test operations on multiple datasets and frameworks, without having to make updates manually. Linking your dataset to the web is now a simple process.
2. Winpure Clean & Match
WinPure can clean up a wide range of databases, including Salesforce and Oracle. Its extensive features make it ideal for efficient data cleansing.
It can clean, match, and induplicate files. It can be set up locally with no worries about record security. That’s why it is used to process CRM and mailing list sensitive information. Winpure includes spreadsheets, CSVs, SQL servers to Salesforce, and Oracle. This cleaning tool comes with valuable features including fuzzy matching and rule-based programming.
3. TIBCO Clarity
TIBCO Clarity is a self-service cleaning tool that can clean data for a variety of purposes. For example, it can clean customer information in Spotfire and prepare the database for consolidating in a master database management solution. It has multiple applications like validation, deduplication, standardization, and transformation of information. It supports different platforms such as cloud, Spotfire, ActiveSpace, Jaspersoft, MDM, Salesforce, and Marketo.
4. Parabola
Parabola is a no-code, database channel tool that connects you to external information sources. You can use it to create nodes in a sequence, clean your data, and transfer it from one place to another. One of the best features of this tool is its scalability and visibility which make employees’ jobs much easier. The only drawback is that it can be difficult to get the right data cleaned and calculated when you need it.
5. Data Ladder
An AI tool that consolidates disparate data into a seamless dataset, it identifies errors and removes them with its deduplication of statistical figures. It can detect fraud in healthcare and finance, which makes it a comprehensive data cleansing tool.
Bottom Line
Thus, you need a full understanding of data cleansing tools and their applications. Otherwise, the carefully drafted data-driven strategies can’t help you.
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