In the data collection process of Big Data, erroneous, duplicate or inaccurate data can slip in. This is dirty data. We delve into it and how to solve it with data cleaning.
Dirty data is a set of erroneous data that is part of Big Data. It sneaks in during the data collection process and hinders the processing task. In order for the conclusions drawn to be true, it’s important to carry out an exhaustive data cleaning process, in which all unreliable information is discarded.
Explanation of dirty data
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It’s a data with such large size and complexity that none of traditional data management tools can store it or process it efficiently. The main idea is simple: to convert all this data into quality information to support a company’s decision making.
To guarantee the quality of this information, the analysis and processing must be correct, but the raw material must also be of high quality. In this case, the raw material is the data, which must be truthful, correct and reliable.
That’s why, after the compilation, it’s essential to eliminate the junk, the data that are not real, the lies, the duplicities, the outdated data, the misprints, the inaccuracies or imprecision, to clean up and ensure that we are working with quality raw material. All that needs to be deleted is dirty data.
How dirty data arises
Dirty data can be the result of intentional falsification, but it can also be the result of carelessness or a lie on the part of the user. Imagine you have a landing page as part of a company’s digital campaign and it includes a contact form with basic data, for example, name, age, e-mail and phone number.
With those three fields alone, multiple problems can arise, for example:
- A typo when typing in the phone number.
- A false e-mail, on purpose, as a way for the user to avoid the commercial information that the company may send later.
- A form that, due to a person’s lack of attention, is filled in twice with the same information.
- A lie when telling one’s age.
Errors in forms have an impact on all company strategies. If the cleaning of dirty data is not correct, decisions will be made based on information that is not real, ergo, they will be wrong and, basically, none of this will make any sense.
Examples of bad strategies based on dirty data
As we explained before, the fundamental use of Big Data is to improve a company’s decision making. However, if the data is false or erroneous, the information derived from its processing will also be false or erroneous. Investment in infrastructure and technology will be of no use.
For example, a company can use its information to improve its marketing campaigns. If the definition of that audience is based on data from people who have lied about their age, neither the channels nor the messages of the marketing strategy will be appropriate.
This affects not only the way you impact that audience. It also affects the knowledge of what their specific needs are. For example, if a company wants to better adapt its products or services to its target audience, one of the keys is to know what age segment they belong to. If this information is wrong, the efforts made will be in vain or the potential results will not be exploited.
Dirty data and data cleaning
With all this in mind, business awareness of the importance of maintaining accurate and up-to-date databases is growing. In this context, data cleaning, a set of tools and solutions that allow automated cleaning of dirty data, has emerged.
The process consists of verifying a massive amount of data. The idea is to perform an analytical analysis to search for duplicates, typos, errors, etc. that can be corrected automatically. This process involves technologies within Artificial Intelligence, including Machine Learning.
In addition, there are ways to reduce the probability of collecting erroneous data, from the most basic, such as simplifying forms, to resorting to test questions, identity verification systems and other developments that slow down data extraction a little but, at the same time, increase its reliability.
Benefits of data cleaning
The cleaning of dirty data through data cleaning brings benefits both for the companies that update their databases and for the potential clients of those companies. Thus, the main advantages are:
- From the company’s point of view: a better knowledge of the market and target audiences allows developing more accurate sales strategies, with products, services, messages and channels that better reach the target and, therefore, are more likely to convert.
- From the user’s point of view: if the company focuses its campaigns, products and services on the customer, it will better meet their needs, provide a better response to their problems and the customer service and experience will be much more satisfactory for them.