Many businesses worry about the data that is published for public consumption, but few realise how damaging it can be to the internal business operations when it includes incorrect information.
Inaccurate, incomplete and duplicated data can affect the internal communication as well as the decision making within an organisation, which has direct effects on your businesses efficiency. In this article we are going to discuss dirty data and its effects on internal business operations, as well as how these problems can be resolved.
What Is Dirty Data?
Dirty data is data that has been incorrectly entered, or which has been corrupted or damaged in some way. Data can get dirty when it’s saved in the wrong format, or when it’s saved in a place where it will be corrupted by another file. Dirty data can also occur when information is copied from one program to another and then pasted into a new document, but not cleaned up before being pasted.
The term “dirty data” is usually used to describe inaccurate or incorrect information in databases, spreadsheets, and other applications. Dirty data can cause problems with the functioning of these programs and systems, so it’s important to make sure that any dirty data is identified and fixed before using it again!
Causes Of Dirty Data
Dirty data is caused by a combination of three main factors: incomplete or inaccurate data entry, outdated data, and missing data.
Incomplete or inaccurate data entry can occur when a person doesn’t fill out all the required fields on a form, or when they enter incorrect information. This can happen accidentally (for example, if there is a software malfunction) or it may be intentional (for example, if someone accidentally enters false information).
Outdated data refers to information that has changed since the last time it was entered into your database. Although this could happen naturally as new information becomes available and old information becomes obsolete, it’s more likely that outdated information happens because people forget to update their records.
Missing data refers to instances where there are gaps in your dataset—that is, instances where you have no information on a product’s description . Missing data can be caused by mistakes in your database structure (for example, having fields for names but not for addresses), which means that certain pieces of information just don’t exist for some records in your system.
The Effects Of Dirty Data On Internal Operations
Dirty data can have a profound impact on how your business functions. It can lead to poor decisions, waste money, and give your competitors the upper hand.
Here are some of the ways dirty data can affect your business:
-Data analysis: If you use dirty data for analytics purposes (for example, using it to determine trends), then you may not get accurate results. This could mean that you miss out on important opportunities or fail to identify problems early enough to fix them before they become too big to solve without significant time or money required from your company.
-Communication: Dirty data can have a huge impact on internal communication within an organisation. The most obvious effect is that dirty data can lead to miscommunication and misunderstandings, which can cause issues with the company’s day-to-day operations. One example being an employee using information to enquire to a colleague , but the information he is using is incorrect resulting in delays and frustration between staff which results in loss of efficiency.
-Decision making: The first kind of decision-making that can be affected by dirty data is strategic planning. If you’re trying to make decisions about the future of your company, but you have inaccurate data on which to base those decisions, then you might end up making bad choices and missing out on opportunities. Another kind of decision-making that can be affected by dirty data is operational management. If you don’t have accurate information about what’s happening in your organisation at any given moment, then you might be unable to make smart decisions about how to deal with problems as they arise—or even what kinds of problems need to be dealt with at all!
-Inventory management: Dirty data has a huge impact on inventory management. Inaccurate or incorrect information is usually the culprit and this can make it difficult for managers to make informed decisions about their inventory. They may not know if they have enough of a certain product, or if they should stock up on a particular item before it sells out. This can lead to wasted time and money, as well as lost profits. Another problem with dirty data is that it can cause problems when reporting to higher management. If you are selling items that are not actually in stock, then your brand may be negatively affected.
There are two solutions to resolving dirty data; one is preventing it through implementing a data maintenance strategy. Two, is if you already have dirty data, use a data cleansing tool such as AICA.
Data maintenance is a process of keeping your data clean and organised. You can do this by removing unnecessary information, or by adding missing or incomplete information.
There are five steps to data maintenance:
1. Identify the need for maintenance
2. Set up a schedule for maintenance
3. Conduct the maintenance
4. Review the results of your maintenance activities
5. Evaluate whether to make any changes based on the review
Data cleansing is a process that ensures cleaning and therefore makes all your data accurate. It’s a way to remove duplicate entries, remove incorrect or incomplete information, and ensure that all data is formatted correctly. The result is a clean dataset that can then be confidently used for statistical analysis and predictive modelling.
How Does Data Cleansing Work?
There are many ways to cleanse your data, but they all have one thing in common: they’re automated processes. This means that machine learning algorithms are used to identify the errors and inconsistencies in your dataset.
The process starts by running the data through a series of tests—these might include things like identifying duplicate records or checking for missing information—and then marking those records as either “clean” or “dirty.” Afterward, the dirty records are sent back through another round of tests until every record has been marked as either clean or dirty. Once this has happened, all of the clean records are combined into one file while all of the dirty ones are grouped together into another separate file that you can use for business intelligence purposes (like reporting).