Cleanliness is next to godliness, as the old saying goes, and this holds true for data and information as much as it does for human beings. As a business, you rely on your data to be correct, complete and up-to-date, so you can make the right decisions. Thus, it can be disastrous for you if that data is inaccurate.
However, given the vast quantities of data that flow in and out of the modern business, it's impossible to ask a human being, or even an entire team, to monitor your data and check for problems, gaps and inconsistencies. Only data cleaning tools can scour your database for these sorts of issues and automatically replace, modify or delete the flawed data.
This buyer's guide will explain what data cleaning tools are, explore their common features and point to some of the bigger issues your business should be concerned about when selecting the right data cleaning software for you.
Here's what we'll cover:
Success in business, and in business intelligence, relies on information—who has it, what they do with it and how good it is. Your business is only as strong as the quality of its data, so you should analyze your past and present successes in order to replicate them in the future, while simultaneously exploring what went wrong with your failures in order to avoid recreating them.
However, not all data is created equal. Generally, your data comes in the form of a record set, table or database, and each of those is equally likely to have a variety of incorrect, inconsistent or duplicate data points. This can be caused by a multitude of issues, including user entry and corruption of the file while in transmission or in storage. Whatever the reason it exists, though, that bad data needs to go.
That's where data cleaning tools come in. These software systems will scan through your information and find the data which stands out as being problematic. Depending on the system and your preferences, you can either have that data automatically scrubbed or replaced, or you can just have it flagged for manual review and updating.
Data cleaning can take a variety of forms:
Though they can sometimes be mistakenly used interchangeably, there's an important distinction between data cleaning and data validation:
|Data profiling||Scan through your data to find patterns, missing values, character sets and other important data value characteristics. Through creating this profile, the software will then know what sticks out as being incorrect or problematic, in comparison.|
|Data elimination||Mapped against the profile created by going through the data, as well as against a validated list of known entities, the software will rid your database of duplicate data, bad entries and incorrect information.|
|Data transformation||Working hand-in-hand with data elimination, this will take bad data and transform it into good data by correcting typos, standardizing/harmonizing data, converting values and normalizing numeric values to conform to minimum and maximum values.|
|Data standardization||Scan through your data and put it all into a common format that you've selected (for example, taking Imperial system measurements and standardizing them to the Metric System) so that large amounts of data can be more easily analyzed.|
|Data harmonization||Similar to data standardization, this will take data from a variety of sources and put them into a common format. This will allows both users and automated data analytics tools to be able to compare, review and analyze data that comes from more than one source.|
|Data enhancement||This is a feature of more robust data cleaning tools, which will allow the software to connect information across databases in order to add related information to the entries it is scanning (such as adding addresses to a list of names).|
Data quality dashboard on data analytics tool Halo
No matter the size or scale of your business, you're likely relying on some kind of database to keep track of your contacts, customers, inventory or other important pieces of information. In order to ensure that the database you're using is correct and up-to-date, you will find data cleaning tools useful.
However, not all businesses are alike, and neither are the data cleaning tools for those businesses. Appropriate tools will be based on the size and scale of the business. Your own business will fall into one of the following categories, based on its size:
Other factors to take into consideration when choosing the right data cleaning tools for your business include:
Access to other systems. In order for data cleaning tools to work, they need access to your data. This may be housed in a variety of places within your computer systems—in your business intelligence software, your customer relationship management software, your project management software or anywhere else that you house large amounts of important information—and thus requires the data cleaning tools to be compatible with the interface and formatting of those databases. Be sure to check with the vendor that the data cleaning tools you are purchasing will be able to access and clean all of your information across these various databases.
Cloud-based software vs. on-premise software. Only a few years ago, software was mostly housed on-premise, meaning that companies had to maintain the physical hardware for the products they purchased, necessitating both storage space and IT knowledge/resources. This made that software more difficult to use for smaller businesses. Today, however, most data cleaning tools can be purchased and employed using a cloud-based model, where the hardware is housed by the vendor and the software is simply deployed by accessing it over the internet. This makes those tools more readily available to small-to-midsize businesses without high-level IT resources, especially since cloud-based software is often quicker and easier to use than on-premise solutions, with fewer up-front costs.
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