“Information is the oxygen of the modern age” - Ronald Raegan
It is hard to undervalue the importance of information. It is information that fosters progress, aids decision making, and helps generate even more new information. And, in terms of the business landscape, information is also one of the biggest assets a company can possess.
What makes data such a vital business asset? There are plenty of reasons for that. Data is what helps businesses:
Understand their target audience;
Build a more solid marketing strategy;
Improve various processes;
Find prospect customers;
Predict sales trends;
Boost sales conversion;
Increase client retention, etc.
Finally, it is data that helps companies make better decisions and find smart solutions to a variety of problems.
What kinds of data do businesses collect? Depending on several factors, such as goals and sphere of business, companies most often collect two main types of data:
Consumer info - this group includes all details that help businesses understand their buyers better, including contacts, location, browsing history, interests, activity on the website, etc.
Firmographic data - this group of data is mostly used by companies engaged in B2B selling. This includes all details about other organizations, such as location, industry, type, number of customers, and other information that can help generate the right B2B leads.
Sounds, pretty easy? However, not everything is that simple. While data is, without a doubt, a vital asset. In today’s world, it is no longer enough to collect basic data and leave it as it is - raw and incomplete. Today, businesses have to put way more effort and resources into organizing the data properly, and that’s when you need data cleaning.
When collecting information about existing customers, prospects, and performance, businesses rarely rely on a single source. As a rule, companies gather data from multiple resources or apply data enrichment strategies to create more complete and thorough data sets. However, when collecting information from multiple sources, the chances are that there will be some duplicated or incorrect data. This can lead to unreliable analysis and poor outcomes of further business processes that are conducted relying on the gathered information. That’s what you need data cleansing for.
In a nutshell, data cleaning or data cleansing is a process of transforming raw information into uniform data sets that are ready for further analysis. Simply put, it is a process of determining and modifying (or removing) pieces of information that are incomplete, incorrect, duplicated, or otherwise irrelevant. The key reason why data cleaning matters is because this process helps you prevent inaccurate outcomes of further analysis.
Although it may sound pretty straightforward, data science is a very deep and complicated science, so data cleansing is also a very complex and responsible process. It is not just about removing mistakes and duplicated information. It is about maximizing the quality of your data according to the following parameters:
These are the key five characteristics of high-quality data that can bring you real value and help attain short and long-term objectives easier.
Although this process is generally considered to be one of the foundational elements of the data science, it seems like many businesses are still undervaluing its importance. This is in vain. Here are some of the core pros that prove how much you can benefit from proper data cleaning:
Eliminating errors from your database allows you to get more accurate analysis results and, thus, reach your business goals more easily.
Using the right data cleaning and enrichment services will save your employees lots of time, allowing them to focus on other things that matter and making them more efficient. And, at the same time, you will not compromise the outcomes.
Fewer errors in datasets can make your employees less frustrated, meanwhile ensuring a higher level of customer satisfaction.
Unfortunately, there is no one-works-for-all strategy for data cleansing. The steps you will have to undertake will vary depending on numerous factors, such as the kind of data you gathered, resources, and so on. However, here, we’ve compiled some of the most widely used practices for effecting data cleaning:
Typically, this is the first step in data cleansing regardless of what you are going to do next. First of all, you need to inspect the gathered data to detect any errors. At this stage, it is vital to keep in mind the five basic characteristics of quality data that we mentioned earlier. Keeping all these nuances in mind, you should be able to identify any mistakes or inconsistencies present in your database.
Once you have scanned the data and determined the errors, the next step is usually the cleaning itself. At this point, you will need to remove all the discovered issues.
Remove Duplicate Data
When collecting information from multiple resources it is easy to get duplicate entries in your database, which may affect the reliability of the further analysis of information. That’s why it is important to identify and remove any duplicate data.
However, to save your time and avoid duplicate information, you can use special data cleansing tools. There are plenty of options available. Of course, good tools are most often paid. However, the time you save with the help of such tools is definitely worth investing in them.
When the process of cleaning is finished, the last touch is to validate the accuracy of your data after all fixes were applied. This step is required to ensure that there are no gaps left.
Standardize the Process
This step is not mandatory, but preferable. Once you have defined the most effective way for data cleaning, it is a good idea to standardize it within your team. By doing so, you will have an established process that will ensure that you get the right data all the time. Also, if there is a specific procedure, this will significantly save your team’s time on future data cleaning processes.
Apart from data cleansing, data enrichment is another essential process for reaching your business goals. In a nutshell, data enrichment is a foundation of data collection. This term refers to a process of merging third-party information gathered from trusted resources with the database you already have. This process allows companies to enhance the quality of their data and make it more complete.
While this process brings plenty of benefits to businesses, it is now made simple with the help of advanced data enrichment services. Today, companies have access to a variety of lead enrichment tools that make it quick and simple to get the right data. One such tool is our Data Enrich software that allows you to find emails, gain a better understanding of your audience, and enrich leads in a few simple clicks.
Regardless of the niche in which you operate or your goals, data enrichment is a smart way to get more valuable information that will bring you the needed results. Whether you are looking to collect information about your clients, prospects, or other businesses, data enrichment is a good way to get the needed data. However, keep in mind that all data you gather comes in its raw form, which is why data enrichment and data cleaning stand side by side.
Explore this automated, refreshed data solution for lead enrichment