Enterprise data and business growth go hand in hand in today’s world. The more data your company can analyze and act on, the better its performance will be in all its functions. The global enterprise data management market was valued at US$ 82.25 Billion in 2021, and is expected to carry on its growth at a CAGR of 14% till 2030. It is, therefore, no wonder that businesses are introspecting about their organizational data and questioning its utility.
The need to invest in gaining high-quality, intelligible, actionable data for your company is critical (whether you do it yourself or through a database cleanup services provider) if you don’t want to get left behind.
Why data cleansing? It is because the data a company receives from multiple resources is full of errors of various types. Using this raw data on an as-is basis will do more harm than good, obstructing your path to data-driven success rather than aiding it. You’ll expend unnecessary resources only to get poor returns on that investment. Eventually, it will harm your brand value, reputation, and market position.
But how does one go about it? What is it exactly, and what value does it hold in the year 2023 for your business? If these questions prevent you from moving ahead, then the following sections will answer them in detail so that you can make an informed decision.
Table of Content
- What Is Data Cleansing?
- When Is Data Cleansing Performed?
- ETL stands for Extract, Transform, and Load.
- ELT, on the other hand, stands for Extract, Load, and Transform.
- Reasons To Opt For Data Cleansing
- The Benefits Of Performing Data Cleansing
- Elimination of Data Incompleteness
- Removal Of Bloated Data
- Better Data Source Selection
- More Efficient Database Management
- Improved Customer Relationships
- Improved Supplier/Vendor Relationships
- Lower Employee Stress and Attrition Rates
- Quicker and Better Business Decision Making
- Steps Followed In The Data Cleansing Process
- Preliminary Setup
- Error Monitoring
- Removal of Duplicates
- Fixing Grammatical and Syntactic Errors
- Outlier Filtering
- Fixing Missing Data
- Validating Data Accuracy
What is Data Cleansing?
Data cleansing, also called data cleaning, is the process in which software, a person, or both, go through a given data set to find and replace erroneous, inconsistent, incomplete, or irrelevant data. It forms an integral part of the data management ecosystem for enterprise data, along with other processes like data standardization, normalization, validation, and verification.
When is Data Cleansing Performed?
Data cleansing can be performed at different intervals of the data management pipeline. It depends on the data’s purpose and your company’s data policy. Two common sequences of data management are followed: ETL and ELT.
- ETL stands for Extract, Transform, and Load. It indicates the sequence of data management where the data sets are extracted from the source, followed by the application of the necessary data management functions, and finally, the storage of that data in the company’s data storage facility.
- ELT, on the other hand, stands for Extract, Load, and Transform.It is the data management pipeline sequence where the database cleansing services occur after both the extraction and load phases are completed. Here, the data stored in the company’s data warehousing facility needs to be re-extracted to transform it.
You can choose the method you want to apply based on your requirements. In the case of ETL, however, you can perform data cleansing in real time on data that are incoming from various sources. This way, you can have clean data entered into your database, saving you storage costs since you don’t have to invest in the space that will be occupied by unwanted data.
But, the downside of performing cleansing early on is that the accuracy could take a hit. You may have multiple data sources that are simultaneously supplying large quantities of data at high speeds and the cleaning service may not be able to handle the demands.
CRM data cleansing, in particular, requires human intervention as the variations can be too complex for present-day AI to manage. That makes delays inevitable as even experts take time to perform this task. In such instances, the ELT methodology is the better choice.
When considering the cleansing position amongst the other data management functions, it comes right at the start of the pipeline. This is because it provides the data quality necessary to perform the other processes accurately. If, for instance, there is missing data in a concerned data set, then you won’t be able to perform data standardization as you can’t accurately judge which standard is applicable. Thus, the sooner you perform data cleansing, the better your data management journey will be.
Reasons to Opt for Data Cleansing
Multiple reasons make database cleaning a necessary task for your enterprise.
- The raw input data is often of poor quality and unsuitable for data analyses.
- Unclean data that is kept unused for a long time becomes a liability for your company instead of a valuable asset.
- You will lose your market edge over the competition that’s successfully using cleansed data for its operations.
- It helps prevent the damage that poor-quality data can do to your business in terms of efficiency and output quality.
- Raw data can also contain malware that can harm your entire IT infrastructure and render your business inoperable.
- Prevention is always better than cure, especially considering the cost savings you get by avoiding the problems caused by poor-quality data. Organizations can lose millions in case there is negligence in this regard.
- Your data warehousing resources are limited and data that hasn’t been acted upon bydata cleansing companies will eat up this precious limited resource needlessly.
- The use of artificial intelligence is picking up drastically across industry sectors, and its development via data annotation requires large quantities of data samples that are accurate.
- Customer data should always be up-to-date and accurate. Otherwise, your marketing and sales team could end up addressing the wrong people and lose valuable resources due to a lack of conversion potential. Worse, your brand reputation will suffer a blow.
There may even be other reasons that are specific to your business that compel you to go for data cleansing. What’s certain is that as the role that enterprise data plays gains in importance, the need to have high-quality data obtained through data cleansing goes up as well moving forward.
The Benefits of Performing Data Cleansing
There are numerous benefits that database cleansing services providers offer to businesses by performing that invaluable function.
1. Elimination of Data Incompleteness
Input source data tends to have chunks of it missing due to various reasons. This incompleteness makes the data unsuitable for applications. Data cleansing experts scan through every file and identify possible missing data components in them. Once that has been done, they go about filling in those gaps to complete that data.
This could occur either by adding snippets of old, pertinent data that’s present in their or your database. Alternatively, they could fill in the missing space with data they create based on their knowledge and experience. Or, they could contact the source for the missing data and get it from there. Either way, you’ll have the complete data set you should have.
2. Removal of Bloated Data
Data bloating is what occurs when there is too much-unwanted data that’s accompanying the important data segments. You can identify data bloating based on set standards and your requirements. And then eliminate the unwanted data either by deleting or storing it in a separate registry.
This trims down the data you store to only that which is deemed necessary for your purposes. It saves you precious data storage space and your personnel needn’t waste valuable time trying to separate useful data from the rest. It also prevents them from falling prey to being misled by the unwanted data portions when they are short on time and can’t work on extracting useful data from the rest.
3. Better Data Source Selection
The choice of data source determines the quality and quantity of data you get. Some sources are inevitable but others can be chosen based on the amount of data cleansing needed.
You can determine which source is responsible for delivering poor-quality data based on the corrective action needed to compensate for it. You can then look for a substitute source that can give you better-quality data from the get-go.
This move saves you from having to needlessly spend resources on wasteful sources that are not giving you the kind of customer data your CRM works best on. You can choose a different survey service provider, a better marketing services provider, etc., to better connect with your buyer base.
4. More Efficient Database Management
Database management is an important business function in and of itself. And it can cost dearly if it’s done inefficiently and without a plan. Proof of this is 91% of C-level executives believe that data preparation ultimately costs their business resources, according to an Experian study. Database cleaningplays a large role in bringing down these costs and improving the overall efficiency of this business function.
The cleansing helps bring down the cost of storage since you won’t be wasting storage space to store unwanted data. This is more pronounced in the ETL method of operation. The data clarity obtained helps categorize the data more easily, reducing load and retrieval times that in turn improve the overall turnaround time of the process using the data.
The cleansing also removes potential data security vulnerabilities that may have seeped through otherwise as a part of the excess data. Thus, you don’t have to raise your data security alertness or threat level for your database and can keep it secure with comparatively less effort on that front.
5. Improved Customer Relationships
Business is built on solid relationships with customers as they help with not only the purchase of products but also in spreading brand recognition. Even the best CRM software works best on data that is accurate, helping to guide the direction of your company’s marketing and sales strategy alongside its product development plans. Therefore, CRM data cleansing must be conducted on your customer data without fail.
The trend today is to go with account-based marketing (ABM), where only those clients/ customers are targeted who are most likely to convert, given their capability to purchase the seller’s offering. ABM is possible only by the creation of personal profiles about each client/customer.
A lot of data needs to be gathered via various means to compile such a detailed profile, including consumer behavior and personal preferences. Inaccuracy in any one of these data types can make the profile ineffective.
Using an ineffective profile for your client/customer outreach will not only waste your resources but could also tarnish your brand reputation in their eyes. Data cleansing saves you from this problem. It also aids data enrichment which is crucial to re target old customers and retain them. And, if you hire a data cleaning company for this task, that will further save you from having to spend on tools and resources to reacquire such lost opportunities.
The result is a customer base that is brand awareness and has a good impression of your company. With those, you gain a loyal customer base that can do the marketing for you through positive word-of-mouth and social media shares.
6. Improved Supplier/Vendor Relationships
Besides customers and clients, a company’s raw material suppliers/ vendors and other service providers. The business will grind to a halt without their help, hence it is important to establish and maintain positive work relationships with them. And similar to your customer/clients, you need to have a lot of data about them to accomplish this task.
The selection of the right service provider/vendor is one of the most important actions you can take to keep your business functioning. Choose an unsuitable partner and you’ll be facing problems like delays and cost overruns. But to choose such a partner, you need to know about their history and capabilities, among other things.
Thus, you need to create a detailed profile about such potential partners based on all relevant data you can gather about them, similar to a customer profile. The creation of these profiles demands that you have accurate data about them, something that data cleaning delivers.
Another important task with respect to acquiring the right vendors/business service providers is the signing and enforcement of mutually beneficial contracts. Your enterprise operations data is critical to gaining awareness about how well the clauses established in the contract are being followed. Without cleaning up vital KPI data of your business, you won’t have the clarity to ensure the clauses in the contract are being adhered to in full at either end. With data clarity, you can generate accurate performance reports that help maintain strong relationships with vendors/service providers.
7. Lower Employee Stress and Attrition Rates
The emotional state of your employees plays a crucial role in your company’s productivity. Higher stress levels demotivate them and ultimately lead to higher attrition rates. And with high attrition rates comes greater productivity reductions, expenses to replace them, further demotivate of existing employees, and additional work pile on that only exacerbate their stress levels.
This sets up a negative feedback loop that can bring down your company from the inside if left unchecked. And you can keep a lid on this by utilizing data cleaning. The process begins right when you hire an employee. Doing a background check using clean data about them lets you select the right person for the job. This factor alone reduces employee management issues in the long run. By continuously gathering employee performance data and analyzing it, you can tell how well they are doing and their pain points. The data accuracy offered by data cleansing on this data type can clue you in about their motivation and stress levels. For example, you can look at their medical leave records and check to see if there are common patterns associated with stress.
This knowledge helps you take preemptive steps to lower those factors and stop the employee from quitting. Cleaning employee data also gives you better awareness of trending employee preferences and labor market conditions. Tailoring your company policies to meet those expectations can help your efforts to lower employee stress levels and improve their output while extending their employment.
8. Quicker and Better Business Decision Making
The more you delay your business decisions, the more you will lag behind the competition in serving your target audience. At the same time, you cannot sacrifice business decision quality for that speed, as doing so could lead to poor decisions. To gain and maintain a market edge, you need to make the right business decisions as soon as you can.
Enterprise data will be your best ally here as it gives the clarity you need to make such decisions. You can then make the most rewarding business decisions in the shortest time based on the reports generated from those analyses, as they will give you deeper insights into pertinent business functions.
Steps Followed In The Data Cleansing Process
1. Preliminary Setup
You should prepare your business for data cleaning through a detailed plan and its execution. Start by establishing your requirements from the process. Then set a budget for it based on those requirements and your capabilities.
If you are outsourcing, make a list of potential data cleaning companies that meet your criteria and narrow down your choices to those that suit your demands. Establish a data-sharing policy that protects your data privacy and security while enabling you to provide your data cleansing partner with the input data they need for the project. Work with them to select applicable data sources for your company.
Decide the number and type of employees you want working on this project in conjunction with the data cleaning service provider and delegate associated tasks to them. Ensure that there is transparent communication between your company and your chosen data cleansing agency at all times to facilitate the frequent exchange of project-related information.
2. Error Monitoring
While thedatabase cleaning servicescompany looks into data errors, it helps expedite the entire data cleaning process if you can also contribute to it. You can do this by keeping a tab on data inaccuracy and other issues that commonly plague enterprise data. You can also maintain records of previously generated errors from the sources’ end so that you’ll know what you can expect from that source.
3. Removal of Duplicates
Deduplication is where duplicate data set versions are removed so that only the original version remains. It is vital to maintain a single-source database that organizations seek as it helps eliminate coordination and data file update issues. It also contributes to data bloating reduction.
4. Fixing Grammatical and Syntactic Errors
Grammatical and syntactic errors are a common occurrence in customer-generated data. These errors are removed after deduplication, starting with the easier syntax issues like age, DOB, etc., and then on to more complex ones, like extensive spelling mistakes that take more time. Automation is used extensively here, especially for fixing grammatical errors.
5. Outlier Filtering
This is where the unwanted data mixed with relevant data gets removed. These data sets are called outliers and are the hardest to identify among all types of data issues. Thus, human intervention is necessary here, making this stage time-consuming. By doing this, you get outlier-free data that can work with algorithms that have a low tolerance for them, ensuring accuracy.
6. Fixing Missing Data
Data incompleteness gets addressed here by your data cleaning servicesprovider. The errors are relatively easy to identify but correcting them takes time. The professionals working on the data must search for verifiable data sources that can provide the missing portions.
This takes time and cannot be automated entirely due to the complexity involved. In some cases, the missing portions can’t ever be found and the data services professional fills in that section themselves through their educated guesses.
7. Validating Data Accuracy
Once the cleaning is done, the data is checked again for accuracy to ensure that the deliverable data is free of all errors. Cross-checking occurs by referencing a single data source or multiple sources depending on the data segment being validated.
Businesses driven by data intelligence are the ones thriving in the modern world, and this trend will only become consolidated going ahead. You can also join the ranks with the right preparation and approach. Or, find a data cleaning company that can deliver on your expectations and aid in achieving your business objectives swiftly and profitably.