Data is vast. Data is important. Data is like the backbone of a business. Data helps in the decision-making processes.
Yes, you already know all that, but can we get to the more pressing question here? Is all the data relevant? You may have countless bytes of business and external data at your disposal, but raw data often contains noise (also termed as bad data) that hampers with the quality of the overall data, and hence your decision-making processes. In fact, bad data can cause additional cost implications for businesses, which eventually lead to huge losses.
Fact: According to the Harvard Business Review, bad data costs the US $3 Trillion per year.
Where does this bad data come from in the first place? Well, there can be many possible answers to that question, including:
- It may be a result of inaccurate data entry (the biggest shortcoming of manual data entry).
- Sometimes, missing data in databases is ignored, which leads to incomplete information.
- Coding standards are often not up to the mark.
- Some businesses use old or outdated systems that may add in some obsolete data.
Most of these reasons are unavoidable. But they can sure be fixed with the help of data cleansing and data scrubbing.
Let’s see how.
Data Cleansing and Business Process Optimization: How are the two related?
As you are probably aware that everything, including data, employs automation. Well, as effective as that approach is, it still leaves room for some undesirable errors. These errors, when left uncorrected, contribute to bad data. This bad data later leads to bigger problems, especially with regard to business process optimization. You can’t optimize or even configure your business processes without appropriate data at your disposal. And let’s not even talk about the consequences if you do decide to continue with bad data. Additional cost implications, business losses, fallen strategies, and whatnot.
This is why you need to treat bad or dirty data before it leads to greater repercussions. As a famous computer axiom suggests: GIGO i.e., Garbage In Garbage Out, data hygiene is something that is paramount and cannot be ignored.
Data Scrubbing: How it helps in improving data quality?
Data scrubbing is basically an error correction technique. Using this technique, data or a database is studied to identify various errors such as incorrect, missing, incomplete, or duplicate information. Once identified, they are then corrected using various data correction methods.
Correcting the lurking errors in your database can help in improving your data quality. Here’s how you must go about it:
- Once you find out the areas where data quality is a problem, review the current data and identify how different is it from your “target” data quality.
- Use data scrubbing tools and software to identify, correct, and implement the changes in the data, resulting in cleansed data.
Let’s get better: Steps to improvement
The main intent is to improve your data quality on the whole and making sure that no possible errors persist in your database. Many business owners choose to go for data cleansing companies to ensure a quality end result.
Here are some important steps that need to be followed to improve data quality:
The very first step is identifying the area of the problem. This can have two aspects:
- Data quality in terms of business (outliers, dictionaries, etc.)
- Data quality in terms of technical accuracy (data formats, statistics, etc.)
Based on these metrics, a data profiling report must be generated which should contain a description of all the problems in the data that are leading to poor quality. Various interactive tools can be used to make this report. This report will come in handy at the time of data cleansing.
2. Data cleaning:
Once you have a detailed report, it’s time to start the cleaning process. The data cleaning process comprises the following steps:
- Parsing: Parsing is basically a method of breaking down a complex field into numerous simple fields in order to understand the context. Using the segmented information, missing or duplicated data is corrected.
- Standardization: This is used when there are a number of different instances of the same variable in a database. For example, your database may have two notations to represent the word “Los Angeles”: LA and Los Angeles. Standardization will replace the two with a single user-defined value in order to eliminate confusion.
- Deduplication: Multiple entries of the same data are identified and then consolidated to get rid of duplicate data.
3. Final data set preparation:
A final data set must then be prepared with all the changes implemented. This is the “cleaned” data that is now ready to be used to define your business processes. These data quality assurance processes can then be automated so that they help you maintain a certain level of data quality for a long time.
So, there you have it – the answer to the million-dollar question. A business that aims for better growth and profits cannot thrive or succeed without clean data.
Let SunTec Data Be Your Savior
Data cleaning can be a taxing process, especially when you have a huge amount of data to deal with. In any case, you don’t need to worry. You can simply outsource all your data processing requirements to a data cleansing company like SunTec Data. Our data experts are highly capable of working with state-of-the-art data cleansing tools and ensuring that the end result you get is perfect. Start off by writing to us at firstname.lastname@example.org and let’s take it from there.
Brought to you by the Marketing & Communications Team at SunTec Data. On this platform, we share our passion for Data Intelligence as well as our opinions on the latest trends in Data Processing & Support Services. Join our mailing list to stay updated.