What Can Help In Improving Data Quality?

With the widespread use of analytics and technologies, it has become important to emphasize the benchmark quality of datasets. This is why various businesses prefer benchmark quality. Salesforce states that businesses lose as much as 30% of their revenue because of bad data quality.  This loss costs them around $700 billion every year, which is enormous.

Converting a set of data into valuable and meaningful is called information. People seek this value to make an impact via their feasible decisions or actionable strategies. Only information can make it truly happen, which is all about flawless data quality.

Now that you know that quality is vital, let’s understand what helps in improving it.

Factors that Improve Data Quality

Typically, there are five factors that generate the quality in any database. These are here to understand:

1. Validity

Validity refers to how accurately a technique or method measures the quality. When it comes to data, there are millions of datasets that can be phone numbers, addresses, birthdays, price lists, etc. People, these days, store them on the server or the cloud.

This storage helps in discovering valid records in no time. It is simply because their protocols and functionalities control digital records. These are basically applied to data in any form or digital document. Typically, limitations are found here in different forms.

• Data-type Constraints

These limitations disallow inconsistencies to come up front because of wrong data types in incorrect fields. It is applicable where fields contain alphanumerical or numerical datasets. Let’s say, entering an address in the column of phone numbers or names is restricted due to this data-type validation.

• Range Constraints

Like data-type constraints, range-based limitations prevent inaccuracies from entering. It happens because of the prior information of what information should be there. These ranges can be related to date, age, height, etc.

• Unique Constraints

As its name suggests, these restrictions update themselves each time when anyone inputs data into the digital document. It controls dupes from entering in accordance with parameters. Their perfect example is nationality number, zip codes, national security number, passport number, etc.

• Foreign Key Constraints

These limitations are applied to fields where a set of validation prevents incorrect keys from entering. These keys can be concerned with a country or state, where a range of details can be availed beforehand.

• Cross-field Validation

It ensures that the entered data is correct and corresponds to multiple fields. Let’s say, you enter a bunch of values in a particular number or amount. This particular number or amount will be a validator, which prevents wrong values from being entered there.

1. Accuracy

Accuracy marks the correctness and feasible entries. However, nobody can guarantee 100% accurate data delivery. But, there can be near-accurate assumptions. If you are not competent to find errors, the data cleansing services in Australia or elsewhere can be considered because of its highly promising and active talents. They are able to observe and discover this feasibility in data.

For example, geography can be discovered 100% correctly even if the postal code differs.

1. Completeness

It defines the extent or a certain degree to which the entered value is input in its entirety.  Fixing missing data issues is a big challenge. This problem can be resolved through validations, which disallow entry-makers to leave without completing the information.

1. Consistency

Consistency, here, is the way data responds to verifications with other fields.

Let’s say, a database carried numeric values. It won’t take any other value because only numeric values are allowed there to enter.

Data Cleansing Process for Machine Learning

Data cleansing or cleaning is a group of subsets that include manual and automatic combing datasets.

Manually, it is easy to remove irrelevant information and analyze if a column should be there or not.

On the flip side, you need hands-on experience plus qualifications in handling the automation process of data cleansing. Any mistake can lead to a massive data loss.

Let’s get through the vital processes involved in cleansing.

• Removing Duplicates and Irrelevancy

Sometimes, frames wherein data are processed contain duplicates in various fields. You have to filter them out.

Let’s say, you have a question paper, which involved a number of participants. Despite that every participant had the same questions to answer, the responses may differ.

In this case, a test is conducted by various algorithms. These algorithms are tried and tested validations. During the data mining process, these algorithms check highly irrelevant to the query responses and then, remove them when the processing reaches the finishing line.

• Fix Syntax Errors

Any error in the syntax represents a mistake in a sequence of characters, especially when you work in a particular programming language. If you work for compiled languages, discover & fix these errors at the time of compiling. If it’s a case of any grammatical issue, you have to put an extra ton of effort to correct it manually.

On the other hand, machine learning algorithms automatically find and fix these mistakes or typos in no time.  However, it’s better to prevent syntax errors by forming a structure beforehand. You may set the sequence of the data in the syntax prior to ensure quality.

• Eliminate unwanted outliers

Any sort of unwanted data, which may be structured or useful, but does not best fit the criterion is called outliers. They are the hardest to filter out.

Data scientists thoroughly analyse before declaring & rejecting any set of data as outliers. These are mainly found during modelling. Sometimes, specific models that have extremely low outlier tolerance are controlled by good tolerant outliers. You may trim the dataset by replacing the bad outliers with good ones. But, this happening leads to poor projections.

• Enrichment of Missing Data

Missing data can lead to poor decisions or wrong procedures. Therefore, they have to be found and fixed as soon as possible.

Data enrichment process helps in this. You may fill up missing details upon careful consideration. Avoid random fills, as it can adversely impact the quality of a data model.

In an Excel database, rows may have missing records. You may find the main attributes and then, fill up the missing dataset in the same manner.

Sometimes, data scientists don’t afford to miss details. So, they fill in through guesswork.

The guesses are often based on observations of two or more data points, which are similar to one another. An average value is discovered from these points to replace missing details.

These all hacks can help in improving data quality for modelling or developing artificial intelligence.

Summary

Various factors improve data quality, which is associated with relevancy, validity, accuracy, completeness, and consistency. While taking all of these factors into account, the data cleansing process is carried out successfully.