Augmented Data Quality Best Practices and its Features

Introduction to Augmented Data Quality

As we know Today’s world everything is going digital, so data is very important, not only the data is important, quality of data is also important. Having biometrics is the best example of Data Quality. Suppose biometric machines record wrong information or incomplete information, then how we will use or access biometrics if data is wrong. So data is essential. Similarly, language translators also depend on data, and if a language translator shows us wrong information, we may land in trouble.

What is Data Quality?

Data is essential nowadays; if the data is wrong or incomplete, then we say that data has poor quality. Data quality is measured by how much data is being used and whether the data is correct. For Example, In the login form, if we entered the wrong info then, that data is useless, so the quality of that data is poor. For Example, Like in our face recognition feature, if data is stored wrong, how this feature never works or produces the wrong output. Example

A measurement of the scope of data for the required purpose. It shows the reliability of a given dataset. Taken From Article, Data Quality Management

Why do we need Data Quality?

By having good Data Quality, we can make better decisions for the growth of our organization because if we have incorrect Data, then how will we make planned for the growth of the organization, List of critical points which are must be present as good Data Quality:-

1. Fit for use.
2. Follow all validations, whichever we apply while making the form.
3. Data must be complete.
4. Easily understand and follow the proper structure.

How to improve Data Quality?

There are lots of ways of improving Data quality. Some of are following:-

1. By adding proper validation on the source. Where, source means we take data from customers(like weather by google forms, login form, and others.)
2. Use the proper structure of data while storing time in the database.
3. Correctly add appropriate fields so the customer will understand easily.
4. Check data before saving anywhere, whether the data is correct or not.
5. Do not make confusing forms for customers because if the customer understands what we want as data, then the customer will enter the wrong data into the form.

What are Convolutional Neural Networks?

Firstly, Convolutional means we have two functions, A and B. We apply one operation C on both A and B, which defines how to function A produces function B, so in this example, C is Convolutional. Secondly, Neural Networks means, like, in our brain, we have lots of connected neurons, which help us learn things. For example, if we see a person's face a second time, our brain will recognize faces. Similarly, in data, AI has the concept.CNN is a technique that is used for recognizing the Image. In simple words recognizing the Image means seeing the object, animals, or anything as an image and then seeing the edges of images, for example,

As we know, images are categorized into grayscale images(means ranges 0 to 255, black and white ) colored images combined with RGB). We can also scale down the Image by 0's and 1's. 

0's represent the white and 1's as black. And between 0's and 1's, we have vertical edges. As we see, this Image is 6X6 pix, and when we apply a 3X3 filter on it, we get a 4X4 filter

4X4 filter

In the 4X4 Image, we can apply the min-max scale, which means converts minimum value to 0 and maximum value with 255 because the max value is 255. in the final, we get the middle layer(middle two layers) is our white side and first and last is our dark side.

What is Augmented Data Quality?

In simple words, we will automate the Data Quality process by performing ADQ techniques or functions on data. The main motive of Augmented Data Quality is to reduce the manual tasks related to data quality, saving time and resources. Example We have one Image which rectangle with one, and we pass that Image to CNN. The CNN will the different types of invariance on that particular Image give us lots of Images within different styles, like in the diagram, we apply different rotations on images. We do zoom in and zoom out as well.

Why do we need Augmented Data Quality?

By Augmented Data Quality, we will analyze information daily to identify the pattern of data quality. As the volume of data increases day by day, organizations will evolve to transform into data-driven organizations; for increasing the speed of this process, they will augment their data for improving the quality of data. 

Suppose we have very few images and we want many images. In that case, we will have different invariance or transformation on images, and we get lots of images from images because let's take an example. If we pass input is X(A), then output is Y(A ). If the input is X(B), the output is Y(B).

Automate the Data quality Task: Profiling, matching data, poor quality warning, merging, these are the data quality tasks, which are automated by Augmented Data Quality functions for improving the data quality.

How does Augmented Data Quality work?

We will create new augmented data by applying reasonable filters on data or images. We can also augment the text, images, audio, and other data. For Image augmented, we use TensorFlow or Keras. Keras uses various layers to apply augmentation to data.

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What are the common Challenges of Augmented Data Quality?

The ever-increasing amount of data has created various challenges regarding data quality. Accurate information has become more essential as businesses use data-driven decision-making. Here are some of the main challenges related to data quality:

• Data Duplication: Data duplication can occur when multiple sources store and provide similar data, but each source might contain different interpretations of the same facts. This can lead to difficulty in identifying accurate duplicates.
• Wrong Data Representations: Inaccuracies can also enter due to errors in the data representation. For example, if a customer's address is entered incorrectly or incompletely, this could prevent them from receiving goods or services or finding their way back to the company.
• Poor Data Formatting: Data formatting can lead to clarity, especially with larger datasets. Data not formatted uniformly or consistently can create issues when creating reports or analyzing the data.
• Fragmented Data Sources: Data silos can form when data is trapped in disparate systems. This can make gaining comprehensive insights across multiple departments or business entities challenging.
• Outdated Information: Stale and outdated information can linger in databases without proper governance, creating inconsistencies and miscommunications.

Several challenges need to be addressed when implementing augmented data quality systems:

1. Establishing a process for data cleaning.
2. Creating criteria for determining accurate data.
3. Creating methods for validating data accuracy.
4. Automating data transformation and cleansing processes.
5. Fighting contamination from external sources.
6. Managing security and privacy requirements.
7. Establishing data governance infrastructure

What are the features of Augmented Data Quality?

The features of Augmented Data Quality are listed below:

Data Integration

In traditional general, tools are replacing and moving the data for data quality, but this Augmented Data Quality will combine data from all resources to get the well structured, and the organization uses the Augmented data quality to make the data easy for real-time analytics. Reducing complexities is critical for any organization to achieve its business objectives.

Unstructured Data

Most of the time, data is not stored in a perfect framework or particular structure. For that, augmented data quality also does lots of help. ADQ also sets some missing, corrupted data.

Accuracy

We can check data accuracy by the percentage of records falling between the upper and lower limit whatever we choose. For example, we want to measure the percentage of completed products that meet the total produced where the lower limit is 93, and we set the upper limit as 97. If we get a percentage outside of the range on the first day, it will have a high impact on data accuracy.

Data Quality Rules and Patterns

Augmented Data Quality will suggest the data quality rule for improving the quality of data. It suggests the requirements based on datasets and the pattern for cleansing merging the data.

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What are the standard methods of Augmented Data Quality Management?

According to Gartner, we will apply Augmented Data Quality in the following areas:

Discovery

This feature is developed by using the reference data in distributed environments with a large number of data assets and active meta. In this, we discover where the data resides, for instance, sensible data for privacy purposes.

Suggestion

Augmented Data Quality will suggest the data quality rule for improving the quality of data. ADQ suggests the requirements based on datasets and the pattern for cleansing merging the data.

What are the standard Data Quality checks?

The standard data quality checks are described below:

Uniqueness

If in our data we have multiple items are repeated, meaning if duplicate values are tremendous, then we will consider that data as poor quality. ADQ will use the SQL rules to apply duplicate items or data filters. Once we can apply that filter, we apply that filter automatically on our data.

Completeness

We consider our Data complete when nothing is missing in our data. We can set the search rule on null values to identify the missing values or any crucial vital data.

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What are the Best Practice of Augmented Data Quality?

Best practices of Augmented Data Quality are given below:

By Automating the Data Quality Task

Data Quality tasks are profiling, merging, cleansing, and monitoring, also by linking automatically between entities.

Start Small and Align KPIs(key performance indicators)

 KPI means we check data quality by performance. Based on performance, we generate KPIs. KPIs are measurable values used to evaluate how successful a person or organization is at reaching a target. Our data, in the beginning, does not have to be used in data science and artificial intelligence directly. First, we have to choose the use case that will align with KPI's when we get success once then move to larger projects.

Focus on Data Quality and Reduce Duplicates

We can increase data quality by removing the repeated values or duplicate values from data. It is an essential and best practice of augmented DQ.

Create Data Recovery or Backup Policy

Suppose if we lose our data for any reason, if we have these kinds of policies, we can easily handle that problem.

By Applying Rules to Specific Data Types

By Data Quality profiling we can scan any type of data in real-time.

Using the Governing Data Approach

In this approach, we create some conditions by which we can get that only need data on time based on those conditions.

What is the flow of Augmented Data Quality?

For adding the augmentation to the data set, we have to follow steps:-

1. First, we have to transform and then copy that statement and use it as a tranform_train statement. Then next start applies alternation or invariance on data sets.
2. The alternation of invariance depends on our need for whatever we want like, rotation, scaling, zooming, or whatever. Next, change the data set according to need or transformation.

Automate Data Quality processes and rules with Artificial Intelligence and Machine Learning. Augmented Data Quality Solutions

Conclusion

Data is a pillar of the organization, so we must follow excellent and simple rules while storing the data. Profiling, matching data, poor quality warning, and merging are the data quality tasks automated by ADQ functions to improve the data quality. The quantity of data doesn't matter, but how much we use the data correctly. 

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