Trending December 2023 # Google Redefines What Is Considered Low Quality Content # Suggested January 2024 # Top 21 Popular

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Google updated its Quality Rater Guidelines this week, which includes new information regarding the assessment of “low quality” and “lowest quality” pages.

Of all the changes made to the guidelines, the sections on page quality received the most significant updates.

Quality Rater Guidelines are a set of instructions that Google’s quality raters follow when manually evaluating the performance of Google’s algorithms.

So, in other words, if a rater were to analyze whether or not a piece of content is of “low quality,” they would refer to what is laid out in the Quality Rater Guidelines.

It’s important to know that quality raters cannot personally change how a page is ranked. Rather, they pass feedback onto those who write Google’s algorithms.

From there, an algorithm update may be pushed out which would then impact page rankings.

How Google’s Quality Rater Guidelines Defines Low Quality Pages

According to Google’s updated Quality Rater Guidelines, low quality pages are those that miss the mark on what they set out to achieve.

This could be for one of two reasons. Either there is not enough main content (MC) to adequately satisfy the reader, or the content creator lacks expertise in the topic they’re writing about.

“Low quality pages may have been intended to serve a beneficial purpose. However, Low quality pages do not achieve their purpose well because they are lacking in an important dimension, such as having an unsatisfying amount of MC, or because the creator of the MC lacks expertise for the purpose of the page.”

The key difference between this revised definition of low quality pages, and the previous definition, is that quality should still be considered “low” even if there was a clear intention for the page to serve a beneficial purpose.

Quality Raters are instructed to rate a page as “Low” if any one or more of the following applies:

An inadequate level of Expertise, Authoritativeness, and Trustworthiness (E-A-T).

The quality of the MC is low.

There is an unsatisfying amount of MC for the purpose of the page.

The title of the MC is exaggerated or shocking.

The Ads or SC distracts from the MC.

There is an unsatisfying amount of website information or information about the creator of the MC for the purpose of the page (no good reason for anonymity).

A mildly negative reputation for a website or creator of the MC, based on extensive reputation research. If a page has multiple Low quality attributes, a rating lower than Low may be appropriate.

Google elaborates on this point, stating:

Here is a roundup of other notable changes that were made to the “Low Quality Pages” and “Lowest Quality Pages” sections.

Ads should now be considered distracting if they feature grotesque images.

Extensive research is required to evaluate the reputation of a content creator.

Identifying a content creator using a long-standing Internet alias or username is now acceptable.

A page is of “lowest” quality when the purpose of the page cannot be determined.

’Your Money, Your Life’ pages with no information about the content creator should be rated lowest.

Unmaintained websites should be rated lowest quality if they fail to achieve their purpose due to the lack of maintenance.

Pages that promote hate against groups of people based on socio-economic status, political beliefs, and victims of atrocities should be rated lowest.

Pages that promote mental, physical, or emotional harm to self or others should be rated lowest.

Content should be rated lowest if the creator has a negative or malicious reputation.

Pages that misinform users with “demonstrably inaccurate content” should be rated lowest.

The points listed above are all new additions to Google’s Quality Rater Guidelines.

For more information, see the full PDF document here.

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Bloomreach Launches Content Quality Metrics

BloomReach today released Continuous Quality Management (CQM) technology delivering ongoing web page quality visibility and management. If you haven’t checked out BloomReach, they are a pretty sweet analytics software that helps customers analyze the long tail.  CQM introduces four types of continuously updated quality metrics for every page managed by BloomReach including content, behavior, uniqueness and flux.  CQM gives marketers more control over the quality of each page published by combining marketer judgment with machine-learning to deliver the best user experience, matching content to intent.

The Metrics of Continuous Quality Management

The four types of metrics within CQM are Content, Behavior, Uniqueness and Flux. Every metric can be used for analysis, filtering and to set thresholds for action.

Content is determined by interpreting a page’s topic and comparing the content to the topic.  Content Quality considers the number of unique, relevant products on the page as well and the fit of the products and their attributes to the intent.

Behavior is measured by integrating traffic metrics such as bounce rate, conversion in addition to other factors.

Uniqueness is measured using BloomReach’s

Dynamic Duplication Reduction (DDR)

technology to determine how much of the content on the page is unique versus other pages on the site.

Flux captures the rate of change of products on the page, which is critical to understanding why quality can degrade over time and critical to predicting pages that require further inspection.

“The perfect balance of technology for generating high-quality content and relevant experiences are those that let machines do what they do best – interpret and act on unmanageable amounts of data – and leave the fine tuning and quality assurance to humans,” said Raj De Datta, CEO of BloomReach. “The launch of CQM gives e-tail marketers a proven tool to manageably capture the long tail at scale with high quality, relevant content.”

I’m interested to see where this technology takes our World.  Does anyone use these guys, I’d love to have a deeper understanding of how they are liking the service.

What Is Data Quality In Machine Learning?

f ML models. It will also delve into the ETL pip he techniques used for data cleaning, preprocessing, and feature engineering. By the end of this article, you will have a solid understanding of the importance of data quality in ML and the techniques used to ensure high-quality data. This will help to implement these techniques in real-world projects and improve the performance of their ML models.

Learning Objectives

Understanding the basics of machine learning and its various applications.

Recognizing the importance of data quality in the success of machine learning models.

Familiarizing with the ETL pipeline and its role in ensuring data quality.

Learning multiple techniques for data cleaning, including handling missing and duplicate data, outliers and noise, and categorical variables.

Understanding the importance of data pre-processing and feature engineering in improving the quality of data used in ML models.

Practical experience in implementing an entire ETL pipeline using code, including data extraction, transformation, and loading.

Familiarizing with data injection and how it can impact the performance of ML models.

Understanding the concept and importance of feature engineering in machine learning.

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is Machine Learning?

Why is data critical in Machine learning?

Collection of Data Through ETL Pipeline?

What is Data Injection?

The Importance of Data Cleaning

What is Data Pre-processing?

A Dive into Feature Engineering

Complete code for the ETL-Pipeline


What is Machine Learning?

Machine learning is a form of artificial intelligence that enables computers to learn and improve based on experience without explicit programming. It plays a crucial role in making predictions, identifying patterns in data, and making decisions without human intervention. This results in a more accurate and efficient system.

Machine learning is an essential part of our lives and is used in applications ranging from virtual assistants to self-driving cars, healthcare, finance, transportation, and e-commerce.

Data, especially machine learning, is one of the critical components of any model. It always depends on the quality of the data you feed your model. Let’s examine why data is so essential for machine learning.

Why is Data Critical in Machine Learning?

We are surrounded by a lot of information every day. Tech giants like Amazon, Facebook, and Google collect vast amounts of data daily. But why are they collecting data? You’re right if you’ve seen Amazon and Google endorse the products you’re looking for.

Finally, data from machine learning techniques play an essential role in implementing this model. In short, data is the fuel that drives machine learning, and the availability of high-quality data is critical to creating accurate and reliable models. Many data types are used in machine learning, including categorical, numerical, time series, and text data. Data is collected through an ETL pipeline. What is an ETL pipeline?

Collection of Data Through ETL Pipeline

Data preparation for machine learning is often referred to as an ETL pipeline for extraction, transformation, and loading.

Extraction: The first step in the ETL pipeline is extracting data from various sources. It can include extracting data from databases, APIs, or plain files like CSV or Excel. Data can be structured or unstructured.

Here is an example of how we extract data from a CSV file.

Python Code:

import pandas as pd #read csv file df = pd.read_csv("data.csv") #extract specific data name = df["name"] age = df["age"] address = df["address"] #print extracted data print("Name:", name) print("Age:", age) print("Address:", address)

Transformation: It is the process of transforming the data to make it suitable for use in machine learning models. This may include cleaning the data to remove errors or inconsistencies, standardizing the data, and converting the data into a format that the model can use. This step also includes feature engineering, where the raw data is transformed into a set of features to be used as input for the model.

This is a simple code for converting data from json to DataFrame.

import json import pandas as pd #load json file with open("data.json", "r") as json_file: data = json.load(json_file) #convert json data to a DataFrame df = pd.DataFrame(data) #write to csv df.to_csv("data.csv", index=False)

Load: The final step is to upload or load the converted data to the destination. It can be a database, a data store, or a file system. The resulting data is ready for further use, such as training or testing machine learning models.

Here’s a simple code that shows how we load data using the pandas:

import pandas as pd df = pd.read_csv('data.csv')

After collecting the data, we generally use the data injection if we find any missing values.

What is Data Injection?

Adding new data to an existing data server can be done for various reasons to update the database with new data and to add more diverse data to improve the performance of machine learning models. Or error correction of the original dataset is usually done by automation with some handy tools.

There are three types.

Batch Insertion: Data is inserted in bulk, it is usually at a fixed time

Real-time injection: Data is injected immediately when it is generated.

Stream Injection: Data is injected in a continuous stream. It is often used in real-time.

Here is a code example of how we inject data using the append function using the pandas library.

The next stage of the data pipeline is data cleaning.

import pandas as pd # Create an empty DataFrame df = pd.DataFrame() # Add some data to the DataFrame df = df.append({'Name': 'John', 'Age': 30, 'Country': 'US'}, ignore_index=True) df = df.append({'Name': 'Jane', 'Age': 25, 'Country': 'UK'}, ignore_index=True) # Print the DataFrame print(df) The Importance of Data Cleaning

Data cleaning is the removal or correction of errors in data. This may include removing missing values and duplicates and managing outliers. Cleaning data is an iterative process, and new insights may require you to go back and make changes. In Python, the pandas library is often used to clean data.

There are important reasons for cleaning data.

Data quality: Data quality is crucial for accurate and reliable analysis. More precise and consistent information can lead to actual results and better decision-making.

Performance of machine learning: Dirty data can negatively affect the performance of machine learning models. Cleaning your data improves the accuracy and reliability of your model.

Data storage and retrieval: Clean data is easier to store and retrieve and reduces the risk of errors and inconsistencies in data storage and retrieval.

Data Governance: Data cleansing is crucial to ensure data integrity and compliance with data regulatory policies and regulations.

Data storage: Wiping data helps save data for long-term use and analysis.

Here’s code that shows how to drop missing values and remove duplicates using the pandas library:

df = df.dropna() df = df.drop_duplicates() # Fill missing values df = df.fillna(value=-1)

Here is another example of how we clean the data by using various techniques

import pandas as pd # Create a sample DataFrame data = {'Name': ['John', 'Jane', 'Mike', 'Sarah', 'NaN'], 'Age': [30, 25, 35, 32, None], 'Country': ['US', 'UK', 'Canada', 'Australia', 'NaN']} df = pd.DataFrame(data) # Drop missing values df = df.dropna() # Remove duplicates df = df.drop_duplicates() # Handle outliers df = df[df['Age'] < 40] # Print the cleaned DataFrame print(df)

The third stage of the data pipeline is data pre-processing,

It’s also good to clearly understand the data and the features before applying any cleaning methods and to test the model’s performance after cleaning the data.

What is Data Pre-processing?

Data processing is preparing data for use in machine learning models. This is an essential step in machine learning because it ensures that the data is in a format that the model can use and that any errors or inconsistencies are resolved.

Data processing usually involves a combination of data cleaning, data transformation, and data standardization. The specific steps in data processing depend on the type of data and the machine learning model you are using. However, here are some general steps:

Data cleanup: Remove errors, inconsistencies, and outliers from the database.

Data Transformation: Data transformation into a form that can be used by machine learning models, such as changing categorical variables to numerical variables.

Data Normalization: Scale data in a specific range between 0 and 1, which helps improve the performance of some machine learning models.

Add Data: Add changes or manipulations to existing data points to create new ones.

Feature Selection or Extraction: Identify and select the essential features from your data to use as input to your machine learning model.

Detect Duplicates: Identify and remove duplicate data points. Duplicate data can lead to inaccurate or unreliable results and increase the size of your data set, making it difficult to process and analyze.

Identify Trends: Find patterns and trends in your data that you can use to inform future predictions or better understand the nature of your data.

Data processing is essential in machine learning because it ensures that the data is in a form the model can use and that any errors or inconsistencies are removed. This improves the model’s performance and accuracy of the prediction.

Here is some simple code that shows how to use the LabelEncoder class to scale categorical variables to numeric values and the MinMaxScaler class to scale numeric variables.

import pandas as pd from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder # Create a sample DataFrame data = {'Name': ['John', 'Jane', 'Mike', 'Sarah'], 'Age': [30, 25, 35, 32], 'Country': ['US', 'UK', 'Canada', 'Australia'], 'Gender':['M','F','M','F']} df = pd.DataFrame(data) # Convert categorical variables to numerical encoder = LabelEncoder() df["Gender"] = encoder.fit_transform(df["Gender"]) # One hot encoding onehot_encoder = OneHotEncoder() country_encoded = onehot_encoder.fit_transform(df[['Country']]) df = pd.concat([df, pd.DataFrame(country_encoded.toarray())], axis=1) df = df.drop(['Country'], axis=1) # Scale numerical variables scaler = MinMaxScaler() df[['Age']] = scaler.fit_transform(df[['Age']]) # Print the preprocessed DataFrame print(df)

The final stage of the data pipeline is feature engineering,

A Dive into Feature Engineering

Feature engineering transforms raw data into features that can be used as input for machine learning models. This involves identifying and extracting the most critical data from the raw material and converting it into a format the model can use. Feature engineering is essential in machine learning because it can significantly impact model performance.

Different techniques that can be used for feature engineering are:

Feature Extraction: Extract relevant information from raw data. For example, identify the most important features or combine existing features to create new features.

Attribute Modification: Change the attribute type, such as changing a categorical variable to a numeric variable or zooming the data to fit within a specific range.

Feature Selection: Determine the essential features of your data to use as input to your machine learning model.

Dimension Reduction: Reduce the number of features in the database by removing redundant or irrelevant features.

Add Data: Add changes or manipulations to existing data points to create new ones.

Feature engineering requires a good understanding of your data, the problem to be solved, and the machine learning algorithms to use. This process is iterative and experimental and may require several iterations to find the optimal feature set that improves the performance of our model.

Complete Code for the Entire ETL Pipeline

Here is an example of a complete ETL pipeline using the pandas and scikit-learn libraries:

import pandas as pd from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder # Extract data from CSV file df = pd.read_csv('data.csv') # Data cleaning df = df.dropna() df = df.drop_duplicates() # Data transformation encoder = LabelEncoder() df["Gender"] = encoder.fit_transform(df["Gender"]) onehot_encoder = OneHotEncoder() country_encoded = onehot_encoder.fit_transform(df[['Country']]) df = pd.concat([df, pd.DataFrame(country_encoded.toarray())], axis=1) df = df.drop(['Country'], axis=1) scaler = MinMaxScaler() df[['Age']] = scaler.fit_transform(df[['Age']]) # Load data into a new CSV file df.to_csv('cleaned_data.csv', index=False)

The data is first retrieved from a CSV file using this example’s pandas read_csv() function. Data cleaning is then done by removing missing values and duplicates. This is done using LabelEncoder to change categorical variables to numeric, OneHotEncoder to scale categorical variables to numbers, and MinMaxScaler to scale numerical variables. Finally, the deleted data is read into a new CSV file using the pandas to_csv() function.

Note that this example is a very simplified version of the ETL pipeline. In a real scenario, the pipeline may be more complex and involve more processing and outsourcing, costing, etc. can include methods such as. In addition, data traceability is also essential. That is, it tracks the origin of the data, its changes, and where it i s not only helps you understand the quality of your data but also helps you debug and review your pipeline. Also, it is essential to clearly understand the data and features before applying post-processing methods and checking the model’s performance after pre-processing. Information.


The Data quality is critical to the success of machine learning models. By taking care of every step of the process, from data collection to cleaning, processing, and validation, you can ensure that your data is of the highest quality. This will allow your model to make more accurate predictions, leading to better results and successful machine-learning projects.

Now you will know the importance of data quality in Machine learning. Here are some of the key takeaways from my article:

Key Takeaways

Understanding the impact of poor data quality on machine learning models and the resulting outcomes.

Recognizing the importance of data quality in the success of machine learning models.

Familiarizing myself with the ETL pipeline and its role in ensuring data quality.

Acquiring skills for data cleaning, pre-processing, and feature engineering techniques to improve the quality of data used in ML models.

Understanding the concept and importance of feature engineering in machine learning.

Learning techniques for selecting, creating, and transforming features to improve the performance of ML models.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


What Is Project Quality Management? Why Is It Important?

blog / Project Management What is Project Quality Management? How Does it Boost Customer Satisfaction?

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For anyone facing challenges in meeting project specifications or delivering quality projects, look no further than auto major Toyota for inspiration. Right from the development stage to the production line, the Japanese company uses a systematic approach to ensure every aspect of its projects meets the highest standard. Essentially, this has helped it gain an unassailable reputation in the automotive industry for delivering reliable and high-quality vehicles. You too can achieve similar results by following Toyota’s Project Quality Management (PQM) mantra. Let’s dig deeper into this management approach.

Project Quality Management

It is an integrated framework of any organization’s total quality management process. The management of quality comprises processes required to deliver a project on time while ensuring that the demands of the stakeholders, including customers, are met. Moreover, it is the ability to manage a product and deliver output in conformity with the requirements of the users while maximizing profits for the company.

Project quality management combines two frameworks: quality management and project management. For instance, let’s take the example of a multimedia tech company that is working on a new high-quality software application. Now, to ensure that it meets customer expectations and delivers value, there are two aspects that the project management team needs to look into. First, it has to ensure that there are no functionality defects in the software (this is referred to as quality management). Next, it must ensure that the new software is equipped with the latest features that are in demand in the market so that the company can generate more revenue (this is referred to as project management). 

ALSO READ: What is Project Management and How to Become a Successful PM

Importance of Project Quality Management Better Decision Making

PQM provides relevant data to an organization and enables it to make strategic decisions by identifying quality requirements. This helps generate revenue.

Enhanced Management

PQM promotes a culture of continuous improvement, leading to increased job satisfaction and a sense of accomplishment among the team members. It facilitates the seamless implementation of management processes.

Enhances Brand Reputation

PQM can help an organization establish a reputation for delivering high-quality products and services, leading to increased business opportunities and a stronger brand.

Project Quality Management Processes Quality Management Planning

It sets quality standards and objectives for the project and determines how these standards will be met. In addition, it includes identifying the processes, tools, and techniques to manage and monitor project quality.

Perform Quality Assurance

PQM ensures that the project is executed by the quality plan. Additionally, this includes activities such as regular quality audits and inspections, as well as continuous monitoring of project performance.

Control Quality

It monitors and controls the quality of project outputs, including products, services, and deliverables. Moreover, this is done by testing, inspections, and verifying the project requirements have been met.

Key Elements of Project Quality Management

Customer Satisfaction:

The primary goal of PQM is to ensure that a project meets or exceeds customer requirements and expectations, with customer satisfaction being the best way to assess this. Thus, customer satisfaction is a core element of quality management.

Prevention Over Inspection:

Inspection-based quality control focuses on detecting and correcting defects after they have occurred. In essence, this approach is reactive and can be costly and time-consuming. PQM enables organizations to take proactive measures to prevent defects, improve quality, and achieve better project outcomes.

Continuous Improvement

: In PQM, continuous improvement is viewed as an essential component of achieving high levels of customer satisfaction. Moreover, increasing project efficiency and reducing the cost of quality are also considered vital.

Benefits of a Project Quality Management Plan

A project quality management plan offers the following benefits throughout the PQM lifecycle:

Improved Customer Satisfaction

In a PQM plan, customers’ needs and expectations are clearly defined. Moreover, the organization focuses on achieving these needs. This increases customer satisfaction and a better overall perception of the project and the organization.

Enhanced Project Performance Better Resource Utilization

PQM provides a framework for monitoring and controlling the use of resources. Thus, it results in more efficient use of time, money, and human resources.

Increased Stakeholder Confidence Project Quality Management Tools and Techniques

There are different types of tools and techniques to aid this process.

Project Quality Management (PQM) Tools

Cost of Quality (COQ) Analysis:

It calculates the cost of poor quality, including the cost of prevention, appraisal, and internal and external failure

Statistical Process Control (SPC):

This is a set of statistical methods used to monitor and control a process to ensure it is operating within defined limits

Control Charts:

This is a statistical tool used to monitor a process to determine if it is in control, and identifies any special cause variations

Pareto Charts:

These are graphical representations showing which factors are contributing the most to a problem or issue


: It represents a process or workflow, used for identifying potential bottlenecks or areas for improvement

Project Quality Management Techniques

Continuous Improvement:

This is a systematic approach that identifies and implements improvements in a process or product

Quality Audits

: This is an independent review of a process or product to assess its compliance with quality standards

Design of Experiments (DOE):

DOE is a statistical method used to determine the effect of different variables on a process or product

Lean Methodology:

This is a management approach that seeks to optimize the flow of work by eliminating waste and improving efficiency

Phases of Project Quality Management

There are three dimensions or levels of project quality management. The first involves meeting the specified requirements and is called quality control. Then, the next is quality management; which focuses on working beyond the specified requirements. Finally, the last level is known as total quality management. In essence, it focuses on constant improvement to enhance customer satisfaction. 

The following are the five essential phases of project quality management:

Project Quality Initiation

: The project quality management framework starts with identifying a potential project and seeking authorization to initiate it. Here, a project manager is appointed who selects the core team and identifies potential risks involved in the project. 

Project Quality Planning

: Now, all stakeholders are informed about the project and their approval and inputs are received. The project management team starts preparing an action plan to complete the project. This step requires market research to understand customer requirements and gather data on ongoing market trends. In addition, the team identifies customer satisfaction standards and suppliers.

Project Quality Assurance

: In the next stage, the project management team focuses on improving the processes to provide quality deliverables. It involves communicating with external stakeholders to overcome obstacles. The team gathers data, identifies the root cause of defects, and conducts quality audits 

Project Quality Control:

In the quality control phase, the project management team analyzes customer satisfaction levels and focuses on improving processes. This stage mainly involves running various tests and correcting errors to ensure the best quality.

Project Quality Closure:

Lastly, the team delivers the final project to the customers, who accept the project and are satisfied. In fact, this phase also involves providing support and training to the customers, assessing the overall project to improve the processes, and acknowledging and rewarding various members of the project management team.

A Career in Project Quality Management

You will need to know the following if you are planning to build a career in project quality management:

Quality management methodologies, such as Six Sigma or Lean

Quality tools and techniques, such as statistical process control and root cause analysis

Quality standards and regulations, such as ISO 9001 or CMMI

Project management methodologies, such as Agile or Waterfall

Quality metrics and measurement techniques

Quality management software and systems

Risk management and mitigation strategies

Due to intense competition in the market and rapidly changing market conditions, project quality management has become an integral part of the project management framework. Hence, at Emeritus, we have online project management courses that can help you acquire such relevant skills to accelerate your career.

Write to us at [email protected]

Quality Raters Guidelines Update – This Is What Changed

Google has published New Quality Raters Guidelines. It contains significant changes to the Your Money or Your Life section as well as new areas of focus. The following tracks the changes and how they may influence SEO trends.

Quality Raters Guidelines are Not Algorithm Hints

Let’s get this out of the way. Many people look to the guidelines for tips on how Google’s algorithm works. This is the wrong way to look at it.

The guidelines instruct the quality raters to focus on certain signals and page properties for the purpose of judging the quality of those pages. What they are instructed to look for are not ranking factors.

Google just wants to know if the sites the algorithm is ranking meets certain quality standards and the instructions are meant to guide the raters on what to look for. That’s it.

Guidelines Do Not Hint at Ranking Signals

The guidelines are written to help the 3rd party quality raters to rate web pages. There are no hints as to what ranking signals are in Google’s algorithm.

However the guidelines to provide hints as to what kinds of quality issues the algorithm may be focusing on.

Quality Guidelines Predict Trends

The quality guidelines have been remarkably accurate for predicting the algorithm trends. For example, the increased instructions on how to rate medical and financial sites coincided with algorithms designed to improve the relevance of those kinds of sites.

Google’s last few core algorithm updates have strongly affected News sites. The fact that the news section was added to the QRG shows how the QRG can reflect where past or future algorithms are focused on.

So while there may not be hints about ranking signals in the QRG, it may be possible to deduce algorithm trends.

What Changed in Google’s Quality Raters Guidelines

A key change to the quality raters guidelines appears in section 2.3.

Section 2.3 handles Your Money or Your Life (YMYL) topics. The change affects news and government related topics.

Previously the news topic section was grouped together with public/official information pages.

The News topic is now it’s own section. It provides guidance on how to judge and rate news pages.

Google has been under fire by politicians and pundits who charged that Google is biased. So it may not be coincidental that the News and the Government/Civics section have been given emphasis in the new quality raters guidelines.

Topics Emphasized Over Pages

It may seem minor, but Google has emphasized the topic of a page over the word “page” itself. The word “pages” has been removed in many places. The word “topic” has been added in many places.

De-emphasizing the word “pages” has the effect of refocusing the sentences on the newly added instances of the word “topic.”

This change happens in the introduction of YMYL section (2.3) and follows through to the entire section.

Old Version

“Some types of pages could potentially impact the future happiness, health, financial stability, or safety of users. We call such pages “Your Money or Your Life” pages, or YMYL. The following are examples of YMYL pages:”

New Version, with additions added:

“Some types of pages or topics could potentially impact a person’s future happiness, health, financial stability, or safety. We call such pages “Your Money or Your Life” pages, or YMYL. The following are examples of YMYL topics:”

This change may appear minor but it has the effect of emphasizing the topicality of a page as something to focus on.

“Some types of pages or topics…”

A phrase like that encourages the raters to think in terms of topics and the topic of the page.

The SEO industry has been pivoting toward thinking of content in terms of topics for the past few years. This is a reaction to the search algorithm updates that seem to be increasingly better at focusing on the topic of a page more than just raw keyword matching.

Research on understanding topics has been ongoing for many years. An example of recent research is: Improving topic clustering on search queries… (PDF)

“Uncovering common themes from a large number of unorganized search queries is a primary step to mine insights about aggregated user interests. Common topic modeling techniques for document modeling often face sparsity problems with search query data as these are much shorter than documents. We present two novel techniques that can discover semantically meaningful topics in search queries…”

There are many more research papers and patents. I just wanted to point out that what seems like a minor change might be somewhat meaningful in terms of how we think of web pages.

YMYL is Rewritten

Almost the entirety of section 2.3 that deals with YMYL issues has been rewritten. Previously shopping, financial and medical topics were in the top three of that section.

The top topics are:

News and Current Events

Civics, Government and Law

Those are followed by:



Health and Safety

Groups of People (new)

Other (newly revised)

Notable is that not only has the Medical section been demoted from third to fifth place, but it’s been renamed from Medical Information Pages to Health and Safety.

Brand New YMYL Content

These are the two brand new News and Civics sections:

“News and current events: news about important topics such as international events, business, politics, science, technology, etc. Keep in mind that not all news articles are necessarily considered YMYL (e.g., sports, entertainment, and everyday lifestyle topics are generally not YMYL). Please use your judgment and knowledge of your locale.

Civics, government, and law: information important to maintaining an informed citizenry, such as information about voting, government agencies, public institutions, social services, and legal issues (e.g., divorce, child custody, adoption, creating a will, etc.).”

These are the newly revised sections, including the new Health and Safety section:

Shopping: information about or services related to research or purchase of goods/services, particularly webpages that allow people to make purchases online.

Groups of people: information about or claims related to groups of people, including but not limited to those grouped on the basis of race or ethnic origin, religion, disability, age, nationality, veteran status, sexual orientation, gender or gender identity.

Other: there are many other topics related to big decisions or important aspects of people’s lives which thus may be considered YMYL, such as fitness and nutrition, housing information, choosing a college, finding a job, etc. Please use your judgment.”

Fitness, Nutrition, College Search and Job Search

It’s also notable that the “Other” topic has been expanded. It now includes additional topics.

Old Version:

“Other: there are many other topics that you may consider YMYL, such as child adoption, car safety information, etc. Please use your judgment.”

New Version:

“Other: there are many other topics related to big decisions or important aspects of people’s lives which thus may be considered YMYL, such as fitness and nutrition, housing information, choosing a college, finding a job, etc. Please use your judgment.”

Fitness and Nutrition, college search and job search are big money affiliate topics. Will they receive scrutiny and emphasis in a future round of core algorithm updates?

It may be reasonable to assume that the quality raters hadn’t been focusing enough on these topics. The fact that Google mentions them points to the likelihood that these are areas Google is giving special focus on as topics that are YMYL.

And what is housing information? Is that real estate, home loans and/or home improvement? Home loans and home improvement are big money affiliate niches.

The topic of housing information feels somewhat vague. It will be interesting to see if housing related niches are impacted in upcoming broad core updates.

Section 2.4.1 Identifying the Main Content (MC)

This section was updated to include new guidance on News and shopping pages.

This the new content:

“News website homepage: the purpose is to inform users about recent or important events. (MC – News Homepage)

News article page: the purpose is to communicate information about an event or news topic. (MC – News Article)

Store product page: the purpose is to sell or give information about the product.

● Content behind the Reviews, Shipping, and Safety Information tabs are considered to be part of the MC. (MC – Shopping Page)”

Section 2.5.2 Author Information

This section is about finding out who is responsible for a website and who created the page content. This section remains intact except for one addition.

Websites are usually very clear about who created the content on the page. There are many reasons for this:

This is the new section that was added:

added by other users.”

Is Google Reviewing Guest Articles and UGC?

This change to the author guidelines relates to news magazines but also any site that accepts guest articles or user generated question and answers.

Does this mean that Google is reviewing guest posts and authors?

Gary Illyes recently called out Quora and their SEO team via Twitter.

Quora is a site that publishes user generated content. Some speculated it had to do with user generated spam links.

Others tweeted that it may have to do with thin content.

We don’t know for certain but it’s interesting that the latest version of the QRG has a new section that addresses the authors of user generated content. This could be indicative that Google may be focusing on identifying low quality sites and excluding them.

Section 5.1 Very High Quality Main Content

This section has been expanded. The new content again addresses the uniqueness and originality. Again, it has a new focus on News sites, but it’s not limited to news sites. The standards of high excellence applies to all sites but in particular to YMYL sites.

Interestingly the new section goes beyond the quality of the text content. It also encourages the quality raters to judge the quality and originality of the artistic content, such as images, photography and videos.

I have long been encouraging site audit clients to review their images so that they not only download quickly but also that they express something about the content, that the images are relevant and original.

Here is the new content:

While what constitutes original content may be very different depending on the type of website, here are some examples:

and should meet professional journalistic standards.

● For artistic content (videos, images, photography, writing, etc.): very high quality MC is unique and original content created by highly skilled and talented artists or content creators. Such artistic content requires a high degree of skill/talent, time, and effort. If the artistic content is related to a YMYL topic (e.g., artistic content with the purpose of informing or swaying opinion about YMYL topics), YMYL standards should apply.

● For informational content: very high quality MC is original, accurate, comprehensive, clearly communicated, professionally presented, and should reflect expert consensus as appropriate. Expectations for different types of information may vary. For example, scientific papers have a different set of standards than information about a hobby such as stamp collecting. However, all types of very high quality informational content share common attributes of accuracy, comprehensiveness, and clear communication, in addition to meeting standards appropriate to the topic or field.”

Section 5.2.3 Very Positive Reputation

This section also received an update. This part is about reputation.

This is the new content:

Website reputation has long been a part of the Quality Raters Guidelines. I don’t think this means you should go out and join associations and seek testimonials.

But, publishers creating YMYL information should ideally be expert and have a good reputation. So this is one of those take due notice thereof and govern yourself accordingly type of situations.

Is Google researching reputation? I’ll have more on that in a future article.

Something I’ve long encouraged clients to do is something I call non-link link building. It’s a type of outreach designed to reinforce the expert quality of a website or website author. So many web publishers focus on links and pass up excellent (non-link) opportunities that demonstrate expertise and excellence.

Takeaways: More to Come?

There are significant changes this time around.

Higher standards for non-text signals of quality like images and videos

Download the latest QRG here (PDF)

What Is The Role Of Documentation In Quality Management?

Documentation is stressed as an integral part of quality management in this article. When it comes to quality control, consistency in processes and procedures, as well as thorough documentation, play crucial roles. Documents such as policies, procedures, work instructions, quality plans, and records are discussed in this article as they pertain to quality management.

The significance of document control in quality management and its influence on the whole process is also emphasized, as are the benefits that accrue from using it to meet both regulatory and industry standards. The importance of writing down lessons learned and taking corrective and preventative measures is also covered. Additionally, the article discusses training and communication strategies for successful documentation, as well as the challenges and solutions for effective documentation in quality management.

Importance of documentation in quality management

When it comes to Quality Management, consistency in processes and procedures relies heavily on documentation. In order to maintain a high level of quality control, Quality Management systems rely heavily on documentation.

An ISO study found that improper documentation was the leading cause of non-conformities discovered during quality audits. Inadequate documentation was found to be the cause of 15% of all non-conformities in the study.

In addition to lowering the likelihood of defects and rework, thorough documentation can aid in the speedy detection and correction of quality issues. It also helps make sure workers have all the information they need to do their jobs effectively and efficiently.

Types of documents used in quality management

Quality Management relies on a variety of documents to guarantee that final outputs are up to par. Organizations can use these documents to ensure uniformity, monitor progress, and share relevant data with key constituents.

Policies, procedures, work instructions, quality plans, and records are some of the most important documents in Quality Management. Quality management policies determine the overall course and objectives, while quality management procedures spell out the specific actions to be taken. Quality plans describe how quality will be ensured throughout a project or process, while work instructions provide more detailed guidance on how tasks should be carried out. Records, like inspection reports and test results, are kept to document proof of adherence to quality standards.

Companies can use these records to make sure their goods and services are up to par, and to spot any flaws in their processes. Customers, regulators, and employees are just some of the audiences who benefit from receiving quality-related information, and they can all be reached through well-documented processes.

Document control and its impact on quality management

The term “document control” is used to describe the method used to keep track of documents from creation to destruction. To guarantee that all of an organisation’s processes and procedures are documented correctly and consistently, document control is an essential part of quality management.

There will be fewer opportunities for mistakes or discrepancies to creep in when everyone in the company has access to the same, up-to-date data thanks to diligent document control. It also aids in documenting and communicating any changes to processes or procedures so that everyone is aware of the modifications and can adjust their work accordingly.

In addition to aiding in compliance with regulatory requirements and industry standards, and making both internal and external audits easier, good document control is essential. Organizations can gain the trust of customers, suppliers, and regulatory agencies by showing that they are following standard operating procedures and protocols through meticulous record-keeping.

Standard operating procedures (SOPs) and their role in quality management

Important documents that detail the actions to take in order to accomplish a given task are called standard operating procedures (SOPs). Standard Operating Procedures (SOPs) play a vital role in Quality Management by guaranteeing that products or services are consistently provided that meet or exceed customer expectations.

Standard operating procedures (SOPs) are used to record the usual way of doing something, down to the tools, workers, and supplies required. This ensures that all parties follow the same procedures and produce the same results.

Standard operating procedures help businesses guarantee their procedures are consistent, repeatable, and scalable. This is significant because it facilitates the reduction of mistakes and variability, both of which enhance the quality of the end product or service. Standard operating procedures (SOPs) can also be used to spot trouble spots and uncover ways to boost effectiveness and productivity in a given process.

Documenting corrective and preventive actions (CAPAs)

Quality management relies heavily on the documentation of corrective and preventive actions (CAPAs). When problems or issues arise with a company’s products or services, corrective and preventive actions (CAPAs) are implemented.

CAPA documentation includes noting the nature of the issue, the steps taken to fix it, and any precautions taken to stop it from happening again. Having a written record of the steps taken by the company to enhance the quality of its goods or services is crucial.

The ability to monitor and assess the quality management processes, pinpoint problem areas, and implement solutions to prevent recurrences depends on the thorough documentation of Corrective and Preventive Actions (CAPAs). It also aids businesses in keeping track of their quality management efforts and meeting applicable regulations.

Challenges and solutions for effective documentation in quality management

The role of documentation in quality management cannot be overstated. Maintaining processes, procedures, and quality standards is easier with well-documented processes. Quality management documentation presents its own unique set of challenges, however, and these must be overcome.

Making sure the documentation is complete and correct is a major obstacle. This may take a considerable amount of time and energy from the documentation team. Establishing who is responsible for what and how often documentation is reviewed and updated is crucial.

Another difficulty is making sure all of the quality management documentation is readily available and understandable. This includes using straightforward language and making sure the document is straightforward to use.

Organizations can implement solutions such as software tools to automate documentation processes, training for employees on best practices for documentation, and a streamlined review and approval process for all documentation to meet these challenges.

Training and communication strategies for successful documentation in quality management

Quality management relies heavily on complete and accurate documentation. It aids in the efficient and accurate documentation of processes and procedures. It’s more challenging to find and fix problems without complete documentation.

Strategies for both training and communication must be implemented for quality management documentation to be successful. Employees can learn the value of documentation and how to do it properly through training. Template, software, and doc management system training fall under this category.

The ability to effectively communicate is also crucial. A successful documentation process relies on clear and concise communication between all parties involved. One way to accomplish this is by encouraging and facilitating honest and open communication amongst team members, as well as setting clear expectations for documentation.

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