Work Breakdown Structure Pros And Cons

Work Breakdown Structure Pros And Cons

Work Breakdown Structure (WBS) pros and cons concern the benefits and disadvantages of the WBS method of project management. WBS is a project management plan that uses a tightly structured and segmented plan. This type of project management comes with many pros but also some cons to weigh before adopting it.

What Is A WBS?

The Work Breakdown Structure (WBS) method of project management uses a template that displays the entire project and all the workers involved, using manageable units. The WBS provides a helpful template for project managers where all the steps of a project are defined and incrementally accomplished. WBS demands that 100% of a project is accounted for when broken down into manageable parts. Each part also produces 100% of the devoted portion of the project. 

Work Breakdown Structure Pros

· You can plan incremental project accomplishments. Projects are more manageable when done in portions at a time

· Determine milestone accomplishments for the larger project. The parts of a project can be measured, and assurance can be felt

· Plan the number of days for the milestone accomplishments. The segments of a project can be more easily predicted when small

· Increase productivity. When many team members are assigned small parts to deliver, much can be accomplished

· Increase transparency. The simpler the deliverables, the easier to be accountable and monitored

· Strengthen accountability. Project managers can have a better sense of the progress of the tasks and those accountable

Work Breakdown Structure Cons

· WBS uses steps that could encourage resentful micromanagement

· Requirements for a deliverable can be mistaken for the task itself

· The breakdown and listing of project tasks can be difficult to agree upon

· The WBS can become outdated during the actual process

· A large WBS project can be painstaking to develop

· Changes may be necessary due to project changes, which will require changes to the WBS

Rules To Make A Good WBS

· The whole project’s completion is the final goal. Individual tasks should not be repeated

· Tasks should be accomplished between 8 hours and 80 hours

· Each task is assigned an individual or select team

· Outcomes are the focus rather than intermediate actions

There are different formats of a WBS template. Some are:

· Tree format

· Outline structure

· Hierarchical structure

· Tabular structure

WBS Areas

· Tasks

· Costs

· Schedule

· Scope

· Function

· Responsibility


Business projects are never attempted at once as a whole piece. Good project managers need to be able to manage their projects by segment and increment. They need to break up projects into manageable units by assigning deliverable parts to responsible team members, and by scheduling durations and deadlines. Using a work breakdown structure (WBS) template will help project managers portion out the work visually in a structured manner. WBS helps to define the steps of the parts in the whole project, which helps in assigning responsibility, allocating resources, and monitoring schedules. WBS helps project managers to efficiently allocate their attention and energies.

The Influence Of AI On Content Marketing

The Influence Of AI On Content Marketing

In the digital age, for marketing to be effective it needs to include a content marketing strategy. Therefore, the way marketing approaches content strategy has evolved from the days of collecting demographic information to more sophisticated ways of engagement using technology. As marketing practices matured so did Artificial Intelligence (AI) to be more applicable to everyday business use cases. When AI was introduced to marketing, the result was a tremendous change in how businesses engage with customers.

The potential of AI can be seen across industries outside of marketing. Finance, healthcare, education, and telecommunications have been redefined by AI. Marketers across industries are leveraging in-depth analysis of customer data to gain actionable insights that drive business.

Automation In Marketing

The new marketing practices are capitalizing on automation to drive revenue. Businesses are learning how to create a content strategy, do SEO and social media management, and optimize email marketing with the power of AI. Such advancements provide better customer insights, more revenues, and better brand positioning.

AI Influencing Marketing Examples

1. Using Chatbots To Manage Customer Experience

 Part of the shift of consumer behavior online was the surge in people relying on chatbots for customer service. Gone are the days that customer service was done on the phone, the new way to connect with customers is online. Companies like Uber and Facebook utilize AI-powered chatbots to converse with users. AI capabilities make the interaction feel like the customer is talking with an actual human rather than a robot. The sophistication of chatbots has made customer service available 24/7 without human errors and inconsistencies. Industries such as banking use chatbots for basic customer service requests and pass customers along to live customer service agents for more complicated queries.

2. Improving Keywords

 Another advantage of AI-powered marketing is improved keyword intent for marketers. Google, as the leader in keyword marketing, is constantly working on improving its algorithm to enhance search results for people. By focusing on the information that people want to see, Google is focusing on user intent. Machine learning (ML), a subset of AI helps marketers identify the right keywords to find patterns in consumer behavior. Marketers rely on these data-driven decisions to segment customers better and personalize the marketing message.

3. Hyper Personalized Content

 One of the most significant benefits of using AI in marketing is the hyper-personalization of content. The customer journey has been redefined by personalization that is based on consumer behavior, interests, and interaction with different kinds of content. For marketers building a digital avatar of a customer is a powerful way to build brand loyalty, increase engagement, and drive conversions. Personalized content such as email marketing can cut through the noise on the internet that is competing for the customer’s attention and focus on what is important to the customer. AI is an iterative technology that figures out what works and what does not. As it learns customer preferences, it can customize messaging and automate content based on customer preferences.


Marketing is an industry dedicated to innovation given its sensitivity to customer demands. As AI became more ubiquitous in society, businesses across industries capitalized on its power. Specifically, marketing used AI-powered technology to develop a stickier content strategy. Applications such as chatbots, better keyword intent, and hyper-personalized content improve the customer experience and make marketing more powerful daily.

The Benefits of AI In Content Marketing

The Benefits of AI In Content Marketing

How AI Has Impacted Marketing

AI technology has been adopted by a diverse array of industries. From financial services to healthcare, service delivery and the customer experience are being redefined by automation. One industry that has seen a radical change in practices due to AI has been marketing. Marketing is highly sensitive to the customer experience and the customer journey. It makes sense that the introduction of AI would change traditional ways of doing marketing. Some of the most significant benefits have been seen in content marketing. What kind of content customers consume and how it is being delivered is in a state of transition. Due to automation, content has become more intimate with a hyper-personalization that has been taken out of the context of 1:1 communication onto the internet.

Benefits of AI In Content Marketing

1. Hyper-Personalization Of The Customer Message

Generic marketing campaigns and mass messaging are history. With so much noise on the internet, marketers have recognized that a generic message blasted out to thousands of people is ineffective at best and at worst intrusive. These days customers expect hyper-personalized content that is curated based on their interests, behavior, and content consumption. Here are some statistics that support this trend.

– 52% of consumers would switch to another company if they were not getting personalized communication

– 80% of marketers believe personalized messages are more effective than generic

– 72% of businesses prioritize making the customer experience better as one of their top objectives

2. Better Marketing Campaigns

 The effectiveness of a marketing campaign is based on engagement and conversions. AI can make those objectives more effortless for marketing. Rather than tailoring thousands of messages manually, AI can automate the process of personalization. In marketing, AI is iterative, with each marketing campaign it figures out what works and what does not. Just like

humans learn with experience, AI systems learn overtime to deliver a more effective marketing message.

3. Tailored Content Strategy

 AI-powered marketing helps businesses to provide the right content to the right audience at the right time. AI uncovers data-driven insights that lead to new opportunities in marketing. Now, marketers can analyze a limitless amount of data on different customer segments such as income levels, personal interest, social media platforms, etc. By slicing and dicing data, marketers can uncover new opportunities and angles to make messages stickier and more personalized. Other technology tools such as text mining can do sentiment analysis on social media to uncover customer behavior trends and moods underlying social media communication. This enables marketers to be more responsive to audiences and anticipate their needs.

The Future of AI In Marketing

In the future, AI-powered marketing will eliminate the need for manual processes in marketing. Such tasks as doing keyword research and organizing a content calendar, creating email marketing, and lead generation will no longer require a human touch.

The efficiency and productivity of AI will transform marketing practices. Also, using AI to uncover data-driven insights will lead to new opportunities. Marketers will be able to automate redundant and mundane activities and focus on higher value-added activities that will encourage innovation

How To Improve Readability

How To Improve Readability

What Is Readability

A writer’s best-kept secret is readability. Readability is the practice of effective communication. To use transparent communication that is accessible to the most general audience. Why does it work? Readability works well for a variety of subjects because it produces digestible content. In 2010, the average reader had an attention span of 12 seconds. By 2021, that attention span has shortened to 7 seconds.

Readability Example

Most people are busy and do not have the time or the patience to read long complex sentences. They want content that is clear and to the point. For example, if a reader is searching on “how to learn Chinese”, they can come across two different search results. One search result is: “The study of Chinese language is a complicated undertaking that takes into consideration the nuances of Chinese characters and how to read them. Chinese tautology has a rich cultural meaning underpinning the semantic context”. That sounds confusing and the reader must overly exert themselves to process the context. Another search result can be: “The Chinese language is a hard language for new students to study. Because of the Chinese culture and characters, there’s a lot to understanding it.” The message has clarity and brevity and gives the reader exactly what they were looking for.

How To Improve Readability

1. Use Plain Language

Plain language improves readability because it is concise and to the point. It does not rely on jargon or complex sentence structure to communicate. Federal agencies are mandated to use plain language as well as some businesses prefer to use it. Research has found that the average American reads at an 8th-grade level. Using plain language ensures that the content is accessible to the widest audience.

2. One Idea Per Sentence, One Idea Per Paragraph

 Short sentences and focused paragraphs increase readability. A good rule to follow is to use one idea per sentence and one idea per paragraph. It is a good practice to start the paragraph with the main idea and follow

with the details. This ensures that the right message is communicated to the reader, and they do not get lost in long-winded sentences.

3. Leave Out The Adjectives And Adverbs

 Adjectives and adverbs have a place when it comes to expository and creative writing. However, for writing that relies on plain language and focuses on readability, a writer should omit them. Adjectives and adverbs provide nuance and descriptions to verbs and nouns in some writing formats. But readability is about precision in communication which leaves little room for interpretation. A writer can increase readability by sticking to an active voice and omitting adjectives and adverbs.

4. Use A Readability Tool

To improve readability, a writer must have good writing habits. But sometimes even professional writers need extra help. By using a readability tool, such as offered by the Content Analytics Platform (CAP) by Scion Analytics, a writer can automatically assess and improve the readability level of content.


Readability makes good writing great and accessible to the reader. A writer should always strive to improve readability for a better experience for the reader.

Benefits Of Plain Language

Benefits Of Plain Language

In 2010, when President Obama signed into federal law the Plain Writing Act of 2010 it was to mandate that federal executive agency use plain language as a standard of communication. The premise of this law is that the American public deserves plain language communication from its government. As plain language became more ubiquitous across the government and business sectors, the tangible and intangible benefits of plain language and readability become more emphasized.

6 Reasons To Use Plain Language

1. Plain Language Is Efficient

These days people have short attention spans. A decade ago, marketers measured that the average reader’s attention span was 12 seconds. Now, the average reader’s attention span is a mere 7 seconds. That is about as long as it takes to read these two sentences. A writer needs to capture the attention of the audience and that is best done with plain language. That is because it gets your message across in the most efficient way possible.

2. Plain Language Is Clear

For businesses, a message is only effective when it is understood. Plain language is based on an 8th-grade reading level ensuring clarity and simplicity in communication. Did you know that the average American reads at an 8th-grade level? For messaging to be inclusive and accessible, it needs to be clear to the general audience.

3. Plain Language Is Easy To Understand

This is especially important when it comes to writing an instructional manual. Plain language is easy to understand, and instructions written in plain language are easy to follow. For a business, this reduces complaints, inquiries, and confusion from customers that are unable to follow complicated instructions.

4. Plain Language Is Better For Marketing

When it comes to user experience on the website and digital marketing collateral such as blog posts, web pages, and articles, plain language offers businesses more of a competitive advantage. Websites written in plain language have less of a bounce rate. Marketing collateral written in plain language drives revenues and builds customer loyalty as it is more

appealing to a wider audience. Also, consider that marketing promotions for a complicated product in the tech space tend to perform better when written in plain language as opposed to technical jargon.

5. Plain language Creates A Positive Image

Using plain language creates a reputation for your business as one that puts people first. Because when you have clear communication, it comes across as having consideration for your audience. Making sure that your audience feels acknowledged by transparent and honest communication. This promotes a positive reputation for the brand as well as customer loyalty. The internet is full of noise and competing for marketing messages. By using plain language, a business stands out from the crowd and tailors its message to the customer.

6. Plain Language Is Universal

While plain language became law for government agencies, it was also used across different industries to improve communication. Businesses such as banks, insurance companies, law firms, legal services, and IT companies use plain language to communicate with customers. This saves time and money with every message that the business puts out.


The benefits of plain language are tremendous for businesses that are looking to have transparent and efficient communication with customers.

What Is Plain Language Writing?

What Is Plain Language Writing?

When the Plain Writing Act of 2010 was signed into law by President Obama the whole country took notice of plain language writing. The law mandated that federal agencies use plain language in all their communication. This was done to create transparency in communication between the government and Americans. Several corporations and business entities followed suit after the government under the premise that plain language communication is transparent and honest. As human beings, we feel more comfortable and connected with communication that is simple and easy to understand. It also improves readability. Any deviance from simple language becomes open for confusion and misinterpretation.

So, what is plain language writing? How can a writer become better at using plain language?

6 Fundamental Rules For Plain Language Writing

1. Write For Your Audience

One of the cardinal rules of plain language writing is to write for your audience. The needs of the audience and the reading level of the audience must be considered. Research has found that the average American reads at an 8th-grade level. Keeping those statistics in mind do away with convoluted language and jargon and focus on what matters most- the reader.

2. Make Your Major Point First Before Going Into Details

Plain language writing is digestible. The writer should emphasize the main point first in the paragraph before going into supporting details. This focuses the reader and gets the point across without the reader losing interest.

3. Limit One Paragraph To One Point

Another fundamental rule of plain language writing is brevity. Plain language writing is known for using short sentences. It eliminates the grammatical mistakes of using long-winded or run-on sentences and keeps paragraphs limited to one idea. By keeping paragraphs focused, it organizes and structures the writing for maximum clarity and gets the message across to the reader.

4. Use Easy-To-Understand Words

An important rule of plain language writing is words that are easy to understand. The writer must do away

with complex words and jargon. If the writer must use technical words, they should be defined on the first reference. This is to ensure that the reader comprehends every word and there is no room for misinterpretation.

5. Use Active Voice

 Did you know there is a similarity between plain language writing and Ernest Hemingway? They both use active voice much more than passive voice in writing. Research has found that most of the bestselling novels of all time are written in active voice. Why? Because active voice provides clarity to the reader as to who is doing the action. Passive voice is less grammatically correct and harder to digest for the reader.

6. Write The Facts, Omit Everything Else

Plain language writing is journalistic in the sense that is a matter of fact. It sticks to the point, and it eliminates unneeded words and hyperbole to deliver the most concise message to the reader.

From government entities to businesses across industries, plain language writing is the foundation of effective communication.

What Is Readability?

What Is Readability?

Readability is a practice that determines how easy or difficult it is for a reader to understand a piece of text. There are different methods and equations for measuring readability which is comprised of different elements of writing. For example, word choice or syntax can influence readability. In marketing copy, a business that chooses the word “nonchalant” instead of “easygoing” is a business that is selecting to use words with poorer readability. For customers, these words are unfamiliar or complex leading to confusing and open-ended interpretations of the text. A business focused on effective communication is committed to sending out the right message.

Great business practices are built on effective communication. It can be the deciding factor between a business that performs well and engages with customers versus a business that is lagging in sales. But what makes communication effective? How can marketing and sales collateral inform the customer as well as attract attention? How can a business improve communication tactics? As businesses become more sophisticated so does how the business communicates internally and externally. This is in part due to the concept of readability.

On a general level, other factors go into a text’s readability such as sentence length, structure, and average syllables per word. Why is readability important? For a business, a customer needs to easily process information without straining to understand the text. Marketing copy that is full of jargon and complex ideas might make a customer lose interest in the company, bounce from the website, and not make a purchase.

One of the formulas that are used in readability is the Flesch Reading Ease Formula. It is used to assess the grade level of the reader. The Flesch Reading Ease Formula has become the standard used by many US government agencies such as the US Department of Defense.

The Specific Mathematical Formula Is For Flesch Reading Ease Formula:

RE = 206.835 – (1.015 x ASL) – (84.6 x ASW)

RE = Readability Ease

ASL = Average Sentence Length (i.e., the number of words divided by the number of sentences)

ASW = Average number of syllables per word (i.e., the number of syllables divided by the number of words)

Readability Benefits

Reader Engagement

To fully understand the benefits of readability, the accessibility of text needs to be put into the context of the Digital Age. The pioneers of readability, Rudolf Flesch and Robert Gunning, could not foresee the tremendous volume of information that would flood the Internet. In its nascence, the Internet was a convenient way to share information that evolved into a content machine. These days, over four billion people are online, and businesses are aware that “content is king” contributing to an even greater demand for sticky content.

The sheer volume of information has probably shortened the attention span of readers. Research has found that in 2000 the average reader attention span was 12 seconds. In 2021, the average reader’s attention span is 7 seconds. That is a short time frame for the writer to grab attention and convince the reader to continue reading.

The pressure for content to be engaging keeps mounting as businesses undergo digital transformations. The benefit of a readability score is that it assesses how easy the text is to read. The easier the text is to read the more likely it will hold the reader’s attention. Readability provides quantifiable measurements for a text that can be used to set targets and metrics as part of a content strategy.

Plain Language

Another benefit of readability is its use of plain language guidelines. Plain language is a movement towards simplifying the content. It was started to make complex legal documents easier to read and is now mandated by the government and used by businesses around the world. In 2010, President Obama passed the Plain Writing Act of 2010 requiring federal executive agencies to adhere to plain language guidelines.

Why is plain language important? The basis of the law was the consideration that the average US adult reads at an eighth-grade level. This statistic shows the necessity of using inclusive language in government communication and beyond. Because plain and easy-to-understand language makes complicated topics more accessible to theaverage reader. It also improves the website user experience by addressing the website audience like a friend without formalities, in plain language.

What Is A Readability Score

For businesses, a variety of readability tools are available to help with messaging. By utilizing a readability tool, a business can generate a readability score. A readability score can signify to a business what level of education someone needs to read a piece of text easily. For example, using the Flesch-Kincaid readability score of 8 is approximately equivalent to a reading level of US grade 8. An 8th-grade reading level is appropriate to ages 13-14 and the writer must strike a balance between being informative yet accessible.

How To Improve A Readability Score

In business, the clarity and effectiveness of the message are important for successful communication. Once a business has come to rely on a readability tool, it can find ways to improve a readability score. The following are proven tactics for better readability:

1. Use Shorter Sentences

There are different readability formulas but one common denominator in all of them is: sentence length. By shortening a sentence, a writer can ensure a better readability score. For example, the sentence: “The friends had gathered for dinner under the candlelight with blue china patterns to be served orange duck with rice.” Can be shortened to: “The friends had gathered for dinner. It was served on blue china patterns. They enjoyed an orange duck with rice under the candlelight.” By breaking up sentence length, the reader can digest the point of each sentence easier. It also makes it easier to scan the text.

2. Minimize The Number Of Long Words

 Another component on which readability is score is word length. Some tests, such as the Flesch-Kincaid, use the number of syllables to calculate word length. Other tests, such as Coleman-Liau, calculate word length based on letter count. For example, using the word “prohibited” instead of “banned” will decrease readability. Using shorter, simpler words is a good tactic to use when writing for the public. However, writing for a legal or the financial industry, a writer may have to use more complicated terminology that is appropriate for that audience.

3. Write For Your Audience

One of the foremost rules of plain language and readability is to be inclusive and transparent with your audience. A piece of writing is so much more effective when it takes into consideration the language particular to the audience it is addressing. A good example is a legal industry which is known for having legal terminology and jargon associated with its communication. If a writer chooses to use terminology, he should offer definitions in the text. With the readability, the goal in mind should be not to ostracize your reader. Considering that the average American reads at an 8th-grade level, that is the baseline most writers need to be mindful about when writing.

4. Use Punctuation

The goal of punctuation is to help your reader understand what is being said. Run-on sentences, fragments, inappropriate or misplaced punctuation lower readability scores. If the use of punctuation is an issue, writers should consult grammar checks or brush up on punctuation rules to make sure it is used properly.

5. Stick To A Structure

For the best readability, it is important to have text that is grammatically correct, clear, and concise. Another factor to consider is how to structure the text. A writer should think about the story he wants to tell. What are the key points? Does the reader need to know more information before engaging with the key takeaways? Having an outline for the text can help the writer prioritize the focus of the article. It also helps the reader follow the story and internalize the message, things that are conducive to readability.

Industries That Use Readability


Readability as a tool and as a practice has meaningful application in many industries. For example, in education teachers use readability to decide whether a particular text is suitable in a curriculum for students. If “To Kill a Mockingbird”, the English literature classic is appropriate for a 9th-grade reading level, it may therefore be too simplistic for an 11th grade English class.


In the business sector, many types of businesses utilize readability to simplify documents, so they are easier to read. This is true both for internal and external communications. For example, a tech company may run readability on an instructional manual it composed for its users. Furthermore, if that tech company uses the Content Analytics Platform (CAP) from Scion Analytics it can take advantage of creating dictionaries.

By using dictionaries in readability, a business can specific words that it wants to avoid in writing, jargon, inappropriate phrases, cliches, and legal risk words. The flexibility and customization of a dictionary help businesses ensure that their message is easy to communicate to the intended audience.


Finally, readability is widely used by marketing departments to assess how well readers engage with digital marketing materials. Running readability of online texts such as blog posts, web pages, and articles can help professionals establish metrics for text. Using metrics, these professionals can improve the text to get better business results.


Readability is a practice and a tool that can help businesses communicate more effectively. Its adherence to plain language and reliance on short sentences, simple words, and easy to digest language makes it indispensable in engaging the reader with the right message.

How Sentiment Analysis Can Analyze Mood

How Sentiment Analysis Can Analyze Mood

COVID-19 And Sentiment Analysis 

2020 was a year that changed history with the spread of the COVID-19 pandemic. As the world shut down and people congregated online, social media channels lit up with collective moods and feelings about the pandemic. Historically, such data would have been subjective however with technological advances in text mining, it was not. Sentiment analysis has evolved in recent years with more uses in the business and public sector.

For the past 50 years, researchers have been working on technology that would enable computers to analyze the emotional tone of words. Such findings have had many real-world applications. For example, a large collection of tweets can be analyzed for collective mood something that would be impossible given human limitations. Now, music researchers can quantify how much a minor chord is sadder than a major chord. And businesses are tapping into websites like Yelp to understand customer behavior better. By assessing a vast number of reviews businesses can align brands with what customers feel and want. Sentiment analysis has medical applications as well with popular platforms like Facebook being able to recognize if users are depressed or suicidal.

When algorithms can gauge mood from what people write online it opens a new world of data and opportunity for society.

How Sentiment Analysis Works

How does sentiment analysis work? Advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) have mastered analyzing the context of language. However, AI has yet to understand the nuances of language. It was a significant win for science that artificial cognition can decipher the emotions behind words without understanding them.

In the beginning, sentiment analysis used word-counting for analysis. It added up the number of positive words and subtracted the negative words. Some content analytics platforms use weighted analysis to determine the mood of the content. For example, the word “atrocious” is given a higher weight than the word “bad”. Humans assign weights to words by creating

customized dictionaries within the software. In the sphere of sentiment analysis, these dictionaries are called lexicons.

However, human work is prone to error. Word-counting is fundamentally effective but ultimately unreliable. There are too many variables in language for word-counting to be precise. A more sophisticated approach to sentiment analysis uses Machine Learning (ML) algorithms to teach computers to recognize patterns and relationships between words. For example, a computer can recognize that “computer” and “mouse” are related and that it is a different type of “mouse” than the rodent variety.

Sentiment Analysis And Mental Health

Computer scientists are working on further advancements in sentiment analysis which has a foundation in psychology. As early as the 1960s, psychologists in leading universities were designing programs that were giving insights into individual’s psychological worlds. For example, computers were able to pick up on specific patterns for patients diagnosed with depression. Depressed patients were apt to use pronouns more often such as “I” and “me” as well as negative affect words or words related to death. These insights have empowered social media platforms to be proactive about user’s mental health. For example, Facebook has an algorithm that can identify suicidal users and if necessary, send the user helpline numbers.

With roots in psychology, sentiment analysis can give tremendous insight into the collective mood on social media. This has implications in the lives of the consumer and the individual as businesses become better at understanding consumer behavior. Also, individuals become engaged in social media platforms that can accurately decipher their mental health. It represents a shift in how much influence collective mood has on society.

Text Mining vs. Natural Language Processing

Text Mining vs. Natural Language Processing

In the 21st century data has become as much of a commodity as oil was in the 20th century. As businesses become more reliant on data-driven decisions, the importance of utilizing the right type of data methods to process the right information for businesses becomes paramount. Access to unstructured data has emphasized the need to leverage large data sets. Businesses are no longer able to process large sets of textual data manually and have come to rely on automation. To learn how to best extract value from data, it is important to understand the difference in benefits of Natural Language Processing (NLP) versus text mining.

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Using Natural Language Generation (NLG) and Natural Language Understanding (NLU), NLP can process different types of speech including misspellings. It does that by utilizing Machine Learning (ML), a system that relies on special types of databases to extract language.

Text mining is a subtype of data mining. It focuses on data mining and ML methods as it relates to textual information. More specifically, it extracts the information from text files. A wide array of text files can be used in text mining including structured and unstructured data in emails, social media posts, and web content. Text mining works for qualitative data analysis and it helps distinguish between different types of data.

Differences Between Text Mining v. Natural Language Processing

1. Purpose

NLP and text mining differ in the goal for which they are used. 

NLP is used to understand human language by analyzing text, speech, or grammatical syntax. 

Text mining is used to extract information from unstructured and structured content. It focuses on structure rather than the meaning of content.

2. Tools

Businesses use different tools when working with NLP and text mining. 

NLP uses businesses leverage advanced ML models, artificial neural networks (ANN), and tools like NLTK in Python. 

Text mining also uses ML models along with statistical models and text processing languages like Perl. A business must invest in the tools necessary for specific data processing to be successful.

3. Capability

NLP is geared towards mimicking natural human communication. It uses text and speech as input to extract grammatical structure and syntax meaning. 

Text Mining is geared towards analyzing qualitative data.

4. Results

 The outcomes for using different data methods differ. 

NLP can be used to extract grammatical structure as well as the sentiment from language. 

Text mining uses statistical indicators like frequency of words, patterns of words, and correlation within words to explain the text.

Differences Between Text Mining v. Natural Language Examples

The major difference between NLP and text mining is in potential application.

Natural Language Processing

In everyday use, NLP can be found in search engines that provide correct answers when users enter queries. It can also be seen in intelligent chatbots that are integrated into communication channels and websites to provide customer service. Another application of NLP is spellchecking apps, tools like Grammarly, have huge databases of words, grammatical rules, and combinations that are powered by NLP.

Text Mining

Text mining is used for SEO and website marketing purposes. It can guide contextual advertising and target promotions. Another application of text mining is analyzing data from website content and social media platforms.


While NLP and text mining have core differences in application, they both provide advantages when used to analyze data for a business. These data methods save time and resources, are more efficient than human intelligence, and track information flow. Gathering insights from structured and unstructured data, NLP and text mining provide valuable insights that move businesses into the future.

Natural Language Processing (NLP) Consulting

Natural Language Processing (NLP) Consulting

What Is Natural Language Processing

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

NLP In Business

As technology advances and humans leverage data for everyday life, the field of NLP becomes more sophisticated. NLP is used to improve the quality of life for humans. It is being integrated into daily lives with intelligent virtual assistants such as Siri and Alexa. These virtual assistants are responsive to human needs and become more intelligent with every interaction with a human. Soon, intelligent automation will enhance human intelligence enabling humans to pursue higher value-added projects requiring ingenuity and creativity.

Businesses can tap into data they did not even know they had to make actionable data-driven decisions. Enterprises benefit from insights, analytics, and decision-making from analyzing 80-90% of hidden data. Unstructured data has many formats from text, voice, audio, images, and videos that can be processed to yield new marketing, sales, and operations initiatives for businesses. By extracting value from unstructured data, a business learns more about its customers and their preferences. For example, a lot of social media channels are full of unstructured data where NLP can be applied to do sentiment analysis. Conducting sentiment analysis on social media, a business gains insights into customer mood and feelings over a product or service. Now, the decisions the business makes surrounding that product or service are not based on guesswork but data.

NLP Consulting

At Scion Analytics, we focus on delivering AI & NLP consulting services with the Content Analytics Platform (CAP). The CAP has NLP capabilities to liberate value from unstructured data that automate processes, scale business, and leverage data to transform the enterprise. By using the CAP, the enterprise can automatically extract value out of content to reduce the sales cycle. It can also use NLP technology to understand the contextual relationship between large data sets which enable the enterprise to execute on opportunities. By leveraging real-time analytics, the enterprise can capitalize on data-driven decisions much faster and gain a competitive advantage. The CAP uses AI & NLP technologies to analyze and process unstructured data by configuring customized microservices for clients. Scion Analytics use cases have found that each time a client uses 1 microservice for every use it saves the client 8 hours of time. For example, a microservice designed for business requirements could parse business documents for predefined keywords and create a dictionary. Therefore, a process that would take hours manually for an employee to go through business documents is automated in minutes.

For the enterprise, the benefits of using a content analytics platform are saving time, increasing revenues, and efficiency. It makes sense that a business that is shaping the future would embrace NLP technology and leverage NLP consulting services to succeed.