Your cart is currently empty!

Google Analytics 4 (“GA4”) is a tracking and measurement tools provided by Google freely to the public. In this article, we will mainly focus on how to assign access rights to others who manage the GA4 on behalf of you.
Instead of sharing the Root Access to others, i.e. the login and password of the Gmail account which you used to registered for the GA4 account, you can instead only share different levels of Access Rights of GA4 account.
If you want to share the Read-Only Access Rights instead of Admin Access Rights which can configure anything inside the GA4 account, you should consider the permission level of the Access Rights
Whenever a staff is quit, if you don’t want to change all the passwords the he/she possessed for the GA4 account, you can in the beginning simply assign him/her different Permission level of Access Rights , instead of giving him/her the Root Access of the GA4 account.
analytics.google.com
> <<ASSIGNOR_GMAIL_ADDRESS>>
Admin
> Account
> Account access management
> Add users
Email Addresses
= <<ASSIGNEE_GMAIL_ADDRESS>>
Notify new users by email
= ☑
Direct roles and data restrictions
Standard roles = Administrator
(Full control of account)1Add
gmail.com
Inbox
from
= [email protected]Email Title
= You have been granted access to a Google Analytics accountanalytics.google.com
Pivot Table is a feature which you can usually see in the Google Spreadsheet and Microsoft Excel. While alots of people can address it existence, seldom people can explain what it is and what it’s difference between the normal table. In this article, we are going to break it down and elaborate it in detail with illustration.
It is a good to start explaining what is Pivot Table by a normal Table.
Month | Sales Turnover (USD$) | Salesperson |
---|---|---|
Jan | 5,000 | Anna |
Feb | 2,000 | Anna |
Feb | 3,000 | Angela |
Mar | 1,000 | Ann |
You will not feel strange about the tabular data pattern which a normal table brings to you. In this Normal Sales Turnover Table, you can easily get 5 piece of information because there 5 records inside the table.
The information, i.e. the 5 records, are in atomic level which means that they are already deintegrated into smallest granularity that if you further break down the record, say breaking down from information per Record to information per Cell, you no longer can get the useful information because it will be meaningless if you only know the value “Anna” without knowing what Month and how much Sales Turnover she get. In summary, a Record is the atomic level of a normal Table.
This “Tree View” of a normal Table is good for recording due to the fact that each record can be input separately without affecting the meaning and usefulness of each other. For example, it is totally fine that there is missing information of March Sales Turnover from Joan if you are only interested in the February result of Anna.
However, it is not good for “Forest View” when you want to see the consolidated result of the Sales Turnover Report. How about if you want to know the Total Sales Turnover Amount from Anna? Of course you can do the calculation by yourself each time you want to know the Total amount. But what if there are 10 million records?
Besides, the normal Table only tells you what you can see , it does not tell you what you cannot see. For example, what if I want to know the Sales Turnover of Joan in Jan ? “No Value (or Null)” is also a valuable information that you want to know.
Now, let’s see how we turn the Normal Table into a Pivot Table.
Jan | Feb | Mar | Total by Salesperson | |
Angela | 3,000 | 3,000 | ||
Ann | 1,000 | 1,000 | ||
Anna | 5,000 | 2,000 | 7,000 | |
Total by Month | 5,000 | 5,000 | 1,000 |
In the Pivot Table , now not only you can see the consolidated total amount in terms of Month or Salesperson, but also can easily address the Cell with missing value.
It’s glad to know, but still not impressive enough.
Because in the previous example, it we only use 2 parameters (i.e. Month and Salesperson) to co-ordinate a value (i.e. Sales Turnover Amount). How about if you want to add an additional parameter , for example, Product ?
Month | Sales Turnover (USD$) | Salesperson | Product |
---|---|---|---|
Jan | 5,000 | Anna | Apple |
Feb | 2,000 | Anna | Banana |
Feb | 3,000 | Angela | Cherry |
Mar | 1,000 | Ann | Dragonfruit |
While there is no big different in terms of the look and feel of the table except the additional 1 column added in the very right of the Normal Table, you can compare it with the new Pivot Table which added the Product parameter in below:
Salesperson | Product | Jan | Feb | Mar |
Angela | Cherry | 3,000 | ||
Ann | Dragonfruit | 1,000 | ||
Anna | Apple | 5,000 | ||
Banana | 2,000 |
Same as the Normal Table, an additional column Product is added immediately after the column Salesperson. And most important is that the Value Anna is shown only 1 time instead of 2 times (in Normal Table) due to the fact that the Pivot Table helps to “consolidate” the data to make it minimal.
While in the previous Pivot Table example there is the Total Amount , I skipped this information because it is not the focus on the essense of the Pivot Table. Feel free add the Total Amount any time you want to create a Pivot Table via using Google Spreadsheet ,Microsoft Excel or any Tabular tools.
Now by referring to this Pivot Table , you can answer the question from your boss like:
What is the performance of each Salesperson so far ?
You can sense that the focus is on the Salesperson which the boss probably wants to decide the quarterly bonus based on the performance of each Salesperson.
How about if the boss wants to see the profitability of his/her business? He may probably ask:
What is the performance of each month since the beginning of the year?
Apple | Banana | Cherry | Dragonfruit | |
Month | Anna | Anna | Angela | Ann |
Jan | 5,000 | |||
Feb | 2,000 | 3,000 | ||
Mar | 1,000 |
Due to the fact that the focus of the question shifted from Salesperson to Month, we then put the parameter Month as the name of the row to present as the focus of the table. We name this action as pivot by Month. Compared with the previous Pivot Table which is pivoted by Salesperson, you can find that the name of the Salesperson Anna shows twice this time. Because as we want to consolidate the data of Month (i.e. pivot by Month), we can only sacrifice the neatness of the Salesperson.
The term “Pivot” means whenever you want to rotate any one of the axises from 3 axises (for example), you need to use 1 axis to rely on. The axis you relied on is called the Pivot, while the axis is referring to the columns (or parameters, or dimensions , whatever you name it) in a normal Table. While in the physical world there can only be a maximum of 3 axises , you can add as infinity number of axises as you wish in logical world.
Pivot Table is good for presentation and bad for atomic level recording. If you start recording by applying the data schema of the Pivot Table, you will realise that if you try to insert the value of the table like what “Sales Turnover Pivot Table (Salesperson , Product , Month) , pivot by Salesperson” Table did previously, eventually you will have some missing value in the cell of the row’s name. Besides, the Pivot Table can never serve the function of data processing in turns of filtering and sorting.
Even worse, the client , including your boss, would like to derive different views for different scenarios in different periods of time. If you directly record the data in Pivot Table format, you will soon realise that whenever the client wants to change the pivot of the Pivot Table, say based on Month instead of based on Product, your previous effort on typing in the value , will be in vain. Therefore:
Use Normal Table format to record
Use Pivot Table format to present
When you search in the internet, although there is many other theory with the same name , Bingo! Theory (remarks : the “!” does matter) is a terminology which is invented by Diamond Digital Marketing to explain the priority of a project go vertically or horizontally.
Bingo is a popular game of chance where players mark off numbers on cards as they are randomly drawn by a caller. The goal is to be the first to complete a specific pattern, such as a horizontal line , vertical line or diagonal, and shout “Bingo!” to win.
Let’s start the explanation by Figure 2. To gain the every time when you complete any of a Client which contains 4 Steps which costs you USD$5 per step, you will be rewarded by a Sales turnover by USD$30, which in turn you get a Bingo! and will bring you USD$10 (30 – (5 x 4) profit.
However, you quickly realise that it is really hard for you to employ a staff who is resilient and has cross-talent which can cater both the 4 Steps covering Business Development , Design, Production and Marketing.
To enjoy the economy of scale, you hired a Business Development Manager, a Designer, a Production Manager and a Marketer to cater 4 Steps separately and respectively. They work very hard and perform well in the very beginning, bringing you 4 times of Sales Turnover (i.e. 4 Bingo!) from Client A,B,C and D of USD$ (30 x 4) – (5 x 16) = USD$40 in total.
However, one day your Marketer requested to resign from his position, which you understand and let him go. Due to the fact that you need to have Bingo! only if you can do both Step 1 ,2,3 and 4 at the same time, as now the Marketer is quit which cannot delivery the Step 4, which means that the effort of Step 1,2 and 3 for the Client A,B,C,D will all be in vain, costing you in total USD5 * 12 = USD$70 lost in total.
In this example, you can see that if you do horizontally , you can deliver as minimum as 4 Steps for any of a Client which costs you USD$5 x 4 = USD$20 to get a Bingo! USD$30 Sales turnover.
However, if you go vertically, even you have done 12 Steps which cost you USD$ 5 * 12 = USD$70 , which means that even though you are more hard working than the minimum 4 Steps, you still cannot get your Bingo! reward.
This example exactly reflects the reality that the most hard working one is not necessarily the one who makes the most profit , only if you have learnt how to prioritise of your work.
In most cases a great success is brought by a cross-talent ability, while in reality the education system only focuses on producing expert with specific talent. Whether you go broad (horizontal) or deep (vertical) is a matter of preference which is no clear cut right or wrong. However, it is crucial that at least you know if you are encountering this Bingo! Theory in your daily choice.
In SaaS or traditional manufacturing industry , Minimum Viable Product (MVP) refers to a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. The MVP is a typical choice of go horizontal instead of go vertical.
A Neural Network Model, also known as an artificial neural network (ANN), is a type of machine learning model inspired by the structure and function of the human brain.
While this model is applied in the Marketing domain, it becomes the Marketing Neural Networking Model.
Instead of diving into the intricacy of the mathematical formula and operation, we instead will put the spotlight on the semantic logic behind the calculation.
In a nutshell, while Marketing Consultant is mainly to providing Marketing Strategy, a Marketing Strategy is simply making a series of decisions on how to choose among alternatives. For example, if you want to sell a Tattoo Printer to teenages, will you use Facebook or Instagram to promote your product?
To choose between “Facebook” or “Instagram” (i.e. 2 alternatives) is called Marketing Strategy. For sure, in reality, it always takes more than 1 factor (or attribute) to make a decision, and takes more than 1 decisions to formuate a strategy . You can imagine it’s in fact a dynamic decision chain in which the outcome of 1 decision will affect not only the outcome, but also even the option values (i.e. all alternatives) of the decision.
The Marketing Neural Networking Model is purposed to learn and solve how to make decisions in a scientific way.
Only after we turn the decision making process in a scientific way can we automate the decision making process via A.I. by applying the Marketing Neural Networking Model, which in turn creates an A.I. Marketing Consultant.
Although the intricacy of the Neural Networking Model is a bit scary, decoupling it in piecemeal and demonstrating with a story, will definitely aid you to comprehend the concept more efficiently. Bear in mind that it is obviously a simplified example which in reality will be 1000 times in scale.
Before starting the story, allow us to provide you the legend of the Figure (Marketing Neural Networking Model) above:
Rectangle ( ▭ ) : The Attribute (or Property, or Layer) of the Object, which the Object is the Marketing Neural Network Model.
Circle (○) : Nodes (i.e. any Business Concepts)
Sold Line ( ⎯⎯ ) : Positive Edges which has directionaly relationship between 2 Nodes
Dot Line (···) : Negative Edges which has NO directional relationship between 2 Nodes
Imagine you are the CEO of a conglomerate which at the same time run a Fashion Retail Store as well as a Diamond Wholesaler business. You are required by your shareholders to incrementally increase the ROI of the conglomerate by 10X, which is quite an impossible mission. In order to achieve this goal, you start by enumerating all the “Concepts” (i.e. the Node) in your mind which related to the business as below:
In reality, the process of addressing , enumerating and filtering all the Concepts (i.e. the Nodes) relating to the business is almost an impossible task for human beings. The more knowledge Nodes the marketer acquired and manipulated, the more professional he is.
Back to our story, immedate after you enumerated all the Nodes in your mind which you think are related to your business, you addressed some pattern that there are some patterns within these Nodes:
Having played around with the interface of the Google Merchant Center for a day, you realized that Google Merchant Center is mainly designed for listing the products in the storefront of Google Shopping Tab in retail price, and therefore the Google Merchant Center is better to apply in any retail instead of wholesale business because there is no any field for the Google Merchant Center to insert any tiered pricing or bulk discount in the storefront. In this sense, you addressed that what Digital Assets (Attribute) you are uisng will be dependent to the Business Model (Attribute 1). Therefore you deduce your own business rule (which is called business intelligent in the business world) as below:
Business Rules 1 : Digital Assets is dependent to the Business Model
By applying Business Rules 1 in your business, you decide to adapt the Google Merchant Center into your Fashion Retail Store (Edge 2) and meanwhile NOT adapt in your Diamond Wholesaler business (Edge 5)
While having 10 years experience on using Linkedin Business Page, you understand that the users who are responsive in Linkedin are mainly seeking for business opportunities (i.e. B2B) instead of retail purchasing (i.e. B2C). Although you have this “insight”, you still from time to time scrolled to some Feeds in Linkedin which are selling to retail customers. As you cannot 100% sure about your insight, and therefore you classify it as a Correlation Coefficient (denotes “r”) relationship which the Correlation Coefficient of the responsiveness between Linkedin Business Page and Retail Business is low (e.g. r=0.3) , and meanwhile it is high (e.g. r=0.9) between Linked Business Page and Wholesale business.
In this stage, you can bypass the understanding of the mathematical operation of the Correlation Coefficient. What you need to know is simply that the higher the value of the Correlation Coefficient (r) , the closer the relationship to (Positive) Causal Relationship.
Now based on the Correlation Coefficient which is conducted by your empirical study, you deduce other Business Rule as below:
Business Rule 2 : The responsiveness of the Linkedin Business Page is high for Wholesale Business and low for Retail Business.
By applying Business Rules 2 in your business, you decide to adapt the Linkedin Business Page into your Diamond Wholesaler Store (Edge 6) and meanwhile NOT adapt in your Fashion Retail Store business (Edge 3)
By continuing deducing the Business Rules based on your experience or any other statistic, you figured out the following Business Rules for the Edge as below:
Decision# | Involved Edge | Business Rules |
---|---|---|
Edge #1 and #7 | Fashion Retail Store > Website > Ads | Fashion Retail Store needs Website as the landing page of placing Ads. |
Edge #1 and #8 | Fashion Retail Store > Website > Payment Gateway | Fashion Retail Store needs Payment Gateway to install in Website to receive payment from Client |
Edge #1 and #9 | Fashion Retail Store > Website > Feed | Fashion Retail Store needs put the Feed in the Website for content marketing articles publishing |
Edge #1 and #10 | Fashion Retail Store > Website > Enquiry Form | Fashion Retail Store needs put the Enquiry Form in the Website for replying questions from client. |
Edge #2 and #11 | Fashion Retail Store > Google Merchant Center > Ads | Fashion Retail Store needs Google Merchant Center showcasing their product in Google Ads Campaign |
Edge #2 and #12 | Fashion Retail Store > Google Merchant Center > Payment Gateway | Google Merchant Center does not support Payment Gateway |
Edge #2 and #13 | Fashion Retail Store > Google Merchant Center > Feed | Fashion Retail Store needs to turn the Product Page of the website to Google Merchant Center’s Feed |
Edge #2 and #14 | Fashion Retail Store > Google Merchant Center > Enquiry Form | Fashion Retail Store does not support Enquiry Form Function |
Edge #3 and #15 | Fashion Retail Store > Linkedin Business Page > Ads | Ads placed in Linkedin Business Page is not appropriate for Fashion Retail Store |
Edge #3 and #16 | Fashion Retail Store > Linkedin Business Page > Payment Gateway | Linkedin Business Page does not support Payment Gateway |
Edge #3 and #17 | Fashion Retail Store > Linkedin Business Page > Feed | Audience of Linkedin Business Page is not expected Retail Feed from Fashion Retail Store showing in their Linkedin Personal account. |
Edge #3 and #18 | Fashion Retail Store > Linkedin Business Page > Enquiry Form | There is no Enquiry Form function in Linkedin Business Page |
Edge #4 and #7 | Diamond Wholesaler > Website > Ads | Diamond Wholesaler needs Website as the landing page of placing Ads. |
Edge #4 and #8 | Diamond Wholesaler > Website > Payment Gateway | Diamond Wholesaler does not expect the client to place order in the Website directly. Therefore Payment Gateway is not needed. |
Edge #4 and #9 | Diamond Wholesaler > Website > Feed | Diamond Wholesaler needs put the Feed in the Website for content marketing articles publishing |
Edge #4 and #10 | Diamond Wholesaler > Website > Enquiry Form | Diamond Wholesaler definitely needs Enquiry Form in the Website as the client will ask for product info and transactional info before placing order. |
Edge #5 and #11 | Diamond Wholesaler > Google Merchant Center > Ads | Diamond Wholesaler may not need to place the Ads via Google Merchant Center Campaign because Google Merchant Center do not support tiered-pricing or quantity pricing function. |
Edge #5 and #12 | Diamond Wholesaler > Google Merchant Center > Payment Gateway | Google Merchant Center does not support Payment Gateway |
Edge #5 and #13 | Diamond Wholesaler > Google Merchant Center > Feed | Diamond Wholesaler may not need to sync the Product Feed from their website to Google Merchant Center because Google Merchant Center do not support tiered-pricing or quantity pricing function. |
Edge #5 and #14 | Diamond Wholesaler > Google Merchant Center > Enquiry Form | There is no Enquiry Form function in Google Merchant Center. |
Edge #6 and #15 | Diamond Wholesaler > Linkedin Business Page > Ads | Diamond Wholesaler is appropriate to place Ads in Linkedin Business Page to seek for the management level Decision Maker or Merchandiser based on the Job Title Ads segmentation. |
Edge #6 and #16 | Diamond Wholesaler > Linkedin Business Page > Payment Gateway | Linkedin Business Page does not support Payment Gateway |
Edge #6 and #17 | Diamond Wholesaler > Linkedin Business Page > Feed | Diamond Wholesaler is appropriate to publish Feed in Linkedin Business Page to seek for the management level Decision Maker or Merchandiser. |
Edge #6 and #18 | Diamond Wholesaler > Linkedin Business Page > Enquiry Form | There is no Enquiry Form function in Linkedin Business Page |
The reason why we need to enumerate all the possbile decision combinations is that while Strategy means “decision“, to formulate a Marketing Strategy, covering all possible decisions comprehensively is as important as figuring out the appropriate answer of a single decision.
The only way to enumerate 100% of the decision combinations is by enumerating all the Attributes and all Option Values of each Attributes, and multiplying them all together to become an Cartersian Product. In turn, there will be no decision combination missing out within the Model (i.e. figured out exactly ALL possibilties within the Model, no more and no less) , provided that there are no relevant attributes in the Marketing Neural Networking Model that are missing out, which we will discuss this “bug” in upcoming chapter.
Remember in the old days (or even today without A.I) you learn digital marketing strategies by listening from the advice provided by the senior digital marketing consultant to the client. Every time when you were participating in a client meeting, you were impressed by how deep the knowledge ocean that the senior digital marketing consultant acquired that seemed he could non stop sharing his knowledge forever. You dropped down every single piece of know-how into a notebook and dreamed of that you might become him some day when you acquired ALL his knowledge, although you never know how “exact quantity” of “ALL” knowledge is.
Even if luckily , you did the miracle and learned “all” the knowledge and become another iconic senior digital marketing consultant, your next generation will encounter the same problem as you did, which he/she needs to take notes and learn piece by piece starting from a blank paper.
This inefficient resistant makes the knowledge transmission process extremely slow, just like what human beings did in the passed 7,000 years since mankind’s history.
Bear in mind that the example that we made previously in this session only describes 24 decision combinations , which accounts for a extremely tiny portion of reality which probably has 10 of millions of decision combinations, which is far beyond the processing power of a mortal within his lifespan.
In order to have a systematic way to record all the Knowledge Nodes and the relationships amongst the Nodes, the Neural Networking Model is a perfect candidate to provide a paradigm which turn reality into a conceptualised mathematical model to do the job , not only by human beings but also by computer, which it’s compute power can dramatically speed up the pace of learning by decade of years, and letting processing ALL decision combinations to be an mission possible.
Human learning is a complex and on-going process which describes the interaction between the human being and the environement surrounded them, and how they interpret the data and formulate the model to project the world. While it’s worth a whole book to explain it, in this article we only extract the part which related to the Data Structure.
First of all, Data is nothing about computers or digital. long before the invention of computer or any digital devices, data exists.
Allow me to explain Data with an example. Some day 5,000 years ago in Mesopotamiaⓘ, a Sumerians named Adamen brought a sheep to the market for sale. While he stood in the street for almost 6 hours, finally he found a richman who was really going to buy his sheep for 50 Shekelsⓘ. He was happy and thought that if he could sell all the sheeps he possessed , which was 10 sheeps , he could have financial freedom. So he left the market and thought of how to execute his plan.
Immediately after he arrived home, he found it’s really hard for him to bring 10 sheeps from his home to the market. He was thinking that instead of bringing the entire sheeps to the market, is there any way that he can only bring part of the sheep? In turn, he cut off one nail from each of the sheep, and brought these 10 nails to the market to make people believe that he possessed 10 sheeps.
In this story, the nail of the sheep is acting as a Data to denote the underlying material object – the sheep.
You may wonder why he doesn’t simply use a paper and write the word “sheep” on it. Please bear in mind that paper and words were not invented at that time.
Of course, when time goes by, when the word and paper were invented, people like Adamen can simply use a paper to write down the wording “Sheep” to denote the underlying material object “Sheep”. No matter how , the function of Data, to point a word (or symbol , or glyphics, or character, or sound, or pronunciation, you name it.) to an underlying material object, is always the same.
That’s the beginning of the story of Data.
A data structure is a concept for running a database. Data structure is a specialised format for organising, processing, retrieving, and storing data. It defines how data is arranged in a computer so that it can be accessed and updated efficiently. There are mainly 2 types of Data Structures:
In common English for easy understanding, you can regard Relational Data Structure as a 2-Dimension table which use both Column and Row to co-ordinate a Value (i.e. we call it “Cell” in MS Excel or Google Spreadsheet). It mainly focus on the relationship between the attribute (i.e. the Column Name and Born) and the attaching object (i.e. the Table Ancient Celebrities) itself.
Example of a Relational Data Structure (i.e. a Table)
Ancient Celebrities # | Name | Born in | Job Title |
---|---|---|---|
201 | Plato | B.C 429 | Philosopher |
202 | Aristotle | B.C 322 | Philosopher & Mathematician |
203 | Alexander the Great | B.C 356 | King of Macedonia |
In common English, you can regard Non-relational Data Structure as a tree (or hierarchical) list which uses Node and Edge to coordinate the Value. Unlike Relational Data Structure which focus on the relationship between the attribute and it’s attaching object, Non-relational Data Structure focus on the relationship (i.e. the Edge) between Object (i.e. the Node) and another Object (i.e. another Node)
Example of a Non-relational Data Structure (i.e. a Tree List)
- Plato (Node 1)
- Aristotle (Node 2)
- Alexander the Great (Node 3)
whereas , there are 3 Nodes in the Tree List. Although it is tempting to think that there only 2 relationships (Edges) between the 3 Nodes, in fact there are 4 relationships (Edges) in among:
4 Edges instead of 2 Edges because the direction of the relationship (Edge) does matter.
Let’s start this topic with a question asked from your friend:
Hey, who is Aristotle?
To answer this question, you may reply him in English as below:
Aristotle is ancient philosopher and mathematician who was born in B.C 322 , whom is the student of Plato as well as the teacher of Alexander the Great.
While the answer above is exactly same as what we will speak in daily English, this sentence is informative enough for anyone to have a brief understanding on who Artistotle is. Nevertheless, even though you are very good in English, compared with the time spent on reading the Table and Tree List , you may spend more time to read through the English sentence word by word.
On the contrary, while you are reading the sentence, in fact what you do to comprehend the sentence is by idetntifing the attributes of Aristotle (e.g. Born in , Job Title) , as well as the hierarchical relationship (i.e. Edges) between Plato (Node 1) and Alexander the Great (Node 3).
By presenting in Table and Tree List format, only with few hours of practice, anyone can comprehend any articles much faster than simply reading in plain English format.
However, the story of human learning does not end just like this. Back to our example, while your friend seriously listened to your reply, although he realised that Aristotle is the student of Plato, you could never imagine he didn’t know the meaning of “B.C.” and he asked you about what is “B.C.”.
“B.C.” is an acronym of “Before Christ”. It is a dating system which is used to denote any year before the birth of Christ. The opposite of “B.C.” is “A.D.”, which stands for “Anno Domini”, which is a Latin phrase meaning “In the Year of Our Lord”. The year of 2024 means we are in A.D. 2024, which we normally will skip the terms “A.D.” as it is by default.
Having Replied by you, now your friend knew the new knowledge regarding the dating system B.C. and A.D. you can simply turn the plain English into the Table and Tree List format as if we have done before as below:
Acronym | Word Stem | Language | Presenting Year |
---|---|---|---|
B.C | Before Christ | English | Before Christ Born at Year 0 |
A.D | Anno Domini | Latin | After Christ Born at Year 0 |
- Dating System
- B.C.
- A.D.
In fact, every single concept (I called it Knowledge Node) will always have its own attributes as well as the relationships (i.e. Edges) between other Nodes.
Imagine if your friend is a 5-year old boy and he knows very little about what you said (and even about this world!) and he is going to ask you almost every single word in your sentense like this:
If you turn all these 11 concepts (i.e. Knowledge Nodes) into Table and Tree List format, you can imagine the Data Structure will resemble the image below:
This is a typical Adaptive Search pattern which someone need to “search for what he wants to search for“, and in turn forming a Knowledge Graph which a smart person like you will quickly realise that you can (or need) to add an infinite number of Nodes and Edges inside the Graph in order to learn something. The more Nodes you add into the diagram, the more attributes will be derived. And each attribute of a Node can become a new Node.
And that’s exactly how the data structure behaves during human learning.
Remember the previous example when you explain to your friend who Aristotle is. In order to make him understand who is Aristotle, he need to acquire the foundation knowledge which made him diving into 4 level of Nodes as below:
You can now sense the challenge of how a human being learns a new concept which he will get lost in the maze very soon after he has no idea how many levels he should dive into in order to comprehensively understand a single concept in a topic (i.e. a Knowledge Domain). And the Knowledge statedion in your brain will finally distribute in this way:
Nevertheless, don’t be upset by the truth and we should (and already have) found a “Map” to navigate us in this knowledge maze.
Finally , let’s back to Aristotle again and end this topic by an citation from him which describes the problem being suffered during human learning:
The More You Know , The More You Realize You Don’t Know
Introduction Electricity is one of the most important advancements of modern life. It powers nearly everything around us ; from…
DDM Group Company Google Drive bGraph Interface While bGraph is referring to the Enterprise Knowledge Management System, the wording “Interface”…
CAUTION Please read carefully and understand every single words inside the paragraph before you use the portable power station. Disclaimer…