Combining Knowledge Administration and Knowledge Storytelling to Generate Worth

Currently, I’ve been specializing in information storytelling and its significance in successfully speaking the outcomes of knowledge evaluation to generate worth. Nonetheless, my technical background, which may be very near the world of knowledge administration and its issues, pushed me to replicate on what information administration wants to make sure you can construct data-driven tales rapidly. I got here to a conclusion that’s usually taken without any consideration however is all the time good to remember. You may’t rely solely on information to construct data-driven tales. It’s also crucial for an information administration system to think about a minimum of two facets. Do you need to know which of them? Let’s attempt to discover out on this article.

What we’ll cowl on this article:

• Introducing Knowledge
• Knowledge Administration Techniques
• Knowledge Storytelling
• Knowledge Administration and Knowledge Storytelling

 

1. Introducing Knowledge

 
We frequently speak about, use, and generate information. However have you ever puzzled what information is and what varieties of information exist? Let’s attempt to outline it.

Knowledge is uncooked details, numbers, or symbols that may be processed to generate significant data. There are various kinds of information:

• Structured information is information organized in a hard and fast schema, comparable to SQL or CSV. The principle execs of such a information are that it’s simple to derive insights. The principle disadvantage is that schema dependence limits scalability. A database is an instance of such a information.
• Semi-structured information is partially organized and not using a mounted schema, comparable to JSON XML. The professionals are that they’re extra versatile than structured information. The principle cons is that the meta-level construction could comprise unstructured information. Examples are annotated textual content, comparable to tweets with hashtags.
• Unstructured information, comparable to audio, video, and textual content, usually are not annotated. The principle execs are that they’re unstructured, so it’s simple to retailer them. They’re additionally very scalable. Nonetheless, they’re difficult to handle. For instance, it’s troublesome to extract which means. Plain textual content and digital images are examples of unstructured information.

To prepare information whose quantity is growing over time, it’s important to handle them correctly. 

 

2. Knowledge Administration

 
Knowledge administration is the follow of ingesting, processing, securing, and storing a company’s information, which is then utilized for strategic decision-making to enhance enterprise outcomes [1]. There are three central information administration programs:

• Knowledge Warehouse
• Knowledge Lake
• Knowledge Lakehouse

 

2.1 Knowledge Warehouse

An information warehouse can deal with solely structured information post-extraction, transformation, and loading (ETL) processes. As soon as elaborated, the info can be utilized for reporting, dashboarding, or mining. The next determine summarizes the construction of an information warehouse.

 

Fig. 1: The structure of an information warehouse
 

The principle issues with information warehouses are:

• Scalability – they don’t seem to be scalable
• Unstructured information – they don’t handle unstructured information
• Actual-time information – they don’t handle real-time information.

 

2.2 Knowledge Lake

A Knowledge Lake can ingest uncooked information as it’s. Not like an information warehouse, an information lake manages and gives methods to eat or course of structured, semi-structured, and unstructured information. Ingesting uncooked information permits an information lake to ingest historic and real-time information in a uncooked storage system. 

The information lake provides a metadata and governance layer, as proven within the following determine, to make the info consumable by the higher layers (studies, dashboarding, and information mining). The next determine exhibits the structure of an information lake.

 

Fig. 2: The structure of an information lake
 

The principle benefit of an information lake is that it will probably ingest any form of information rapidly because it doesn’t require any preliminary processing. The principle disadvantage of an information lake is that because it ingests uncooked information, it doesn’t help the semantics and transactions system of the info warehouse.

 

2.3 Knowledge Lakehouse

Over time, the idea of an information lake has advanced into the info lakehouse, an augmented information lake that features help for transactions at its high. In follow, an information lakehouse modifies the present information within the information lake, following the info warehouse semantics, as proven within the following determine. 

 

Fig. 3: The structure of an information lakehouse
 

The information lakehouse ingests the info extracted from operational sources, comparable to structured, semi-structured, and unstructured information. It gives it to analytics purposes, comparable to reporting, dashboarding, workspaces, and purposes. An information lakehouse includes the next essential elements: 

• Knowledge lake, which incorporates desk format, file format, and file retailer
• Knowledge science and machine studying layer
• Question engine 
• Metadata administration layer
• Knowledge governance layer. 

 

2.4 Generalizing the Knowledge Administration System Structure

The next determine generalizes the info administration system structure.

 

Fig. 4. The overall structure of an information administration system
 

An information administration system (information warehouse, information lake, information lakehouse, or no matter) receives information as an enter and generates an output (studies, dashboards, workspaces, purposes, …). The enter is generated by individuals and the output is exploited once more by individuals. Thus, we are able to say that we’ve individuals in enter and other people in output. An information administration system goes from individuals to individuals. 

Individuals in enter embrace individuals producing the info, comparable to individuals carrying sensors, individuals answering surveys, individuals writing a assessment about one thing, statistics about individuals, and so forth. Individuals in output can belong to one of many following three classes: 

• Common public, whose goal is to be taught one thing or be entertained
• Professionals, who’re technical individuals wanting to know information 
• Executives who make selections.

On this article, we are going to give attention to executives since they generate worth.

However what’s worth? The Cambridge Dictionary provides completely different definitions of worth [2].

1. The sum of money that may be obtained for one thing
2. The significance or price of one thing for somebody
3. Values: The beliefs individuals have, particularly about what is correct and incorrect and what’s most vital in life, that management their habits.

If we settle for the definition of worth because the sum of money, a call maker might generate worth for the corporate they work for and not directly for the individuals within the firm and the individuals utilizing the providers or merchandise supplied by the corporate. If we settle for the definition of worth because the significance of one thing, the worth is crucial for the individuals producing information and different exterior individuals, as proven within the following determine.

 

Fig. 5: The method of producing worth
 

On this state of affairs, correctly and successfully speaking information to decision-makers turns into essential to producing worth. Because of this, your complete information pipeline must be designed to speak information to the ultimate viewers (decision-makers) as a way to generate worth.
 
 

3. Knowledge Storytelling

 
There are 3 ways to speak information:

• Knowledge reporting consists of information description, with all the small print of the info exploration and evaluation phases. 
• Knowledge presentation selects solely related information and exhibits them to the ultimate viewers in an organized and structured manner. 
• Knowledge storytelling builds a narrative on information.

Let’s give attention to information storytelling. Knowledge Storytelling is speaking the outcomes of an information evaluation course of to an viewers by a narrative. Based mostly in your viewers, you’ll select an applicable

• Language and Tone: The set of phrases (language) and the emotional expression conveyed by them (tone)
• Context: The extent of particulars so as to add to your story, based mostly on the cultural sensitivity of the viewers

Knowledge Storytelling should contemplate the info and all of the related data related to information (context). Knowledge context refers back to the background data and pertinent particulars surrounding and describing a dataset. In information pipelines, this information context is saved as metadata [3]. Metadata ought to present solutions to the next:

• Who collected information
• What the info is about
• When the info was collected
• The place the info was collected
• Why the info was collected
• How the info was collected

 

3.1 The Significance of Metadata

 
Let’s revisit the info administration pipeline from an information storytelling perspective, which incorporates information and metadata (context)

 

Fig. 6: The information administration pipeline from the info storytelling perspective
 

The Knowledge Administration system includes two components: information administration, the place the primary actor is the info engineer and information evaluation, the place the primary actor is the info scientist.
The information engineer ought to focus not solely on information but in addition on metadata, which helps the info scientist to construct the context round information. There are two varieties of metadata administration programs:

• Passive Metadata Administration, which aggregates and shops metadata in a static information catalog (e.g., Apache Hive)
• Lively Metadata Administration, which gives dynamic and real-time metadata (e.g., Apache Atlas)

The information scientist ought to construct the data-driven story.

 

4. Knowledge Administration and Knowledge Storytelling

 
Combining Knowledge Administration and Knowledge Storytelling means:

• Contemplating the ultimate individuals who will profit from the info. A Knowledge Administration system goes from individuals to individuals.
• Contemplate metadata, which helps construct probably the most highly effective tales.

If we take a look at your complete information pipeline from the specified end result perspective, we uncover the significance of the individuals behind every step. We will generate worth from information provided that we take a look at the individuals behind the info. 

 

Abstract

 
Congratulations! You will have simply realized how to have a look at Knowledge Administration from the Knowledge Storytelling perspective. It is best to contemplate two facets, along with information:

• Individuals behind information
• Metadata, which supplies context to your information.

And, past all, always remember individuals!  Knowledge storytelling helps you take a look at the tales behind the info!

 

References

 
[1] IBM. What’s information administration?
[2] The Cambridge Dictionary. Worth.
[3] Peter Crocker. Information to enhancing information context: who, what, when, the place, why, and the way

 

Exterior sources

 
Utilizing Knowledge Storytelling to Flip Knowledge into Worth [talk] 
 
 

Angelica Lo Duca (Medium) (@alod83) is a researcher on the Institute of Informatics and Telematics of the Nationwide Analysis Council (IIT-CNR) in Pisa, Italy. She is a professor of “Knowledge Journalism” for the Grasp diploma course in Digital Humanities on the College of Pisa. Her analysis pursuits embrace Knowledge Science, Knowledge Evaluation, Textual content Evaluation, Open Knowledge, Internet Purposes, Knowledge Engineering, and Knowledge Journalism, utilized to society, tourism, and cultural heritage. She is the writer of the e book Comet for Knowledge Science, printed by Packt Ltd., of the upcoming e book Knowledge Storytelling in Python Altair and Generative AI, printed by Manning, and co-author of the upcoming e book Studying and Working Presto, by O’Reilly Media. Angelica can be an enthusiastic tech author.