Combining Knowledge Administration and Knowledge Storytelling to Generate Worth

Recently, I’ve been specializing in knowledge 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 mirror on what knowledge administration wants to make sure you can construct data-driven tales rapidly. I got here to a conclusion that’s usually taken with no consideration however is all the time good to remember. You possibly can’t rely solely on knowledge to construct data-driven tales. It is usually vital for a knowledge administration system to contemplate at the very least two elements. 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 discuss, use, and generate knowledge. However have you ever puzzled what knowledge is and what forms of knowledge exist? Let’s attempt to outline it.

Knowledge is uncooked information, numbers, or symbols that may be processed to generate significant data. There are several types of knowledge:

• Structured knowledge is knowledge organized in a set schema, reminiscent of SQL or CSV. The principle execs of any such knowledge are that it’s simple to derive insights. The principle downside is that schema dependence limits scalability. A database is an instance of any such knowledge.
• Semi-structured knowledge is partially organized with no mounted schema, reminiscent of JSON XML. The professionals are that they’re extra versatile than structured knowledge. The principle cons is that the meta-level construction might comprise unstructured knowledge. Examples are annotated textual content, reminiscent of tweets with hashtags.
• Unstructured knowledge, reminiscent of 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 tough to extract which means. Plain textual content and digital photographs are examples of unstructured knowledge.

To arrange knowledge whose quantity is rising 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 knowledge, which is then utilized for strategic decision-making to enhance enterprise outcomes [1]. There are three central knowledge administration methods:

• Knowledge Warehouse
• Knowledge Lake
• Knowledge Lakehouse

2.1 Knowledge Warehouse

An information warehouse can deal with solely structured knowledge 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 a knowledge warehouse.

The principle issues with knowledge warehouses are:

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

2.2 Knowledge Lake

A Knowledge Lake can ingest uncooked knowledge as it’s. Not like a knowledge warehouse, a knowledge lake manages and offers methods to devour or course of structured, semi-structured, and unstructured knowledge. Ingesting uncooked knowledge permits a knowledge lake to ingest historic and real-time knowledge 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 (reviews, dashboarding, and knowledge mining). The next determine reveals the structure of a knowledge lake.

Fig. 2: The structure of a knowledge lake

The principle benefit of a knowledge lake is that it might ingest any form of knowledge rapidly because it doesn’t require any preliminary processing. The principle downside of a knowledge lake is that because it ingests uncooked knowledge, it doesn’t help the semantics and transactions system of the info warehouse.

2.3 Knowledge Lakehouse

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

Fig. 3: The structure of a knowledge lakehouse

The information lakehouse ingests the info extracted from operational sources, reminiscent of structured, semi-structured, and unstructured knowledge. It offers it to analytics functions, reminiscent of reporting, dashboarding, workspaces, and functions. An information lakehouse includes the next foremost 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 final structure of a knowledge administration system

An information administration system (knowledge warehouse, knowledge lake, knowledge lakehouse, or no matter) receives knowledge as an enter and generates an output (reviews, dashboards, workspaces, functions, …). The enter is generated by folks and the output is exploited once more by folks. Thus, we will say that we have now folks in enter and other people in output. An information administration system goes from folks to folks. 

Folks in enter embody folks producing the info, reminiscent of folks carrying sensors, folks answering surveys, folks writing a evaluation about one thing, statistics about folks, and so forth. Folks in output can belong to one of many following three classes: 

• Basic public, whose goal is to be taught one thing or be entertained
• Professionals, who’re technical folks wanting to know knowledge 
• Executives who make choices.

On this article, we’ll concentrate on executives since they generate worth.

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

1. The amount of cash that may be acquired for one thing
2. The significance or price of one thing for somebody
3. Values: The beliefs folks have, particularly about what is correct and mistaken and what’s most necessary in life, that management their conduct.

If we settle for the definition of worth because the amount of cash, a call maker might generate worth for the corporate they work for and not directly for the folks within the firm and the folks utilizing the companies or merchandise provided by the corporate. If we settle for the definition of worth because the significance of one thing, the worth is important for the folks producing knowledge and different exterior folks, as proven within the following determine.

Fig. 5: The method of producing worth

On this situation, correctly and successfully speaking knowledge to decision-makers turns into essential to producing worth. For that reason, your entire knowledge pipeline ought to be designed to speak knowledge to the ultimate viewers (decision-makers) to be able to generate worth.
 
 

3. Knowledge Storytelling

 
There are 3 ways to speak knowledge:

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

Let’s concentrate on knowledge storytelling. Knowledge Storytelling is speaking the outcomes of a knowledge evaluation course of to an viewers by means of a narrative. Primarily based in your viewers, you’ll select an acceptable

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

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

• Who collected knowledge
• 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 a knowledge storytelling perspective, which incorporates knowledge and metadata (context)

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

The Knowledge Administration system includes two parts: knowledge administration, the place the principle actor is the info engineer and knowledge evaluation, the place the principle actor is the info scientist.
The information engineer ought to focus not solely on knowledge but additionally on metadata, which helps the info scientist to construct the context round knowledge. There are two forms of metadata administration methods:

• Passive Metadata Administration, which aggregates and shops metadata in a static knowledge catalog (e.g., Apache Hive)
• Lively Metadata Administration, which offers 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 folks to folks.
• Think about metadata, which helps construct probably the most highly effective tales.

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

Abstract

 
Congratulations! You have got simply realized how to have a look at Knowledge Administration from the Knowledge Storytelling perspective. You must think about two elements, along with knowledge:

• Folks behind knowledge
• Metadata, which provides context to your knowledge.

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

References

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

Exterior assets

 
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 “Data Journalism” for the Grasp diploma course in Digital Humanities on the College of Pisa. Her analysis pursuits embody Knowledge Science, Knowledge Evaluation, Textual content Evaluation, Open Knowledge, Net Functions, Knowledge Engineering, and Knowledge Journalism, utilized to society, tourism, and cultural heritage. She is the creator 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.