Combining Knowledge Administration and Knowledge Storytelling to Generate Worth

Combining Knowledge Administration and Knowledge Storytelling to Generate Worth
Combining Knowledge Administration and Knowledge Storytelling to Generate Worth

Currently, I’ve been specializing in knowledge storytelling and its significance in successfully speaking the outcomes of information evaluation to generate worth. Nevertheless, my technical background, which could be very near the world of information administration and its issues, pushed me to mirror on what knowledge administration wants to make sure you can construct data-driven tales shortly. I got here to a conclusion that’s usually taken with no consideration however is all the time good to remember. You’ll be able to’t rely solely on knowledge to construct data-driven tales. It’s also obligatory for an information administration system to contemplate a minimum of 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 Programs
  • Knowledge Storytelling
  • Knowledge Administration and Knowledge Storytelling


1. Introducing Knowledge

We regularly discuss, use, and generate knowledge. However have you ever questioned what knowledge is and what kinds 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 various kinds of knowledge:

  • Structured knowledge is knowledge organized in a set schema, reminiscent of SQL or CSV. The primary execs of such a knowledge are that it’s straightforward to derive insights. The primary disadvantage is that schema dependence limits scalability. A database is an instance of such a knowledge.
  • Semi-structured knowledge is partially organized with out a mounted schema, reminiscent of JSON XML. The professionals are that they’re extra versatile than structured knowledge. The primary cons is that the meta-level construction could include unstructured knowledge. Examples are annotated textual content, reminiscent of tweets with hashtags.
  • Unstructured knowledge, reminiscent of audio, video, and textual content, aren’t annotated. The primary execs are that they’re unstructured, so it’s straightforward to retailer them. They’re additionally very scalable. Nevertheless, they’re difficult to handle. For instance, it’s troublesome to extract which means. Plain textual content and digital pictures 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 observe of ingesting, processing, securing, and storing a corporation’s knowledge, which is then utilized for strategic decision-making to enhance enterprise outcomes [1]. There are three central knowledge administration techniques:

  • Knowledge Warehouse
  • Knowledge Lake
  • Knowledge Lakehouse


2.1 Knowledge Warehouse

A knowledge 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 an information warehouse.


The architecture of a data warehouseThe architecture of a data warehouse
Fig. 1: The structure of an information warehouse


The primary issues with knowledge warehouses are:

  • Scalability – they aren’t 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. In contrast to an information warehouse, an information lake manages and supplies methods to eat or course of structured, semi-structured, and unstructured knowledge. Ingesting uncooked knowledge permits an information lake to ingest historic and real-time knowledge in a uncooked storage system. 

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


The architecture of a data lakeThe architecture of a data lake
Fig. 2: The structure of an information lake


The primary benefit of an information lake is that it could possibly ingest any type of knowledge shortly because it doesn’t require any preliminary processing. The primary disadvantage of an information 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 an information lake has advanced into the info lakehouse, an augmented knowledge lake that features help for transactions at its high. In observe, an information lakehouse modifies the present knowledge within the knowledge lake, following the info warehouse semantics, as proven within the following determine. 


The architecture of a data lakehouseThe architecture of a data lakehouse
Fig. 3: The structure of an information lakehouse


The info lakehouse ingests the info extracted from operational sources, reminiscent of structured, semi-structured, and unstructured knowledge. It supplies it to analytics purposes, reminiscent of reporting, dashboarding, workspaces, and purposes. A knowledge lakehouse includes the next important parts: 

  • 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.


The general architecture of a data management systemThe general architecture of a data management system
Fig. 4. The final structure of an information administration system


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

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

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

On this article, we’ll concentrate on 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 value of one thing for somebody
  3. Values: The beliefs folks have, particularly about what is true and flawed and what’s most necessary 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 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 crucial for the folks producing knowledge and different exterior folks, as proven within the following determine.


Combining Knowledge Administration and Knowledge Storytelling to Generate Worth
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, the complete knowledge pipeline must be designed to speak knowledge to the ultimate viewers (decision-makers) with a purpose to generate worth.

3. Knowledge Storytelling

There are 3 ways to speak knowledge:

  • Knowledge reporting consists of 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 an information evaluation course of to an viewers by a narrative. Primarily based 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, primarily based on the cultural sensitivity of the viewers

Knowledge Storytelling should contemplate 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 an information storytelling perspective, which incorporates knowledge and metadata (context)


The data management pipeline from the data storytelling perspectiveThe data management pipeline from the data storytelling perspective
Fig. 6: The info administration pipeline from the info storytelling perspective


The Knowledge Administration system includes two parts: knowledge administration, the place the primary actor is the info engineer and knowledge evaluation, the place the primary actor is the info scientist.
The info 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 kinds of metadata administration techniques:

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

The info 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 have a look at the complete 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 have a look at the folks behind the info. 



Congratulations! You might have simply discovered how to have a look at Knowledge Administration from the Knowledge Storytelling perspective. It’s best to contemplate two elements, along with knowledge:

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

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



[1] IBM. What is data management?
[2] The Cambridge Dictionary. Value.
[3] Peter Crocker. Guide to enhancing data context: who, what, when, where, why, and how


Exterior sources

Using Data Storytelling to Turn Data into Value [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 Functions, 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 also be an enthusiastic tech author.

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