OPEN LECTURE: Data Sources and Data Types in MarTech
- Megi Gogua
- Jan 25
- 3 min read
Updated: Mar 18
Introduction
Hello everyone!
I’m really happy to welcome you to my Online Open MarTech lecture.
My name is Megi, I have taught and studied MarTech for many years already, and with this series of Online Open Lectures, I would like to share with you what I know so far.
This series of lectures will contain all the topics related to MarTech, from broad approaches to certain examples and cases.
These lectures are for those who consider MarTech to be the topic, field, or direction of their career development. Also, these videos are for those who already have marketing experience but want to see a broader horizon and more examples of the application of technologies in marketing. Those who generally wish to learn something new can also benefit from these recordings of Online Open Lectures.
I really hope that this initiative of mine will be useful for you and helpful because I would love to share with you everything I know and invite you to this wonderful journey of finding something new, something interesting. If you ask me, this field of MarTech is incredibly fascinating and useful, and I’m happy to be able to guide you through it.
MarTech operates on Data - today, we will discuss this fundamental aspect.
Introduction of me as a lecturer
Before we get to the lecture, I would like to introduce myself with this slide.
Full information about my Education and Experience is available here:
What is the definition of data?
Let’s start with the definitions.
Data - information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer [1]
As a reminder, here is a definition of the term MarTech:
MarTech applies to major initiatives, efforts, and tools that harness technology to achieve marketing goals and objectives. [2]
With the emergence of the available customer data, marketers became able to enhance customer-centric marketing strategies.
The following table defines the benefits and the drawbacks of data use in MarTech.
Data Use Benefits | Data Use Drawbacks |
receive personalised recommendations that are created to speed up our decision-making | ethical aspects |
website adaptation based on our prior activity | privacy concerns |
individualized selection based on our preferences and forecasted preferences | data resales |
etc. | misleading corrupted databases |
| unregulated disrupted legislation |
| etc |
Structured VS Unstructured Data
There are numerous categorisations of data types. However, I would like to introduce the core differentiation – structured and unstructured data.
Structured data is data that has a standardized format for efficient access by software and humans alike. It is typically tabular with rows and columns that clearly define data attributes. [3] Examples: Excel files; SQL databases; Point-of-sale data; Web form results; Search engine optimization (SEO) tags; Product directories; Inventory control; Reservation systems
Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in non-relational (NoSQL) databases. [4] Examples: sensors, text files, media - audio and video files, email messages, word-processing documents, PDF files
Both types of data are used in MarTech for various purposes; we will discuss the applications in one of the future posts!
I hope it was useful! Do not hesitate to share this article with anyone you consider to be interested in this topic.
If you wish to learn more, you can follow the links:
Explore the MarTech field
Learn about MarTech: Attend my Open Online MarTech Lectures
Talk about MarTech: Participate in the “MarTalks” – my open speaking/discussion club
Practice for your MarTech communications: Complete my Free English Exercises for Marketers
Excel in your MarTech career
Learn about your MarTech career possibilities:
Explore the specificities of the developing MarTech field by going through my guide – The MarTech Guide
Ask me directly – register for a consultation with me to learn about the MarTech career trajectories, required skills and examples of the tasks
Improve your English for marketing purposes
Register for the Individual consultation or lessons based on your request (ex. presentation rehearsal, preparation for negotiations, MarTech terms and vocabulary enrichment)
Register to develop your individual learning programme for a chosen career path in MarTech
Was this post useful for you?
Yes
No
I already knew all of this
Sources:
[1] Definition of data from the Cambridge Advanced Learner's Dictionary & Thesaurus
[2] Brinker Scott, "Hacking Marketing: Agile Practices to Make Marketing Smarter, Faster, and More Innovative", Wiley; 1st edition (March 4, 2016)
[3] Amazon Web Services (AWS), What is Structured Data? https://aws.amazon.com/what-is/structured-data/
[4] IBM. Structured versus unstructured data: What's the difference? https://www.ibm.com/think/topics/structured-vs-unstructured-data
Comments