What are the basics you should know about this emerging field? This article outlines what content data science is all about, covering:
Author and entrepreneur Peter Hinssen has recently popularized the term content science, which he defines as “the management of a company’s unstructured data paired with the vast potential of LLM platforms.” He uses the term ‘content’ to refer to unstructured documents or ‘unstructured data’ — such as digital documents, slideshow presentations, emails, images, etc.
This relatively new field, enabled by the rapidly accelerating capabilities of AI, is particularly exciting because the vast majority — a full 90%, according to an IDC white paper — of most organizations’ data is unstructured. This data is found in a wide variety of content like marketing materials, internal documentation, instruction manuals, images, and videos, to name just a few examples.
Whereas traditional data science primarily involves working with structured data neatly organized in databases and spreadsheets, similar principles can be applied to unstructured data found in content. For that reason, we prefer to think of this extended application of these principles as content data science.
To understand the business value of content data science, it helps to compare its overall purpose to that of traditional data science in an enterprise setting. While both disciplines are focused on deriving actionable insights, the types of insights and their applications differ significantly. Here is a breakdown of these differences:
| Traditional Data Science | Content Data Science | |
| Insights | Quantitative snapshots explaining “what is happening.” | Qualitative and quantitative pictures explaining "why something is happening." |
| Objective |
Support business decision-making. For example:
|
Improve the overall effectiveness of content. For example:
|
Extracting valuable insights from structured data is not easy. Extracting them from unstructured content data is even more difficult because it involves more complexities that traditional data science methodologies often fall short in addressing. Top challenges include:
Artificial intelligence plays a crucial role in content data science. By discerning patterns from extremely large datasets of unstructured data, large language models (LLMs) can extract value from such content through:
AI algorithms, particularly those based on NLP, are essential for analyzing unstructured content such as text documents, social media posts, and customer reviews. These algorithms can identify correlations, extract key themes, and understand sentiment, providing valuable insights into customer behavior, market trends, and content performance.
One of the most promising applications of AI in content data science is the concept of "grafting." This involves combining the power of LLMs with a company's own internal content, creating custom AI tools that are uniquely tailored to the organization's knowledge base.
It is worth noting that insights gained through AI-enabled content data science can form a natural foundation for developing future content with generative AI (GenAI) tools. For example, the creation of blog articles, social media posts, and other forms of marketing content — even dynamic email campaigns that adapt to user interactions — is one of the most widely recognized categories of use cases for GenAI. Also, AI can help govern content to ensure adherence to brand guidelines, legal requirements, and ethical standards.
While traditional data science and content data science are distinct fields, they are rarely practiced independently. For example, a common technical goal of content data scientists is to bring structure to unstructured data using techniques such as retrieval-augmented generation (RAG) and knowledge graphs. These tools can help reduce the amount of training needed for LLMs and lower the cost of AI development.
Also, content data scientists may rely on structured data to inform how they will structure or curate unstructured data.
To illustrate the business value potential of these synergies in marketing and advertising, consider a hypothetical example of a CPG company that has launched a digital advertising campaign for a new product:
Marketing and advertising are, however, far from being the only business functions where synergies between traditional and content data science can add value. Here are a few more hypothetical examples:
Using traditional data science methodologies, analysis of a telecommunications company’s customer service call logs reveals that customers calling about a specific new phone model have a significantly higher rate of unresolved issues, leading to increased call volume.
Content data science methodologies are then applied to investigate why call volume is up. This process involves analyzing the content of online support resources and the phone's user manual. The conclusion is that the instructions for setting up a particular feature are unclear or missing, leading to customer confusion and frustration.
A regular analysis of sales figures and online reviews for a new line of organic snacks reveals that sales are below projections and customer feedback frequently mentions a bland flavor profile.
Content data science practitioners, focusing on understanding consumer preferences and perceptions, analyze customer interviews and surveys to uncover the “why” behind the negative feedback. They discover that the target audience associates organic snacks with stronger, more distinctive flavors and that the product's current taste doesn't meet expectations. They also analyze the packaging and marketing materials to ensure that they effectively communicate the intended flavor profile and brand values.
Through analysis of employee engagement surveys and performance reviews, traditional data science practitioners identify a significant drop in employee satisfaction and productivity within a specific department following the implementation of a new project management system.
Content data science practitioners then explore the “why” behind this trend by analyzing recordings of interviews between employees and managers, training materials, and digital communication patterns within the department. They find that the training materials for the new system are inadequate, leading to a lack of understanding and adoption among employees. They go on to investigate whether communication channels are effectively facilitating collaboration and knowledge sharing within the team.
Analysis of structured data related to a manufacturing company's environmental impact reveals that water usage has increased significantly over the past year, exceeding targets set in the company's sustainability plan.
Analysis of unstructured data from documentation related to operational processes, maintenance records, and employee training materials reveals that a new manufacturing process — while intended to be more efficient — is using more water than the previous method due to a miscalculation in its design.
By embracing a comprehensive approach to content data science — which relies on AI to make sense of unstructured data in a wide variety of content — organizations can move beyond just analyzing “what” is happening and understand “why” it is happening. The resulting insights can lead to better decision-making and a stronger competitive edge across multiple business functions.
Taking this comprehensive approach to content data science involves sophisticated tools and techniques. In our next blog post, we will explore the most important of these tools and techniques and how Lingaro can help you make the most of them.