Get Ahead of the Debate Around Content vs Data

Content vs data?

There’s something to be said for both sides.

While content is subjective and open to interpretation, it adds essential context and turns data into truly intelligible information. Data, on the other hand, is vital for making intelligent and objective decisions. For example, it’s only by referring to historical data that we can accurately infer future possibilities without biases getting in the way. Data also adds credibility and ratifies the unchangeable nature of knowledge beyond the immediate context.

Yet, data can sometimes be misleading, and without sufficient data literacy, one risks coming to incorrect conclusions. Moreover, raw data is challenging to share and therefore cannot lead to widespread impact.

I wanted to discuss the debate around content vs data because we live in a time where enterprises are generating more data than ever before via their workforce, technology systems, IoT endpoints, and analytics. While there is a proliferation of content in both official and personal spaces, it is further aggravated by social media integration and democratised digital access to customers, employees and partners. As a result, it makes it challenging to formulate decision-making strategies that take both sides into account.

Understanding the Difference between Content and Data in an Enterprise

A typical organisation will deal with vast amounts of content and data every day, and there is a possibility of confusing one for the other.

Let’s take a simple example — imagine an enterprise runs an organisation-wide workforce engagement survey. One employee could have added a 300-word response to an open-ended question detailing their subjective experience. Meanwhile, “career opportunities” emerges as the most common key phrase across the organisation. In both cases, there is no quantifiable metric or a complex number attached to the findings. However, the former qualifies as content and the latter as data.

Enterprises must understand the difference between content and data without resorting to reductive definitions. For example, data has numbers that content doesn’t, or that data refers to indisputable facts that content doesn’t. Instead, the actual difference lies in context availability and how closely a piece of information is linked with the context at hand. Data can exist without context (and must necessarily do so), while content relies on a foundation of context to provide information. In many ways, this is an epistemological difference that significantly determines organisational change when data-driven decision-making is so widely adopted.

Scenarios Where the Data vs Content Debate Comes into Play

Besides the more significant import of the debate, you could be wondering if there are specific use cases where the question of data or content comes into play. Again, I would argue that such use cases exist across the organisation, in every functional unit, department, and team.

Scenario 1:

Data is a central part of the decision-making process in marketing, influencing nearly every action from when to send a marketing email and identifying keyword prioritisation. So it is by intelligent marketing data analytics, which is getting smarter every day. To measure the engagement numbers for your web page and pinpoint the exact page elements where customers are engaging and which ones are causing drop-off is now possible. But, again, it is the raw data, a reflection of customer perception of your webpage (not necessarily its quality).

You want to get to a meaningful action from this data — i.e., fine-tuning web page elements to increase engagement and drive conversion. Companies can make recommendations with scalability and power to process massive amounts of structured (e.g. news or content searches they prefer) and unstructured data (e.g. user viewing/listening patterns over OTT or Social mediums). Companies analyse user behaviour by consuming clicks and viewing customer journeys based on which they make the recommendations based on linking the user to a customer persona. However, over time, by leveraging machine learning and predictive analytics, the suggestions become better tailored to the user’s taste

While data gives you objective results about the current state, you need ancillary content to augment this information and guide the way forward. For instance, you might have to conduct A/B testing, observe user responses to different page versions, note these observations as content, and use this knowledge to inform your decision-making. One can use Content Analysis for making inferences regarding the antecedents of communication such as-

· Analysing the traits of individuals

· Inferring cultural aspects & change

· Providing legal & evaluative evidence

· Answering questions of disputed authorship

Scenario 2:

For IT departments, systems data is increasingly becoming the backbone of the decision-making process. Typically, data streams will pour in from various sources across infrastructure, internal/external services, and business units. To use this data meaningfully, you need support from next-gen systems like AIOps that can convert data into automated action independently and from the meaningful articulation of context through shareable content around systems data.

Log data is fundamental for many businesses and give a tremendous edge for powering their applications. Log management and analysis tools have been around long, but now it has become part of big data. Thus companies must harness data residing in their devices, systems and applications. The growth of digital business activities and transactions creates a mountain of log data. It can become a massive headache to be stored, processed, and presented in the most efficient, cost-effective manner.

The log search capabilities, driving insights from the same and extensive data management, has enabled organisations to discover insights for more agile operations. As a result, log analytics applications are now widely used for various business goals, for persoanlisation, service optimisation, system security and network performance besides learning market trends.

Content is often a creative process, while data is an observation process of human or object actions, and context is an influencing process or building patterns where content & data jointly come into play. Data precedes content; data analysis leads to the formation of information, and that creates content. When content precedes data, consumption generates patterns that are converted into knowledge. In my understanding, however, recommendations or insights are generated by building bridges and linkages between content and data sets.

And here lies the crux of the data vs content debate: while AI and other autonomous systems can make decisions based on raw or processed data, human choices are most effective when based on data-enriched content. As a species, human beings communicate via narratives, and the contextualised nature of content ensures that essential details and information is not left out when relaying data. Knowing this distinction could make or break organisational decisions in the age of data and content proliferation.

To know more about the ongoing contest between data and content as pertaining specifically to the information technology sector, you can download my white paper on this topic. You will also learn how data stories could significantly bridge this divide.

Please share your thoughts with me in the comments below or email me at Arvind@am-pmassociates.com.