Playing with Patterns: Data Science for Digitising Events
Updated: Sep 28
The future of events will unfold in the digital arena, where terabytes of data lie scattered, awaiting discerning minds to mould them into meaningful patterns.
In an increasingly digital world, we're witnessing an unprecedented explosion of data. According to a study by IDC, the total volume of data created, captured, copied, and consumed worldwide is forecasted to grow from 64.2 zettabytes in 2020 to an astonishing 180 zettabytes by 2025. This data deluge is especially evident in digital events– webinars, virtual conferences, or online workshops. Each interaction within these events – a sign-in, a click, a download – contributes to these waves upon waves of data.
On one hand, it presents an opportunity, an untapped vein of insights waiting to be discovered. On the other, it poses a significant challenge for data specialists, tech teams, and management personnel: How can we make sense of this vast and varied data to draw meaningful conclusions and deliver actionable insights? The answer lies not in the traditional confines of structured data analysis, but in the playful and creative realm of pattern recognition, a skill long associated with human intelligence and creativity.
In Digitising Events, this means tapping into participant behaviour and interactions to plug those gaps in our operations and empowering ourselves to respond to emerging trends. With such a skill, we address questions like: Will this webinar have an interactive audience? What is the likelihood of a sponsor having a positive experience? Which participants will have a positive return on time invested?
So, how do we cultivate this skill? Let’s dive right in!
Data as New Language for Digitising Events
We've long understood data in the conventional sense, a stream of ones and zeros, a monolithic entity to be processed, organised, and analysed. Traditionally, businesses have approached this challenge with more resources, throwing computing power and storage at the problem. But this approach is akin to building higher walls of sand to keep an advancing tide at bay. As the rate of data creation continues to accelerate, we can see the futility of this strategy as value is created not by organising the data but by mining it for insight.
How do we find the golden insights hidden in Digitised Events in a world drowning in data?
Instead, we propose a paradigm shift: we must treat data not as an onslaught to be defended against, but as a goldmine to be excavated. Digital events, by their very nature, generate a diverse array of data. This can be classified broadly into structured, unstructured, and semi-structured data.
Structured data is information with a high degree of organisation, readily searchable by straightforward algorithms. In the context of digital events, this might include data like registration forms and post-event surveys, which fit neatly into predefined fields. This data is typically collected via event management systems and stored in databases, ready for analysis.
Unstructured data, on the other hand, lacks a pre-defined data model or is not organised in a predefined manner. This type of data is typically text-heavy and may contain dates, numbers, and facts. Examples from digital events might include social media posts, video and audio recordings of sessions, and discussion threads. This data is often captured and stored using data lakes or object storage, then should be processed using tools like natural language processing (NLP) to extract valuable insights.
Finally, semi-structured data contains aspects of both. It's not as rigid as structured data, but it contains tags or other markers to enforce hierarchical groups of records and fields within the data. Examples include XML, JSON files, or email data. In digital events, chat logs, email communications, questions at sessions or user-generated tags could fall into this category.
Collectively, these different types of data provide a rich tapestry of information about participant behaviour and interest at digital events. They offer a wealth of insights, from preferences and interaction patterns to feedback and discussion themes, all of which hold significant value in understanding and enhancing the participant experience. This diversity and richness underline why it's crucial to shift our perspective and view data as valuable building blocks rather than an overwhelming barrage.
Instead of focusing on taming this data tsunami, we focus on learning its language, understanding its nuances, and ultimately unveiling the intricate patterns hidden within. This shift in perspective doesn't negate the complexities of data; instead, it equips us with a more effective way to approach and derive value from it.
Data Science: The Rulebook of the Game
As we continue playing with patterns, it's time to decipher the rulebook. Data science is our guide, providing the principles and methodologies to navigate the labyrinth of data. For our seasoned players, this realm is not unfamiliar. Still, we aim to illuminate its significance in the specific context of Digitising Events – how honing our pattern recognition skills gives our business predictability, stability and actionable insight before it's too late.
Treat data not as a relentless storm but as a treasure trove of signals. The future of events hinges on this shift in perspective.
The analytics process can be thought of as four interrelated stages, each serving a distinct purpose: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics answers the 'what happened' question. It involves examining historical data to identify patterns and trends. In Digitising Events, it might mean analysing the number of attendees, the average session duration, or the most popular features.
Moving a step further, diagnostic analytics investigates 'why did it happen'. It identifies correlations and patterns to determine causality. For instance, did a specific keynote speaker result in higher attendance? Did certain content consistently underperform? Or was a technical glitch responsible for participant drop-offs?
Predictive analytics, as the name suggests, is all about forecasting 'what might happen'. Using machine learning and statistical algorithms it allows us to predict future behaviour based on past data. In Digitising Events it can predict trends like participant engagement levels, popular topics, or potential technical issues. This is where the real power of pattern recognition comes into play, allowing us to construct predictive models that guide audience acquisition and investment that is relevant and actionable.
Building on these predictive models, prescriptive analytics offers advice on 'what should we do'. By simulating different scenarios, it provides recommendations on the optimal course of action. For example, it could suggest the best time to schedule an event to maximise attendance, the best format for a given IAEK outcome, and the optimum time frame to activate a key channel.
Recognizing these different types of analytics allows us to tailor our approach depending on our needs, whether we're looking to understand past performance, diagnose service design issues, predict future trends, or determine strategic actions. The ability to turn raw data into actionable insights, informed decisions, and leveraged opportunities drives the overall success of Digitised Events, embodying the essence of business sanity. So, how do we put this data science into action?
Data Science and Digitising Events in Practice
Stepping away from individual tools and techniques, which we already elaborated on in our previous article, let's examine the unique interplay of these capabilities in the context of Digitising Events.
Seeing strategies unfold in the real world adds a layer of practicality to our game of pattern recognition. Let's consider two case studies showcasing how these data science principles, tools, and techniques have been creatively applied to digital events.
Beyond the numbers and algorithms, data tells a story—a narrative of patterns, behaviours, and possibilities critical for the advancement of Digitised Events.
Case Study 1: Maximising Engagement at a Virtual Conference
A global tech corporation was looking to create an event based on the principles of knowledge exchange. It knew that the old way of best guess sessions and solutions trails had seen diminishing returns on attendance and engagement.
It was able to collate behavioural data from its knowledge base, participant questions from webinars, and structured data about customers appended to prioritisation scorecards to rapidly develop a live content matrix as a scaffold for the event.
Its subject matter experts were then challenged to write solutions and knowledge paragraphs aligned to this live matrix. These were then put through a large language model to generate session abstracts for the event.
The company went one step further as the session abstracts were published; they were continually augmented based on behaviour interactions to improve and fine-tune them.
Utilising real-time analytics, they monitored participant interactions throughout the event, quickly identifying popular sessions and high-traffic periods. Simultaneously, machine learning algorithms analysed this real-time data, identifying patterns and feeding these back to a presenter dashboard for them to incorporate into upcoming sessions.
Additionally, using NLP tools on chat logs in their app and discussion threads, they identified hot topics and common queries. This allowed the organisers to dynamically adjust session content and FAQs, providing real-time responses to attendees interests and concerns. As a result, they achieved record engagement levels and highly positive attendee feedback.
Case Study 2: Optimising Information Exchange in “The 150 in 2”
"The 150 in 2" presents a unique challenge: to create a significant shift in domain understanding, information exchange, and actionable outcomes in a condensed, four-hour online event for 150 people instead of a 2-day in-person event. With such a concentrated timeframe and audience size, every interaction counts, making data science crucial for optimisation.
To maximise domain understanding, the organisers could leverage NLP tools in the pre-event stage. By analysing user profiles, past participation data, and survey responses, the system could identify key areas of interest, knowledge gaps, and potential connections between participants. This information would allow the organisers to tailor the event content, ensuring it is highly relevant and enriching for the participants.
During the event, real-time analytics would come into play, monitoring participant interaction, engagement levels, and feedback in real-time. This data would feed into predictive models that forecast participant behaviour, enabling organisers to adjust the event flow dynamically. For instance, if a particular discussion generates high engagement, the schedule could be tweaked to extend that session, maximising the value for participants.
To facilitate impactful information exchange, data science could be used to create 'dynamic clusters' of participants. By analysing real-time interaction data, the system could identify participants with synergistic interests or complementary expertise, suggesting breakout groups or discussion threads.
Post-event, the organisers could use prescriptive analytics to gauge the event's effectiveness. By analysing participant feedback, session engagement data, and the post-event survey responses, they could gain insights into what worked well and what could be improved, informing strategy for future events.
This strategic application of data science, tailored to the unique format of "The 150 in 2", demonstrates how analytics can optimise the event experience, drive understanding, facilitate information exchange, and produce actionable outcomes even in a condensed time frame.
The case studies show how building application-specific data models and harnessing data science in innovative ways can truly revolutionise the digital event experience, paving the way for more engaging, impactful, and efficient Digitising Events.
The Power of Playing with Patterns for Digitised Events
As we draw this exploration to a close, let's revisit the challenge we started with the monumental task of making sense of the vast, varied, and rapid-fire data generated by digital events. We've journeyed from the traditional view of data as an intimidating deluge to a new perspective where we see data as an intricate puzzle waiting to be solved.
In this game of "playing with patterns", we've learned to view data science not merely as a technical discipline but as a creative act. We've examined how different types of data generated in digital events serve as fundamental building blocks and how the tools and techniques of data science act as a rulebook for our game. The case studies illuminated how these principles can be applied to real-world scenarios, transforming digital event experiences.
Embracing this perspective of data science can lead to invaluable insights, more effective strategies, and enhanced participant experiences in Digitised Events. As we continue to explore this realm, remember that data is not just numbers and facts, it's a story waiting to be told, a pattern waiting to be discovered. And the keys to unlocking these patterns lie in the strategic application of data science.
As we venture into the future, with its exciting advancements and emerging trends, remember this journey. Remember the joy of playing with patterns, and let it guide our approach to data science, illuminating our path forward in the world of Digitising Events.