Leading thought with data:
Recalculating thought leadership
23 October, 2024
By Natalie Bahmanyar
We’re halfway through the 2020s, and companies are irrevocably dependent on data and analytics to guide their business strategies. Sound data adds credibility and depth to opinions, enabling leaders to make more meaningful decisions. In communications, data is a trusted way to meaningfully engage with a variety of audiences, from employees to investors to clients. In short, people are much more likely to trust information that comes with verifiable evidence.1
Thought leadership specifically—the process of positioning oneself as an expert in the industry—is evolving with the growing availability of data. For example, authors can now tap into social media listening, data analytics, AI, qualitative and quantitative interviews, focus groups, ethnographies, pulse research, competitor research, published work, and, in addition, companies’ internal macro and micro data to craft insights that are both substantiated and impactful.
“This is important,” points out iResearch CEO Yogesh Shah, “because leveraging these diverse data sources allows thought leadership teams to offer deeper, evidence-based perspectives, enhancing their credibility and influence in the industry.”
The data’s in the details
Version 1: A company rolled out a new employee engagement program, and it resulted in improvement. Employees seemed more engaged in their work, and management noticed a positive shift in the office atmosphere. Overall, the company believed the program had a beneficial impact on the organization.
Version 2: A US-based data analytics firm launched an employee engagement program allowing 10% work time for passion projects, aligning with its “curiosity-driven growth” value. After six months, employee satisfaction rose 32% (per pulse surveys), while productivity increased 15% (measured by completed projects).
Which is more compelling? Need we ask?
And which version is more common in the documents, case studies, and other communications you see daily?
“Data, especially from senior professionals, adds immense value to thought leadership content,” says Ashok Iyer, associate director of Market Research Operations at iResearch, “elevating it from mere opinion to expert-backed fact.” Of course, the “expert” part is key: data alone is just numbers, but paired with qualitative insight, it becomes empirical evidence, allowing companies to build trust with their intended audiences by creating a bridge between opinions and facts.
Moreover, data can forecast the future in increasingly sophisticated ways by serving as “a tabular representation of the truth,” in the words of Bang Analytics Director Yiannis Antypas. “To plan ahead, you need a clear understanding of your current position. Data provides that foundation—a structured view of reality. While no dataset is perfect, this perspective offers our best chance to identify where we stand and to spot emerging trends and patterns.”
Data experts are quick to caution that it’s important to let data shape the narratives, rather than the other way around. According to Yiannis, data can show us “what we are meant to say, rather than what we want to say.” In this way, data-driven thought leadership maintains its integrity by allowing empirical evidence to guide insights and conclusions, rather than succumbing to the temptation to cherry-pick statistics in support of preconceived notions.
A foundational how-to
Here are our recommendations, broken down into practical steps:
Clarify your audience
Seems pretty self-explanatory, right? But it’s worth reiterating that, before you can properly assess any data, you need to know who it’s for, what they’re interested in, and what they expect from you. Figure out your audience, and you’ll be able to home in on what data to prioritize and how to use it to make your point most effectively.
Choose your data wisely
Most people don’t intentionally manipulate data—but it can be easy to fall prey to common data pitfalls. According to Yiannis, your mantra when vetting data should be to “challenge everything,” beginning with manageable datasets that the company can trust and gradually expanding into more innovative and diverse sources. “The simplest and most actionable data often comes from within the company; thought leadership reports, internal webinars, annual reports, or even timesheets can all be untapped resources. Structuring this data turns it into a valuable asset. Once internal sources are optimized, the logical next step is to move to external datasets like LinkedIn, where individual profiles reveal organic trends across sectors and geographies.”
Shape a clear story
Your data may be gold, but according to research published in Cognitive Research, “a substantial body of work has shown that people are more persuaded by anecdotal than statistical evidence.” That means you can’t forget the importance of telling a good story. Narratives reinforce warmth and connection, while data and statistics reinforce competence. Use both to your advantage.
Visualize data
Just as people connect to stories, they also connect to images—and we process data much faster when it’s visualized. Needless to say (but we’re going to anyway), visualization tools such as Tableau and Power BI can be incredibly helpful when transforming complex datasets into accessible insights. Even Excel and PowerPoint, wielded responsibly, can work visual wonders for early drafts. (By the time you publish, make sure everything is labeled and the chart looks publication-grade.)
Create longitudinal studies
By tracking data over time, you can observe changes and trends that may not be visible in a snapshot analysis. Both Yiannis and Ashok agree that it is “imperative” to maintain longitudinal data, and they highlight the importance of consistency in the scope of your data collection. “Consistent data collection, integrity of historical data, and standardized metrics ensure meaningful year-over-year comparisons,” says Yiannis.
Build a data-driven culture
“No source is flawless,” Yiannis observes, but Ashok notes that periodic checks to review inconsistencies, outliers, and the reliability of sources can help iron out issues. Ultimately, companies that want to sustain a commitment to high-quality data will need to undergo a longer-term culture change where data is the fundamental foundation for strategy and process. Investing in data literacy at all levels of the organization, integrating data into routine processes and planning, and encouraging experimentation can help foster a culture where testing hypotheses is the norm.
Data-driven thought leadership in action:
Learning from leaders
You know an organization is good at what it does when other leaders look to it for motivation. And while some companies and leaders are new to the process, there are several organizations that have so successfully integrated data into their thought leadership strategies that they are effectively known as the thought leaders of thought leadership—at least according to our experts.
McKinsey and Gartner, for instance, are often cited as pioneers in data-driven thought leadership, using comprehensive datasets to support their insights on industry trends and future developments. Take a look at McKinsey’s “How six companies are using technology and data to transform themselves” report to see what I mean. These organizations provide a valuable template for how brands can use data to enhance their strategic messaging.
Moreover, innovative data journalists such as Kevin Quealy at The New York Times demonstrate the power of data-driven storytelling, such as an interactive visualization on how race impacts social mobility. (If you’re like me, you could spend hours watching the data scroll and morph, walking through new research on the stubborn wealth gap between white and Black men—even those raised in rich households.)
Leaning into AI
It’s 2024, and no blog on data could stand without mentioning AI and large language models (LLMs). “AI has been transforming data for years,” reflects Yiannis, “but now, with the rise of LLMs like GPT-4, we’re seeing an exponential leap.”
LLMs now allow for the automation of complex tasks, such as classifying vast amounts of data, in ways they previously could not. Generative AI is helping to “[solve] the scale issue, helping us to efficiently analyze” large datasets that exist everywhere from articles to LinkedIn posts, Yiannis says. “This ‘army of digital interns’ is only getting smarter, presenting exciting new opportunities for the future of data analysis.”
As tools such as ChatGPT become more sophisticated, companies will find new ways to automate data analysis and enhance the storytelling process. This development will shorten the time needed to produce impactful thought leadership, while allowing for greater personalization and relevance.
The future of data-driven thought leadership
As concerns about data privacy grow, thought leaders will need to adapt by prioritizing transparency and ethical data collection practices. “Data transparency is critical for the audience to understand how data is collected, used, and protected,” Ashok comments, noting both the financial and reputational risks for companies that fail to clarify sources and secure data.
Organizations that can harness data responsibly while providing meaningful insights will be better positioned to build trust with their audience and maintain a competitive edge in their industry.
Book a strategy call to explore the most powerful angles for your thought leadership.
