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Data strategies are becoming increasingly crucial for thought leadership programs. You need the data to prove your insights and create a sense of urgency to take action.

What is new: Great thought leadership relies on insightful data, and the shelf life for impactful data is shrinking rapidly.

Why it matters: Understanding the data you need and can develop or acquire plays a central role in thought leadership strategy and execution.

The data sets you need: Data is the numbers that give your thought leadership insights into life. The following categories serve as a starting point:

• quantifying the outcomes you can improve
• proof of advancements achieved with new technology
• putting numbers on customer opinions and perceptions
• sizing opportunities and future market potential
• numbers representing progress in market adoption and use of your offerings
• measure how well your offerings perform in their target environment

⛳️ The metrics for the opportunity you target, moving from an initial hypothesis via market validation to the key outcomes you aim to affect.

🖇️ Proofs of technology advancements, from the initial minimum viable products via prototypes to production products and software.

🗳️ Customer opinions and perceptions help you focus and prioritize. There is a delicate balance between quantifying customer wants and the underlying needs. You market towards wants, sell towards needs, and need both.

📈 For existing offerings, you want data on how and how much they are used and their actual performance during use—a gold mine for improving products and value propositions for specific customers and the market overall.

💎 Sizing of opportunities and markets builds on a mix of widely available statistics and assumptions about adoption potential, pace of growth, recurring revenues, and rough order of magnitude for price points.

🪜 The final category addresses key performance indicators to track how well your offerings gain market acceptance and ramp-up—target indicators representing new purchases and actual use.

📆 Shrinking shelflife: Data need to be fresh. In the past, we could create primary research projects and work with quarters for data capturing and adding months for data processing and publication.

⏲ The most attractive data for thought leadership today is hours or days old, and processing and publication add days to the timeline.

⏱ Data-driven worlds move faster. The value of insights grows when the data is fresh, and the sense of urgency grows with data presented in real time.

Data visualization: Translating raw data into insightful visuals is a nontrivial task. Masters of thought leadership excel at structuring visuals to appeal to visual, numeric, and word-triggered decision-makers.

🔎 A good base structure is highly flexible and allows you to zoom in on specific aspects in a visual. Each visual presented to a customer should deliver new insights and trigger some kind of reflection or action. If not, a double-click might be required.

⚠️ Avoid stacking numbers where context and insights are weak. A necessary step is to filter out the golden nuggets, as you otherwise can come across as reading from a list of phone numbers.

🔦💰 Capture or acquire: The data you collect is an essential asset in the markets and segments where you already play. What you already capture can be expanded to meet new data needs.

A data acquisition strategy is viable where the data you possess is insufficient, the cost of capture is high, or third-party objectivity is vital. It is the only option when you expand beyond your core markets.

❓🩺 Ask or measure: You can capture data in two different ways: surveys about future/current use or measurements of current use.

Surveys can capture user and customer opinions, preferences, and net promoter scores. Key success factors are your ability to articulate questions well and efficient working methods for recurring surveys.

⚖️ Measurements balance raw data capture and the models developed to turn data into insights. Expect the modeling work to be an essential part of your strategy.

🧹🧽 Validate and clean what you already have: The challenge with data you already have is cleaning it for efficient use by AI/ML tools.

Cleaning and validating data once is a common challenge in large enterprises. The most straightforward area to attack is consolidating openly published data that many teams or users will use in your data library. When all teams have incentives to contribute to one repository in one format, you have paved the way to making extracting data with generative AI easy.

An essential part of the validation is articulating a crisp context for each data point and its meaning. The context is what will make data useful for thought leadership initiatives.

📚 Data you can access and use: Data regulations play an important role in determining which data you can capture and how you can use it.

Some data can be captured and used to guide your decisions, but not for marketing purposes. The same applies to data you have acquired but for which you do not have redistribution rights.

💡 Data quantifying new opportunities: Putting numbers on novel opportunities is tricky. Expect asks to present proof points backing up even the first stages of an innovation.

👟👟👟The data sets you develop around innovations evolve from hypotheses to validated facts. Arrange the initial list above in an order representing how you expect your ability to create data. This list is a base for setting expectations for all key stakeholders.

📊 Some data, representing business outcomes, the impact of technology advancements, and market potential, is stable once quantified. Other areas, such as market adoption, performance in use, and customer opinions, can fluctuate with the impact of external factors. Innovations require frequent data capture and analysis to ensure strong alignment between market realities and your data sets.

🔼 Building bottom-up numbers: Large top-down numbers serve a purpose in the early stages of emerging opportunities. As you progress into developing thought leadership initiatives, the challenge moves to bottom-up validations of identified segments:

• quantification of market segments that build up a market
• quantification of outcomes that support the overall value proposition
• validated improvements in value propositions and business cases

⤴️ Data for interpreting inflection points: One area of high interest is determining the strength of an inflection point or a market shift. Disruptive innovation and hockey stick growth curves need constant monitoring.

• which leading indicators help us understand the disruption at hand?
• how do we best track each of them?
• which threshold values indicate an accelerated pace of transition?

🪩 For example, determining the strength of the Generative AI inflection point requires understanding factors such as the cost of training a new generation, the number of users, the user adoption rate, revenue growth for premium services, energy demand, and startup valuation.

Bottom line: Data brings evidence to your insights. With a clear data strategy, you can maintain the relevance and impact of your thought leadership initiative. Planning for a future where data updates happen in real-time will be mandatory for all forms of thought leadership.

Additional reading suggestions

• How to use data to fuel your thought leadership efforts [ARTICLE] – by Kristin Faulder, Forbes
• Prove it: Exactly how modern marketers earn trust [BOOK] – by Melanie Deziel
• Building a data-driven thought leadership plan [BLOGPOST] – by Laurie Heard, Move Digital Group