Dave Brock: Rudy, one of the fascinating things we've discussed is the concept of data as a journey. Can you explain what that means and how it helps us address the challenges we face?
Rudy Agovic: When we think about analytics, we need to understand the core of the data. Most enterprise data is not just rows of information; it represents experiences and journeys.
What is Data as a Journey?
For example, a customer goes through a journey when visiting a website or going through a sales cycle. Each step in the journey involves decision-making at intersections. So, a significant portion of enterprise data is actually journey-based.
Dave Brock: It's intriguing because we have different types of data across marketing, website visits, sales, CRM, ERP, and customer experiences. When we view them as part of a sequence or a time flow, we realize that they form a customer's journey, depicting their experiences with us and our interactions with them. It's a unique concept.
What Does the Journey Tell Us?
Rudy Agovic: Indeed, it gets even more interesting when we start connecting these journeys. For example, a marketing asset within a marketing campaign might intersect with a sales journey when a sales representative exposes it during a sales cycle.
The marketing asset might also appear on the website, connecting to the customer's web journey. The real value lies in interconnecting these journeys, creating richer and more meaningful data than a simple list of rows.
By exploring the relationships and patterns within these interconnected journeys, we can gain insights. For instance, we can analyze successful sales patterns and identify behavioral patterns that lead to success.
However, it's crucial to be selective about the journeys we capture. Dumping all CRM data into a single journey isn't efficient. Instead, we need to carefully consider the relevant steps in the sales journey for our analytics goals.
This process of identifying relevant journeys creates a highly valuable dataset for AI purposes. Even without AI, knowledge representation plays a foundational role in understanding the data and its relationships. So, in a way, it's already a part of AI.
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