Business intelligence (BI) provides insights on four levels: descriptive (what happened), diagnostic (why did happen), predictive (what will happen) and prescriptive (how to make it happen). The most crucial point to remember is that each level builds on the one before it. For example, a prescriptive task to determine the pricing for a service or product which would improve sales by 15%, is contingent on accurate sales volume predictions, which are based on knowledge (diagnostic) of what factors influence sales and consumer purchasing decisions, which is contingent on access to previous sales numbers.
There is a challenge, however, with many intelligent mobile apps that prescribe actions to their users to reach some goals (for example, fitness, health or financial goals): they struggle with the second level, the diagnostic, and accordingly, their recommendations are not as effective as might be expected by the users. To "prescribe" training or diet regimes, a fitness or health app needs reliable diagnostic about why did a user achieve or miss their target. One way of providing this diagnostic is to rely on assumptions learned across multiple users which are then refined using user input via questionnaires. The problem with that technique is that it is tedious, and the users may be unaware of factors influencing their behavior and choices.
Another technique for providing a more reliable diagnostic of users is to collect a broader range of user interactions across different aspects of their lives and rely on these interactions to learn user-specific patterns and preferences. Such user-specific patterns would reveal insights into the user's lifestyle and commitments, which might then be used by an app to enhance Its diagnostic and provide more effective recommendations.
This is what the Information Digital Twin (IDT) is attempting to accomplish: observe user digital interactions, learn their specific routines, infer and classify their interaction preferences, and provide the user—and other apps, if desired—with a better insights Into the user's lifestyle and thus enable the users to achieving their goals more effectively. Our assumption is that many aspects of our lives are revealed through our digital interactions. Which app (or application) categories we use, how often, for how long, when, where, and in what order we use them—regardless of content—can reveal insights about a person's mood, lifestyle, and preferences, and enable accurate and personalized assumptions about why-type questions.
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