Smarter Data Science. Cole Stryker

clear about their objectives or informational needs. Users may not necessarily know what to request.

      Consequently, in business, there is a need to question everything to gain understanding. Although it might seem that to “question everything” stymies progress in an endless loop (Figure 2-5), ironically to “question everything” opens up all possibilities to exploration, and this is where the aforementioned trust matrix can help guide the development of a line of inquiry. This is also why human salespeople, as a technique, will often engage a prospect in conversation about their overall needs, rather than outright asking them what they are looking for.

      Figure 2-5: Recognizing that the ability to skillfully ask questions is the root to insight

In Douglas Adams' The Hitchhiker's Guide to the Galaxy, when the answer to the ultimate question was met with a tad bit of disdain, the computer said, “I think the problem, to be quite honest with you, is that you've never actually known what the question is” (New York: Harmony Books, 1980). The computer then surmised that unless you fully come to grips with what you are asking, you will not always understand the answer. Being able to appropriately phrase a question (or query) is a topic that cannot be taken too lightly.

      Inserting AI into a process is going to be more effective when users know what they want and can also clearly articulate that want. As there are variations as to the type of an AI system and many classes of algorithms that comprise an AI system, the basis to answer variations in the quality of question is to first seek quality and organization in the data.

      However, data quality and data organization can seem out-of-place topics if an AI system is built to leverage many of its answers from unstructured data. For unstructured data that is textual—versus image, video, or audio—the data is typically in the form of text from pages, documents, comments, surveys, social media, and so on. But even nontextual data can yield text in the form of metadata, annotations, or tags via transcribing (in the case of audio) or annotating/tagging words or objects found in an image, as well as any other derivative information such as location, object sizes, time, etc. All types of unstructured data can still yield structured data from parameters associated with the source and the data's inherent context.

Social media data, for example, requires various additional data points to describe users, their posts, relationships, time of posts, location of posts, links, hashtags, and so on. This additional data is a form of metadata and is not characteristic of the typical meta-triad: business metadata, technical metadata, and operational metadata. While data associated with social media is regarded as unstructured data, there is still a need for an information architecture to manage the correlations between the core content: the unstructured data, along with the supporting content (the structured metadata). Taken in concert, the entire package of data can be used to shape patterns of interest.

      Even in the case of unsupervised machine learning (a class of application that derives signals from data that has not previously been predefined by a person), the programmer must still describe the data with attributes/features and values.

      QUESTIONING

      When questioning, consider using the interrogatives as a guide—what, how, where, who, when, and why. The approach can be used iteratively. You can frame a series of questions based on the interrogatives for a complete understanding, and as you receive answers, you can reapply the interrogatives to further drill down on the original answer. This can be iteratively repeated until you have sufficient detail.

Summary

      This chapter covered some of the organizational factors that help drive the need to establish an information architecture for AI. More broadly, an information architecture is also relevant for maximizing the benefit of all forms of analytics. The mind-set to think holistically was covered through the introduction of the six interrogatives of the English language—what, how, where, who, when, and why—over the time horizon of the past, present, and future.

      Through democratizing data and data science, an organization can elevate the impact of AI to where it can more unilaterally benefit the organization and its culture. Democratizing data and data science must be placed squarely in the context of each person's role and responsibility and would therefore require sufficient oversight to attain organizational objectives.

      While an information architecture can provide for efficiencies and flexibility, if the data is tied too closely to volatile business concepts, the effect can be too binding and stifle the rate of change that IT wants to deliver to the business.

Holistic thinking, democratization, AI, the use of an information architecture, etc., can all serve as a means for an organization to cut down on the time it needs to react. While organizations can plan for the changes they want to create, external factors and influences can hasten the time an organization has to respond. The better equipped an organization becomes with handling data and AI, the less reliance you'll need for responding with a gut feel to a situation.

      In understanding that different organizational roles and responsibilities require different lenses by which to undertake a particular business problem, due diligence would require intended responses to be sufficiently questioned.

      In the next chapter, we'll further explore aspects on framing concepts for preparing to work with data and AI.