toolkit.
AI puts machine learning and deep learning into practice, and the resulting models can help organizations reason about AI's hypotheses and apply AI's findings. To embed AI in an organization, a formal data and analytics foundation must be recognized as a prerequisite.
By climbing the ladder (from one rung to the next: collect, organize, analyze, and infuse), organizations are afforded with the ability to address questions that were either previously unknown (When will a repeat buyer buy again?) or previously unanswerable (What were the influencing factors as to why a given product was purchased?).
When users can ask new questions, users can benefit from new insights. Insights are therefore a direct means to empowerment. Empowered users are likely to let specific queries execute for multiple minutes, and, in some cases, even hours, when immediate near-zero-second response is not fully required. The allure of the ladder and to achieve AI through a programmatic stepwise progression is the ability to ask more profound and higher-value questions.
The reward for the climb is to firmly establish a formal organizational discipline in the use of AI that is serving to help the modern organization remain relevant and competitive.
In the next chapter, we will build on the AI Ladder by examining considerations that impact the organization as a whole.
CHAPTER 2 Framing Part I: Considerations for Organizations Using AI
“We don't just pass along our DNA, we pass along our ideas.”
—Lisa Seacat DeLuca
TEDBlog
The use of artificial intelligence (AI) is not exclusively about technology, though AI cannot exist without it. Organizational motivation to adopt digital transformation is, in large part, being driven by AI. Arguably, the rate of successful AI initiatives is far less than the number of AI initiatives that are started. The gap is not centered on the choice of which AI algorithm to use. This is why AI is not just about the tech.
AI does not force its own organizational agenda. AI augments how an organization works, driving how people think and participate in the organization. Through tying together organizational goals with AI tools, organizations can align strategies that guide business models in the right direction. An organization augmented as a coherent unit is likely to achieve its digital goals and experience a positive impact from using AI.
As organizations realize value from the use of AI, business processes will see further remediation to operate efficiently with data as a direct result of AI-generated predictions, solutions, and augmented human decision-making.
From pressures that emanate from within the organization as well as those from the outside, the need to develop a balanced tactical and strategic approach to AI is required for addressing options and trade-offs. AI is a revolutionary capability, and during its incorporation, organizational action must not be seen as remaining conventional.
As a data scientist, you'll determine what types of inputs, or features, will be of benefit to your models. Whether determining which features to include (feature engineering) or which features to exclude (feature selection), this chapter will help you determine which features you'll need for the models that you develop. You'll also learn about the importance of organizing data and the purpose of democratizing data.
Data-Driven Decision-Making
The most advanced algorithms cannot overcome a lack of data. Organizations that seek to prosper from AI by acting upon its revelations must have access to sufficient and relevant data. But even if an organization possesses the data it requires, the organization does not automatically become data-driven. A data-driven organization must be able to place trust in the data that goes into an AI model, as well as trust the concluding data from the AI model. The organization then needs to act on that data rather than on intuition, prior experience, or longstanding business policies.
Practitioners often communicate something like the following sentiment:
[O]rganizations don't have the historical data required for the algorithms to extract patterns for robust predictions. For example, they'll bring us in to build a predictive maintenance solution for them, and then we'll find out that there are very few, if any, recorded failures. They expect AI to predict when there will be a failure, even though there are no examples to learn from.
From “Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action” by Sam Ransbotham, David Kiron, Philipp Gerbert, and Martin Reeves, September 06, 2017 ( sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence )
Even if an organization has a defined problem that could be solved by applying machine learning or deep learning algorithms, an absence of data can result in a negative experience if a model cannot be adequately trained. AI works through hidden neural layers without applying deterministic rules. Special attention needs to be paid as to how to trace the decision-making process in order to provide fairness and transparency with organizational and legal policies.
An issue arises as to how to know when it is appropriate to be data-driven. For many organizations, loose terms such as a system of record are qualitative signals that the data should be safe to use. In the absence of being able to apply a singular rule to grade data, other approaches must be considered. The primary interrogatives constitute a reasonable starting point to help gain insight for controlling all risk-based decisions associated with being a data-driven organization.
Using Interrogatives to Gain Insight
In Rudyard Kipling's 1902 book Just So Stories, the story of “The Elephant's Child” contains a poem that begins like this:
I keep six honest serving-men: (They taught me all I knew)
Their names are What and Where and When and How and Why and Who.
Kipling had codified the six primitive interrogatives of the English language. Collectively, these six words of inquiry—what, where, when, how, why, and who—can be regarded as a means to gain holistic insight into a given topic. It is why Kipling tells us, “They taught me all I knew.”
The interrogatives became a foundational aspect of John Zachman's seminal 1987 and 1992 papers: “A Framework for Information Systems Architecture” and “Extending and Formalizing the Framework for Information Systems Architecture.” Zachman correlated the interrogatives to a series of basic concepts that are of interest to an organization. While the actual sequence in which the interrogatives are presented is inconsequential and no one interrogative is more or less important than any of the others, Zachman typically used the following sequence: what, how, where, who, when, why.
What: The data or information the organization produces
How: A process or a function
Where: A location or communication network
Who:
