Smarter Data Science. Cole StrykerЧитать онлайн книгу.

      Figure 1-4: Ends and means model

      The ends and means model can be iterated upon to adjust to continuous change and continuous course correction to drive toward sustained and improved relevance.

      If an organization can combine relevance with uniqueness, then the path forward may offer greater opportunities, as the organization is unlikely to be viewed as another commodity player in a given space.

      Formulating the ends is potentially more difficult than formulating the means. Words that are substitutes for means include transformation (including digital transformation) and disruption. An organization wanting to transform might simply do so by focusing on select technologies or capabilities that are newer; for example, cloud computing, agile development, big data, consumer-oriented platforms, blockchain, analytics, and even AI. Regardless of which technologies and capabilities are picked as a means, the question would remain: but, to what ends?

      Relevance can be more difficult to articulate in terms of an ends, especially in the light of newer technologies and capabilities. The struggle with articulation might result from subject-matter experts and end users having minimal insight or experience with how a new solution can actually be positioned and leveraged.

Disruption can be viewed through its two adjectives: disruptive and disrupted. A disruptive organization is more likely to garner a leadership position in handling change, while a disrupted organization is likely to have a lagging position. For example, traditional publishing companies and journalists were found flat-footed in the wake of the digital publishing era. For more than a decade, profits and viability have dwindled in traditional publishing due to social media, blogs, and the proliferation of web-based content.

      Another example of a business type in the midst of being disrupted is the hospital. In the United States, the population size has grown by approximately 100 million people in the past 35 years and by nearly 200 million people in the past 70 years. It would not be unreasonable to assume that the demand for hospital beds has steadily risen alongside population growth, especially with an aging population. However, the demand for hospital beds is approximately the same now as it was in the 1940s. In the United States, the need for hospital beds peaked in the early 1980s.

      Not all that long ago, a hospital was not exactly the safest of places to go and get well. In one account from the 1800s, it was noted that “hospitals are the sinks of human life” (www.archive.org/stream/proceedingsconn08socigoog). Through the use of anesthesia and the adoption of sterilization techniques, along with the advent of X-rays in 1895, hospitals turned a corner from being a highly risky place to get treated. But now, in a throwback to the 18th and 19th centuries, hospitals are once again seen as a less-than-desirable place to receive therapeutic medical treatment, as cited in a CDC report on hospital-acquired infections (www.documentcloud.org/documents/701516-cdc-hai-infections-deaths.html).

      Facilities currently challenging the traditional hospital include walk-in urgent care centers, imaging facilities, surgical centers, physician offices, and so on. Hospitals are being forced to consider mergers and acquisitions as well as downsizing. Hospitals are being disrupted and need to seek nontraditional ways to remain relevant. Could the use of advanced analytics be part of the approach?

      Ultimately, if AI is going to augment human intelligence, AI will be part of the means to transform or disrupt. While AI can potentially hypothesize about what can be relevant, AI is likely going to be challenged to convey a de facto direction as what needs to be done to remain relevant. For AI and for humans, collaboration is an augmented opportunity to address a defining issue of our times: relevance.

      Therefore, the sole purpose of an information architecture for AI can be postulated, as an aid in the transformation and disruption of an organization that is on a ladder to achieve sustained or regained relevance, whereby each point of leverage is based on data and the organization is willing and capable of harnessing the insights that can be derived from that data.

      ECONOMICALLY VIABLE

      Without data there's no AI. Period. AI works well because organizations now have the means to economically collect and hoard immense quantities of digital information. Augmenting our work with machines and AI is the norm.

For example, when was the last time you went a day without Googling? Chances are, you don't give machine learning a second thought when you're interacting with AI-infused capabilities. We are augmenting our work with AI, and in large part, it feels totally natural.

      Organizations still have a long way to go to fully realize how they can augment all of their processes with AI. For companies that are successful, the result should feel natural.

Data-Based Reasoning Is Part and Parcel in the Modern Business

      Advanced analytics, including AI, can provide a basis for establishing reasoning by using inductive and deductive techniques. Being able to interpret user interactions as a series of signals can allow a system to offer content that is appropriate for the user's context in real time.

      To maximize the usefulness of the content, the data should be of an appropriate level of quality, appropriately structured or tagged, and, as appropriate, correlated with information from disparate systems and processes. Ascertaining a user's context is also an analytical task and involves the system trying to understand the relationship between the user and the user's specific work task.

      For an industrial-based business application, a user might have a need to uncover parts and tools that are required to complete maintenance on a hydraulic system. By using adaptive pattern-recognition software to help mine a reference manual about hydraulic systems and their repair, a system could derive a list of requisite tools and related parts. An advanced analytic search on hydraulic repair could present content that is dynamically generated and based on product relationships and correlated with any relevant company offerings.

      Pulling content and understanding context is not arbitrary or random. Aligning and harmonizing data across an enterprise or ecosystem from various front-end, mid-end, and back-end systems takes planning, and one of the results of that planning is an information architecture.

      Advances in computer processing power and the willingness for organizations to scale up their environments has significantly contributed to capabilities such as AI to be seen as both essential and viable. The ability to harness improved horsepower (e.g., faster computer chips) has made autonomous vehicles technologically feasible even with the required volume of real-time data. Speech recognition has become reliable and is able to differentiate between speakers, all without extensive speaker-dependent training sessions.

There is no hiding that AI can be a complex subject. However, much of the complexity associated with AI can be hidden from a user. While AI itself is not a black art, AI benefits when traditional IT activities such as data quality and data governance are retained and mastered. In fact, clean, well-organized, and managed data—whether