Smarter Data Science. Cole Stryker
Читать онлайн книгу.
rung:
Collecting data with a common SQL engine, the use of APIs for NoSQL access, and support for data virtualization across a broad ecosystem of data that can be referred to as a data estate
Deploying data warehouses, data lakes, and other analytical-based repositories with always-on resiliency and scalability
Scaling with real-time data ingestion and advanced analytics simultaneously
Storing or extracting all types of business data whether structured, semistructured, or unstructured
Optimizing collections with AI that may include graph databases, Python, machine learning SQL, and confidence-based queries
Tapping into open source data stores that may include technologies such as MongoDB, Cloudera, PostgreSQL, Cloudant, or Parquet
The Organize rung infers that there is a need to create a trusted data foundation. The trusted data foundation must, at a minimum, catalog what is knowable to your organization. All forms of analytics are highly dependent upon digital assets. What assets are digitized forms the basis for what an organization can reasonably know: the corpus of the business is the basis for the organizational universe of discourse—the totality of what is knowable through digitized assets.
Having data that is business-ready for analytics is foundational to the data being business-ready for AI, but simply having access to data does not infer that the data is prepared for AI use cases. Bad data can paralyze AI and misguide any process that consumes output from an AI model. To organize, organizations must develop the disciplines to integrate, cleanse, curate, secure, catalog, and govern the full lifecycle of their data.
These are key themes included in the Organize rung:
Cleansing, integrating, and cataloging all types of data, regardless of where the data originates
Automating virtual data pipelines that can support and provide for self-service analytics
Ensuring data governance and data lineage for the data, even across multiple clouds
Deploying self-service data lakes with persona-based experiences that provide for personalization
Gaining a 360-degree view by combing business-ready views from multicloud repositories of data
Streamlining data privacy, data policy, and compliance controls
The Analyze rung incorporates essential business and planning analytics capabilities that are key for achieving sustained success with AI. The Analyze rung further encapsulates the capabilities needed to build, deploy, and manage AI models within an integrated organizational technology portfolio.
These are key themes included in the Analyze rung:
Preparing data for use with AI models; building, running, and managing AI models within a unified experience
Lowering the required skill levels to build an AI model with automated AI generation
Applying predictive, prescriptive, and statistical analysis
Allowing users to choose their own open source frameworks to develop AI models
Continuously evolving models based upon accuracy analytics and quality controls
Detecting bias and ensuring linear decision explanations and adhering to compliance
Infuse is a discipline involving the integration of AI into a meaningful business function. While many organizations are able to create useful AI models, they are rapidly forced to address operational challenges to achieve sustained and viable business value. The Infuse rung of the ladder highlights the disciplines that must be mastered to achieve trust and transparency in model-recommended decisions, explain decisions, detect untoward bias or ensure fairness, and provide a sufficient data trail for auditing. The Infuse rung seeks to operationalize AI use cases by addressing a time-to-value continuum.
These are key themes included in the Infuse rung:
Improving the time to value with prebuilt AI applications for common use cases such as customer service and financial planning or bespoke AI applications for specialized use cases such as transportation logistics
Optimizing knowledge work and business processes
Employing AI-assisted business intelligence and data visualization
Automating planning, budgeting, and forecasting analytics
Customizing with industry-aligned AI-driven frameworks
Innovating with new business models that are intelligently powered through the use of AI
Once each rung is mastered to the degree that new efforts are repeating prior patterns and that the efforts are not considered bespoke or deemed to require heroic efforts, the organization can earnestly act on its efforts toward a future state. The pinnacle of the ladder, the journey to AI, is to constantly modernize: to essentially reinvent oneself at will. The Modernize rung is simply an attained future state of being. But once reached, this state becomes the organizational current state. Upon reaching the pinnacle, dynamic organizations will begin the ladder's journey anew. This cycle is depicted in Figures 1-2 and 1-3.
Figure 1-2: The ladder is part of a repetitive climb to continual improvement and adaptation.
Figure 1-3: Current state ⇦ future state ⇦ current state
These are key themes included in the Modernize rung:
Deploying a multicloud information architecture for AI
Leveraging a uniform platform of choice across any private or public cloud
Virtualizing data as a means of collecting data regardless of where the data is sourced
Using DataOps and MLOps to establish trusted virtual data pipelines for self-service
Using unified data and AI cloud services that are open and easily extensible
Scaling dynamically and in real time to accommodate changing needs
Modernize refers to an ability to upgrade or update or, more specifically, to include net-new business capabilities or offerings resulting from transformational ideas or innovation that harness reimagined business models. The infrastructural underpinnings for organizations that are modernizing are likely to include elastic environments that embrace a multicloud topology. Given the dynamic nature of AI, modernizing an organization means building a flexible information architecture to constantly demonstrate relevance.
THE BIG PICTURE
In agile development, an epic is used to describe a user story that is considered far too big to be addressed in a single iteration or a single sprint. Therefore, an epic is used to provide the “big picture.” The big picture provides an end-to-end perspective for what needs to be accomplished. The epic can then be decomposed into a series of workable stories that can be worked on. The epic serves to ensure the stories are threaded appropriately.
In the AI Ladder, the ladder represents the “big picture.” The decomposition is represented by rungs. The ladder is used to ensure that the concepts for each rung—collect, organize, analyze, infuse—are threaded appropriately to ensure