Pages: 9

“Some people want it to happen, some wish it would happen, others make it happen.”

Pages: 27

This chapter aims to review, characterize, and comparatively analyze selected organizations analytics maturity models. Analytics maturity describes how deeply and effectively the organization uses tools, people, processes, and strategy to manage and analyze data for the purpose of informing business decisions. It indicates the actions that should be taken to develop the organization's analytics culture. In the data management area, the questions concern data quality, accessibility, and processing, as well as the company's data management method. Leaders in the United States often have a limited understanding of their core business. Innovators exhibit all the same characteristics as leaders with respect to strategy development. They are the top spenders for AI and analytics programs. They spend a significant portion of their budget on technology as well. They focus on creating an infrastructure that is elastic and rapidly scalable.

Pages: 20

In this chapter, the author discusses the experience of, AI and analytics use cases for, and lessons learned for explorers. Data technologies used by explorers mainly include operational client server or mainframe application systems. However, many explorers do not have such centralized systems on site. Explorers in manufacturing are beginning to investigate new techniques to improve product quality and reduce costs. Budgets are always a set of competing alternatives. There are hard numbers as well as politics associated with budgets. People are the key to any data-driven success. Explorers are just getting started with AI and analytics, and they show interest in investigating and moving to the next level. Their venture into analytics tends to be PC based, using spreadsheets and databases, but they are also exploiting the power of add-ins for simulation, forecasting, and visual BI.

Pages: 26

In this chapter, the authors discuss experiences, AI and analytics use cases, and lessons learned by novices. Novices are entering the next level of analytics maturity. The typical novice is an organization that has been collecting data for years. Data technologies used by novices include operational client server or mainframe application systems with operational data stores and potentially some functional data stores as well, like a financial data mart. Revenue cycle management (RCM) is a major concern for providers. One of the most important reasons for prenatal visits is doing an ultrasound examination to estimate fetal size and development. Data analytics doesn't operate in a vacuum. It is used to solve specific problems and to provide information about operations to improve strategic decision-making. A medium-sized bank was facing high labor costs based on manual inputs and data verification in its feeding of data into a corporate financial planning and reporting system.

Pages: 28

In this chapter, the authors discuss experiences, AI and analytics use cases, and lessons learned from practitioners. Practitioners are in the center stage of the AI and analytics maturity continuum. A successful data-driven analytics practice takes a village. Practitioners, unlike novices, are dedicated as an organization to the pursuit and engagement of AI and analytics methods and practices. Practitioners are using data-driven technologies every day in their business processes. Practitioners often have a mixture of commercial AI and analytics platforms for analyzing and visually exploring data, creating dashboards, building ML pipelines, and managing these assets. AI and analytics are being used to speed the time and accuracy of diagnosis and treatment. In the early days of managed care there was much concern that with contracting focusing on avoiding overuse and waste that prevention and screening would suffer. The telecommunications (telecom) industry has always been a big user of data, especially data involving math and engineering.

Pages: 28

In this chapter, the authors discuss experiences, AI and analytics use cases, and lessons learned for leaders. Text is an example of unstructured data with much promise. Text, a subset of unstructured data, is one example of the types of data leaders are using in large measure compared to practitioners. Important parts of the claim information are notes and ancillary text. Online payments are the lifeblood of modern businesses. Financial technology companies that operate as online payments systems support online money transfers and serve as an electronic alternative to traditional paper methods, such as checks and money orders. Extracting information from unstructured data is much more difficult than extracting it from structured data. There are many other leaders in the public and private sectors. To be an innovator requires hiring and retaining some of the brightest minds in the AI and analytics industry.

Pages: 29

Innovators are a small group of organizations or pockets within an organization. In this chapter, the authors try to inspire thinking about some of the innovative ideas that have surfaced over the last several years. Innovators exhibit all the same characteristics as leaders with respect to strategy development. ChatGPT, developed by OpenAI, is one of the latest rages in AI. It holds the possibility of fundamentally changing peoples’ professional work and personal lives. TinyML software is typically ultra-low power; these applications run on a physical hardware device. However, geospatial machine learning is getting a new reinvigoration. Innovators typically use the largest datasets in the world. Smart innovators are very careful to weigh out the short-term versus long-term needs. Synthetic data is artificially generated data that is used to train and evaluate machine-learning models. Causal inference is an extremely important topic in AI and analytics. The computational capacity of deep-learning models have been critical to advancing a panoply of conservation efforts.

Pages: 18

Strategy comes first, and developing strategy is circular with the ability to execute it. The best strategy means nothing if the people cannot execute. Analytics strategy supports the overall corporate mission and goals. Conducting the assessment begins as a top-down process. It needs to be dynamic in nature and adapt to the organization and its people. The cascade for information continues with interviews of identified staff to assess readiness across the dimensions already discussed. Based on the meeting outcome the JTF will develop a presentation for senior leadership to present the findings and plans for the program. Some organizations are redesigning themselves to be more data- and process-centric. The goal of any digital transformation is to improve operational efficiency, increase revenue, and enhance customer engagement and satisfaction. People and cultural challenges are the hardest to get right.

Pages: 35

Leading healthcare providers have found that putting effort and rigor into laying their data foundation is the most important component of a data strategy that provides efficient access to patient insights in real time across the enterprise. In most corporations today, the vast majority of business decisions are made on a distinct minority of the data. Most business decisions are based on structured data. But structured data is only a small part of the data that passes through the corporation. Typical sources of voice recording are hospitals and video recordings. There are two basic types of analytics that can be performed on the databases of text that are produced. Those two forms of analytics are: correlative analysis and sentiment analysis. Some types of text lend themselves to different kinds of processing. Cities, counties, and states provide many critical services to the community that often go overlooked, undervalued, or represented poorly by a single incident.

Pages: 6

The rise and speed of technology innovation has created a lot of stir in the media, the press, universities, businesses, and governments. Some of the information is accurate and useful. Some of this information is inaccurate and confusing. Some of it is beyond the reach of the average professional or student because it is either too narrowly focused or too technical without the necessary background. The authors considered the information presented in that text as what the authors would cover in a course titled “Creating Value from Data 101” taught at a university, what some would call a survey course. Our societies have spent hundreds of billions of dollars collecting data, but most of it sits in silos. Silos of data never analyzed, never touched again.