The Data-driven Value Of Business Intelligence Tools For The Private Healthcare Sector

The Data-driven Value Of Business Intelligence Tools For The Private Healthcare Sector

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The Data-driven Value Of Business Intelligence Tools For The Private Healthcare Sector – In a follow-up to the first article in the Democratizing AI newsletter, which focused on AI as a key exponential technology in the smart technology era, this article shares some text and audio excerpts from Chapter 4, “AI-Driven Digital Transformation of the Business Enterprise.” The book Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Age of Smart Technology will be discussed at BiCstreet’s “AI World Series” live event on 17 March 2022 (see more details at the bottom of the article):

Artificial intelligence will no doubt be a transformative and disruptive driving force in business, impacting all aspects of the business industry, business models and more business sectors, creating new market opportunities and impacting the industries of the future. There we will see more AI-powered money, the codification of markets and trust, the weaponization of code through AI-powered cybersecurity, and the development of fundamental markets. AI is estimated to add $13 trillion to the global economy over the next decade.

[i]Over the past two decades, many industries and business sectors across the globe have been directly involved in developing and delivering impactful AI software solutions. , it’s amazing to see how business value drivers like customer growth, retention, risk, productivity, efficiency, throughput, yield and quality can be impacted as more data becomes available and the business gets better tools and connectivity.

This is further aided by significant increases in computing and data storage and processing capacity, and AI and smart technology toolboxes are being strengthened by the utility and capability of more powerful algorithms using all available structured and unstructured data.

The Data-driven Value Of Business Intelligence Tools For The Private Healthcare Sector

As enterprises look to AI to move into new business segments or maintain a competitive advantage in their industry, a rethinking of industries and industry is required. We now see the evolution of markets in terms of more informed consumers, faster and scalable marketplaces, dynamic and vibrant businesses and leaner operations. Significant advances in AI are helping the creation of new industries and business segments, taking a fast adoption journey for a new industry to move from innovation to commercial application.

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Some early examples of AI driving new industry segments are GPS-powered ride-sharing companies, hyper-personalized online shopping platforms focusing on microsegments, intelligent virtual assistants driving conversations with customers as well as industry, recommendation-driven streaming channels and adaptive learning-based educational companies. We have seen an enormous increase in AI-focused startups with 1800% investment in the last six years.[ii]These developments are putting more pressure on the executive management of enterprises to act quickly in making strategic changes to monetize these new business opportunities.

And the acceleration of AI adoption and its applications adapting their business models has led to the creation of new industries and business segments. While the current focus of AI applications is largely on improving efficiency in existing industries, the most extraordinary long-term economic use of AI is likely to be in solving large, complex, and open-ended problems that can become the foundation of new industry segments. For this, business leaders and AI strategists must identify key trends, track cutting-edge AI developments, and act quickly around new possibilities.

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We need to specifically rethink the impact of human-computer interaction, automation, jobs, the workplace and cybersecurity, among many other factors that affect business value drivers, employee and customer experience. It is also clear that consumer-facing businesses must offer personalized customer experiences at scale, which is beautifully illustrated by the success of Internet giants such as Google, Amazon and Alibaba and their ability to deliver personalized experiences and recommendations.

By using AI to build a dynamic real-time 360-degree profile of customers as they interact through mobile apps, intelligent virtual assistants and online web portals, suppliers of goods and services can quickly learn how their AI-powered forecasts fit customer needs and requirements with ever-increasing accuracy. . When we flip through recommendations on Netflix or Amazon or search on Google, most AI-based computations are happening on high-powered processors in remote data centers (in the cloud) with handheld or desktop devices acting as interfaces and communicating.

Results. This will change as AI algorithms become more efficient and able to operate on low-power devices at the “edge,” where custom processors are designed to carry out real-time analytics on the fly, near the point of data collection and use. As the cost of hardware and software continues to fall, AI tools (augmented by IoT, cloud and edge computing, virtual and augmented reality, etc.) will be increasingly embedded into our vehicles, appliances and workplace accessories.

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Every shape and size is capable of learning itself. As IoT integration allows for the development of an environment where solution providers and customers can interact, it is possible to design experiences on products, further impacting business models.

Given the fast pace of change and disruption in the age of smart technology, any business to stay relevant and thrive, needs to transform itself into an AI-driven business and build more real-time intelligence into all aspects of its internal operations. , customer needs and influence, and competitive and collaborative forces in the ecosystem in which the business operates.

For businesses to move towards AI-powered automated decision-making, they need to overcome the information quality barrier. However, accurate data is becoming increasingly available with better quality sensors, improved connectivity, and the rise of smart technology and ways to simulate real-world processes and procedures in the digital domain. We will see an increase in the availability and accuracy of real-world simulations, leading to more powerful and accurate AI systems.

As computers are now powerful enough and trained on accurate-enough data to perform simulations in the digital world, the cost and risk of testing AI systems in the real world can also be reduced. For example, we’ve seen how simulations can help businesses working on the development of autonomous vehicles acquire thousands of hours of driving data without the vehicles ever leaving the factory, leading to an increase in data quality and a significant decrease in cost. Even more accurate real-world driving data is captured given the nature of Tesla’s software-defined electric vehicles.

Whether they have Autopilot enabled or not, data from Tesla vehicles is sent directly to the cloud and used to create more data-dense maps that they claim are more accurate than alternative navigation systems. A company can better mine all available internal and external data on its operations, value chain, customers and ecosystem to create real-time dynamic simulation models of all aspects of its business, able to optimize the best business in short, medium and long-term windows and adjust its course where necessary. . It is relevant in all industries.

Across industries, businesses and organizations are assessing ways and means of making better business decisions using such untapped and abundant information. As the world becomes instrumented, data is generated at an exponential rate, but data consumption increases relatively linearly relative to data generation.

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With evolving AI technologies that can unlock value from growing data sets and more and more business use cases to contend with, the required data infrastructure, computing (both hardware and software), there is a need for innovative approaches that take AI into account. Tools and platforms, processes, organizational alignment and roles. As enterprises look to innovate faster, launch novel products, and improve customer services, they need to find better ways to manage and utilize data across internal and external firewalls.

Organizations are realizing the need and importance of scaling their existing data management practices, overcoming siled execution and adopting new information management models to counter the risk of little business insight or lack of effective solution deployment. Therefore, an organization’s ability to analyze that data to discover meaningful insights and implement AI-driven solutions is becoming increasingly complex.

Current business success stories come from companies creating analytics innovation and data services, embedding a culture of innovation to create and promote new database solutions, enhancing existing solutions for data mining, implementing predictive analytics and machine learning techniques. Creation of skills and roles such as data scientists, AI or machine learning engineers, data science developers, big data architects, data visualization specialists and data engineers. These enterprises’ experiences in the AI ​​and Big Data analytics landscape are characterized by agility, innovation, acceleration and collaboration.

Another important aspect of leveraging smart technology is understanding where it can be used, when it can be used, and how it can be used. Business value drivers can generally be categorized as either strategic or efficiency related. Some of the strategic drivers include generation of new business opportunities through exploratory analytics to uncover hidden patterns, proactive decision making and operational insights through predictive analytics, customer and market forecasting.

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