With the new challenges posed by the coronavirus epidemic, organizations will need more advice, more data and visibility to minimize the impact of the epidemic on the business.

Long before the impact of the epidemic on society and life, data has been regarded as an important asset to improve customer service. Organizations are still trying to extract more tangible value from their massive data to improve employee and customer experience.

Analysis of the implementation and application of artificial intelligence in the future

Data islands, legacy systems and fast-paced agile competitors all require the use of organizational data to drive the most important value. Due to the huge challenges, many organizations are beginning to realize the use of partner ecosystem and various technologies such as artificial intelligence and advanced analysis to meet the needs of innovation using data.

From the adoption of industry standards to the use of graphical databases, as well as the actual use cases of artificial intelligence and advanced analysis, six industry experts discussed the impact of the transformation of artificial intelligence and advanced analysis, and explained how to implement these technologies.

1. Align data strategy with business objectives

Patrick Smith, chief technology officer of pure storage in Europe, Middle East and Africa, explained the value of data. This is one of the most valuable forms of modern money, he says.

However, he pointed out, “a large amount of business data is only feasible if it can be processed, read and understood quickly. In this sense, advanced analysis can accomplish heavy data processing work, support business transformation efforts, and help various organizations improve performance and performance. “

Smith stressed, however, that most organizations lack infrastructure and analysis software, or expertise to effectively implement AI and advanced analysis.

To overcome this, he explained, organizations must focus on aligning data strategies with business objectives and working with technology partners to provide a modern data experience with a fast, scalable, easy-to-use infrastructure.

2. Get rid of artificial process

Over the past decade, business intelligence has been used to gain insight from historical data, but until recently, these analytical techniques have been largely manual.

This is changing, explains Wayne Butterfield, director of ISG, a global technology research and consulting firm. Business leaders welcome AI’s commitment to eliminate artificial processes and improve the quality of insight.

“Data driven insights (using historical data to predict future results) combine data, advanced analysis and artificial intelligence to transform decisions based on predictive insights in areas such as revenue, demand and supply,” he said. It’s still in its early stages, but the technology of automatic machine learning (automl) is lowering the threshold for organizations to enter, because these organizations may not have a large team of data scientists, but they still see the value of analyzing data. “

Referring to automated machine learning (automl) tools such as koritical.io and data robot, Butterfield explained, “these are becoming more and more popular at the center of excellence because advanced artificial intelligence models are put into relatively simple robotic process automation types and act on these predictions.”

3. Complete view

Kerrie heath, European sales director of artificial intelligence at opentext, said extracting value from data should not be a daunting task.

“By adopting advanced artificial intelligence analysis technology, organizations can drive value in real time and deliver value in a visual and interactive way, enabling users to easily predict products, themes, events, trends, and even themes and emotions,” she said. Only by fully understanding these unstructured data and combining them with the structured data in the enterprise system in real time, can the organization analyze, understand and manage its enterprise digital ecosystem more effectively. In turn, organizations provide themselves with tools to ensure and implement data governance. “

4. Required industry standards

Alejandro saucedo, engineering director of Seldon, believes that the implementation of advanced artificial intelligence and analysis technology is having a huge impact on society.

But saucedo pointed out that if it is not implemented properly, artificial intelligence will bring adverse consequences to the organization, especially when it comes to network security, privacy and trust damage.

“In order to best implement AI and ensure that it brings net benefits to our economy and society, we need to develop industry-specific standards and regulatory frameworks that are appropriate for our purposes,” he suggested. A transparent and executable framework is the key, and we need to ensure that relevant technical and non-technical experts are constantly involved in developing and updating them. “

He pointed out that artificial intelligence cannot predict the future. For example, even the most advanced artificial intelligence technology cannot predict the occurrence of an epidemic or its impact on the world. But today’s AI models will be able to use this period of data, including the impact of the epidemic, to provide information for future predictions.

5. Graphic database

Amy Hodler, manager of analysis and artificial intelligence programs at neo4j, a graphics database provider, said: “the logical extension of analysis is to use the relationships and network structures stored in all data, which have proved to be highly predictive. This will change analysis and artificial intelligence, because connectivity based learning is necessary to solve complex problems, including problems about system dynamics and group behavior, which have less data.

Enterprises can make use of the associated data insight in graphic database to improve efficiency and flexibility, otherwise they can’t use relational database. Because the purpose of building a graphical database is to preserve and calculate relationships, it can make valuable, often subtle predictions, such as identifying interactions that indicate fraud, identifying similar entities or individuals, finding the most influential factors in the journey of patients or customers, and even improving it operations. “

“When data scientists use graphical algorithms to understand the natural state of complex systems through data patterns and improve prediction accuracy, they gain power,” she continued. When artificial intelligence automatically transforms the prediction data into a more flexible automatic structure, it will provide a more flexible prediction structure. “

6. AI and advanced analysis in practice: insurance industry

Life insurance industry is just one of many industries that can use artificial intelligence and advanced analysis technology for transformation. Paul Donnelly, executive vice president, Europe, Middle East and Africa, Munich Re automation solutions, explained the application of artificial intelligence and advanced analysis in the life insurance industry.

“The insurance industry uses a lot of manual processes and back office procedures, which leads to a poor customer experience,” he said. Although we don’t want to buy life insurance in any case, the complicated process certainly doesn’t help to attract modern digital users. This is where artificial intelligence and data analysis technology come into play. For many reasons, these advanced technologies optimize the journey of end customers. For example, using artificial intelligence technology means that we don’t have to endlessly ask customers repeated personal questions, but rather guide them through questions related to them. Because in such a world, people can easily buy most of the products they want in a few minutes with just a few clicks of the mouse, and the long life insurance process is not attractive at all.

In addition, advanced analysis enables insurers to leverage large amounts of applicant data and translate it into viable insights. These insights enable insurance companies to modify underwriting rules in real time, resulting in technologies that can design, improve and simplify the interview process, thereby facilitating customers and shortening the time to underwrite them. “

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