In recent years, my country's digital economy has developed vigorously, and data has become a new element to promote economic and social development. The National 14th Five-Year Plan pointed out that in order to activate the potential of data elements and speed up the construction of the digital economy, it is necessary to focus on the implementation of the "cloud, data and intelligence" action to promote the coordinated transformation of the data empowerment of the entire industry chain. In order to further move towards high-quality and high-level digital development, digital intelligence—digital + intelligence is becoming a new goal of enterprise transformation.
Interpretation of the latest trends
In Gartner's latest top ten data and analysis trends for 2023, we can see many concepts and trends that enterprises are concerned about in digital intelligence. This report mentions the three major themes of data as business, from platform to ecology, and people-centered, and interprets the hot technological trends in the market, such as value optimization, data sharing becoming a necessity, consumers becoming creators, and emerging AI.
- data is business
For enterprises, if data analysis is only regarded as part of IT capabilities, it is difficult to truly serve the business and maximize the value of data. "Data is business" refers to expanding data capabilities as a business capability and connecting data analysis with business value.
Among them, the ability of data sharing is essential to promote the implementation of "data is business", which not only includes data sharing and collaboration within the organization, but also allows more business personnel to undertake some data analysis or data management responsibilities, and can also be used externally. Package data analysis capabilities into data products for empowerment and sales. In this process, enterprises can fully reuse the previously deposited data assets, thereby increasing the value of data sharing.
- From platform to ecology
After years of data architecture construction, it is extremely important for enterprises to maintain the normal operation of the data ecosystem. The composability of the ecosystem is achieved by building, assembling, and deploying configurable applications and services. When introducing new systems and technologies, enterprises hope to achieve seamless integration of old and new systems and different technology stacks, and achieve efficient operation through data governance.
At present, providing diversified and standardized application docking methods has become a required capability for enterprise-level data applications. Data applications should support seamless integration with mainstream BI, APP products, Web applications, and even Excel, WPS, etc., to better help existing data quickly integrate into various aspects of business operations and management, and help realize the value of data elements.
- people-centered
The report also pointed out that it is expected that by 2026, data analysis capabilities driven by generative AI will become an important part of large enterprises' expenditure on data analysis, thereby supporting the needs of enterprises to complete automated closed-loop data analysis results. Faced with the new wave of AI, enterprises can use technologies such as NLP to realize "decision intelligence" and realize a closed loop from data analysis to business value.
At the same time, data analysis will no longer be the job of professionals only. Through conversational, dynamic and embedded user experience, more and more data consumers will become creators, able to make full use of data to support daily business decisions and drive business innovation.
Metrics Drive Broad Application
In recent years, more and more leading enterprises have chosen to build a digital intelligent management system with indicators as the core.
In the banking industry, a leading joint-stock bank is driven by business scenarios and based on the basic capabilities of AI + BI + content to build a unified platform for index management and application. The platform integrates and unifies the indicator model, enhances the sharing and opening capabilities of indicators, and provides unified, consistent, credible and convenient indicator data for data applications such as management cockpit, report, BI, large screen, mobile terminal, and digital business platform Service capabilities. At present, the bank has shortened the data development cycle by an average of 3-5 days ; the manpower consumption of big data report development has been reduced by 30% ; the replacement rate of conventional requirements has reached more than 25% .
In the insurance industry, a Fortune 500 insurance company built a unified indicator platform to accelerate the transformation of data-driven business decisions . After building the indicator platform, the company has realized the reuse and standardization of indicators, greatly saving development resources; standardized governance of indicators from the modeling level to solve the company's long-standing data governance problems; it has become a hub between development and business, Reduce the investment of developers and increase productivity, shorten the data processing link, and save costs by more than 30% ; the innovative application of indicators can be copied to all indicators, and the overall delivery efficiency is increased by more than 5 times .
In the retail industry, a leading chain catering company built an indicator analysis and management application to quickly respond to the new needs of business data analysis, serving multiple departments such as market operations, human resources, and supply chain. At present, the company's performance analysis efficiency has increased by 3-4 times compared with before , which has reduced the cycle of data development and analysis, saved manpower, material and financial resources, and related costs have dropped by nearly 70% .
In the manufacturing industry, a leading car company built a quality data intelligent management platform to support the analysis of Six Sigma’s daily quality indicators in a stable, efficient and flexible manner, realize the sharing and unified management of quality data, and ensure the actual implementation of Six Sigma quality management. The lean management collaboration requirements of intelligent manufacturing within the enterprise facilitate the continuous innovation of products and services.
In 2022, Gartner released the "Indicator Platform Innovation Insight Report" , and pointed out that enterprises can consider using the indicator platform on top of the existing architecture to manage business data, thereby providing a single and credible data source for enterprise decision-making and management. It is worth mentioning that Kyligence is the only Chinese manufacturer included in this report.
At present, Kyligence indicator platform products and solutions have served many domestic and foreign customers, covering financial, retail, manufacturing, pharmaceutical and other industries. Help enterprises solve pain points such as indicator management, analysis and application, help enterprises build a digital management system, and realize indicator-driven management and decision-making.
On July 14th, the Kyligence User Conference will be grandly opened in Shanghai. We invite you to attend the event, focus on the opportunities brought by new technologies, new applications, and new platforms in the digital economy, and discuss how to deal with the impact and challenges of the new wave. To learn about the innovative practice of digital intelligence in finance, retail, manufacturing, medicine and other industries, click the link to sign up.
About Kyligence
Founded in 2016 by the founding team of Apache Kylin, Kyligence is a leading provider of big data analysis and indicator platforms, providing enterprise-level OLAP (multidimensional analysis) product Kyligence Enterprise and one-stop indicator platform Kyligence Zen for users Provide enterprise-level business analysis capabilities, decision support systems and various data-driven industry solutions.
Kyligence has served many customers in banking, securities, insurance, manufacturing, retail, medical and other industries in China, the United States, Europe and Asia Pacific, including China Construction Bank, Ping An Bank, Shanghai Pudong Development Bank, Bank of Beijing, Bank of Ningbo, Pacific Insurance, China UnionPay, SAIC, Changan Automobile, Starbucks, Anta, Li Ning, AstraZeneca, UBS, MetLife and other world-renowned companies, and reached global partnerships with Microsoft, Amazon Cloud Technology, Huawei, Ernst & Young, Deloitte, etc. Kyligence has received multiple investments from institutions such as Redpoint, Broadband Capital, Shunwei Capital, Eight Roads Capital, Coatue, SPDB International, CICC Capital, Gopher Assets, and Guofang Capital.