In recent years, ChatGPT, which has been popular in the circle, has set off a long-lost artificial intelligence boom. How to better make artificial intelligence truly used by enterprises has become a recent hot topic. Big data and artificial intelligence complement each other. The training of artificial intelligence is based on a large amount of data, and the value of data needs to be fully exploited by artificial intelligence.
When training artificial intelligence, it generally needs to go through a series of steps such as data collection, data cleaning, feature extraction, model selection, model training, model testing, and deployment. In this process, the level of data quality is extremely important for the update iteration of artificial intelligence. This is also similar to the daily data analysis of enterprises. Only high-quality data can better support users to conduct accurate analysis and prediction, thereby helping enterprises to make better business decisions.
From data-driven to index-driven, build a new engine for enterprise digital intelligence management
In the future, artificial intelligence will be implemented in more scenarios. In addition to collecting general public data, more data and information with industry attributes will be needed. Especially in industries with high knowledge barriers, enterprises need to efficiently accumulate business operation data as the basis for training artificial intelligence.
However, in the process of digital transformation of enterprises, various complex technologies and platforms have brought massive data. If you are not careful, there may be millions of indicators scattered in various systems, and there are a large number of wrong indicators and redundant indicators. , invalid indicators, "same name but different meaning" indicators and other data quality problems, all of which have brought many difficulties to the "data-driven decision-making" originally expected by enterprises......
So, how can enterprises improve data quality and turn data resources into data assets? Unified management of "indicators" with business implications and the construction of a "one-stop indicator platform" have become the key , that is, to manage and analyze data with indicators and indicator systems that are closer to the business, and move from data-driven to indicator-driven .
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, so as to provide 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. A16Z, a well-known venture capital institution, also mentioned that as an independent layer between data warehouses and downstream analysis tools, more and more companies choose the "Metrics Layer" to decentralize and democratize the ability to build and distribute metrics .
（图源：A16Z Emerging Architectures for Modern Data Infrastructure）
At present, in industries such as finance, retail, and manufacturing, many leading companies have built indicator platforms one after another, realizing the transformation from data-driven to indicator-driven:
- Use indicators to express business logic, accumulate valuable data assets, and improve data reusability;
- Use indicators as a common data language for enterprises to achieve efficient communication and collaboration;
- Establish data trust within the enterprise with a unified indicator platform, promote the construction of data culture, and facilitate broader business innovation.
Kyligence Zen, an agile metrics tool for everyone
Indicators will play a key role in boosting efficient business decision-making, and a complete indicator platform here is particularly important for enterprises: not only need to have an indicator layer that can be self-managed, but also realize the automatic completion of data construction. Model and conversion, complete end-to-end data analysis and output business decision basis .
Kyligence Zen is a one-stop indicator platform based on Kyligence's core OLAP capabilities. Combining Kyligence's rich practical experience in implementing indicator platforms for customers in financial, retail, manufacturing and other industries for many years, Kyligence Zen aims to solve the pain points of indicator management, analysis and application faced by enterprises, help enterprises build digital management systems, and realize indicator-driven management and decision making. With its low-code metrics service, everyone can work with metrics in agility.
Kyligence Zen one-stop indicator platform
In the one-stop indicator platform, the indicator catalog is an important carrier for building a unified indicator system and realizing indicator-driven. Kyligence Zen can easily define and manage indicators and form a unified indicator caliber, so that all business users, data consumers and decision makers can use a consistent caliber and indicator system, and the unified indicator system is the core of efficient management and communication of enterprises.
One of the core values of the indicator platform is to help business users lower the threshold for using data and improve the ability of business teams to interpret and use data. Compared with the traditional table-based processing and presentation, indicators can give the business a more intuitive feeling, because indicators are the most friendly presentation for business personnel, and they are the premise for independent exploration.
Want to learn more about the technical interpretation and innovative products of the indicator platform? Welcome to click the link to participate in the Kyligence indicator platform product launch at 10:00 on April 11.
On the day of the event, Kyligence co-founder and CTO Li Yang will share how to "transform from data-driven to indicator-driven" and release the GA version of Kyligence Zen; at the same time, we will also bring the joint solution of Kyligence Zen and Amazon Cloud Technology, and The Kyligence Cloud Business Partner Program will be officially launched, so stay tuned for more exciting events!
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, China Merchants 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.