Guandata CEO Chunyuan Su: BI Enters a New Era of Business Value (Plus Guandata 2022 Intelligent Decision Summit & Product Launch Agenda)

In the post-pandemic era, as the economy enters a new normal and market uncertainty intensifies, more and more companies need to strengthen their survivability through refined operations and agile responsiveness while searching for new growth opportunities. In this context, the value of data has gained further recognition, and data-driven analysis and decision-making capabilities have gradually become essential for enterprises.

Meanwhile, enterprise demand for BI products is also evolving. According to iResearch's observations, BI products that sit closer to business departments are increasingly winning favor among enterprise users, becoming a new trend in the BI market.

What changes have occurred in enterprise demand for BI? What new value can modern BI — with business users at its core — bring to enterprises? And how can companies truly enable business personnel to participate in data analysis? To explore these questions, iResearch recently interviewed Chunyuan Su, CEO of Guandata.

Founded in 2016, Guandata operates with the mission to "empower business users and enable smarter decisions," providing one-stop data analysis and intelligent decision-making products and solutions to leading enterprises in retail, consumer goods, internet, finance, and high-tech sectors. It has now deeply served 500+ industry-leading clients including China Merchants Bank, Unilever, ANTA Group, China Resources Group, Genki Forest, Mixue Ice Cream & Tea, and Xiaohongshu, accumulating extensive experience in enabling business departments to participate in data analysis.

Su believes that 2022 marked a clear inflection point for China's BI market.

Due to shifts in the external market environment, enterprises' need for data analysis agility and ease of use has continuously increased for their own survival and development. The traditional analysis model — where business departments submit requests and IT responds — no longer meets demand; business departments need to participate more directly in data analysis work. Modern BI is replacing traditional BI, which was dominated by IT teams producing reports. A truly modern BI product that "lets business users get hands-on" must simultaneously meet enterprise-grade performance and usability requirements, and integrate with business problems to form scenario-based solutions. Additionally, enterprises need to enhance their analytical capabilities and data literacy through empowerment, training, certification, operations, and other methods. Over the next decade, data analysis capabilities will rapidly become a core competitive advantage for enterprises.

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01

Multiple Factors Driving a Shift in BI Market Winds

iResearch: Based on iResearch's observations, recent BI market trends clearly show a shift from IT toward business, and from reporting tools toward analytical decision-making. As a long-time industry practitioner and one of the navigators of this new trend, please share your perspective on this shift.

Chunyuan Su: 2022 was a very clear inflection point for the BI market. Over the past 5 to 10 years, reporting products were the most popular BI products in China. However, a Gartner report on China's analytics platform market released this year noted that demand growth for reporting products is gradually stagnating, with incremental demand coming from modern analytics-oriented BI platforms that emphasize low-barrier analytical capabilities. These products will lead the entire BI market with growth exceeding 30% over the next five years.

This conclusion aligns perfectly with our observations from six years of continuous client engagement and market sensing. For leading enterprises, most have already deployed some reporting or traditional BI products, and are now at a stage where they need to upgrade and replace them. Meanwhile, new-economy companies in new retail, new energy, and broad internet sectors mostly need to build modern BI from scratch — going from zero to one in one step.

iResearch: What are the key driving factors behind this inflection point?

Chunyuan Su: Let's break this down by the two types of enterprises we just mentioned.

For new-economy companies, it's relatively straightforward. Companies like new consumer brands, broad internet players, and EV newcomers were mostly founded in the past 5-10 years. Their businesses are inherently agile and data-driven, and they have very friendly procurement habits for digital products and services — preferring to purchase directly from vendors rather than building in-house. This reduces extraneous investment and allows them to better focus on their core competencies. At the same time, decision-makers at these companies have a strong understanding and appreciation of data value, with shorter decision chains, making it very easy to reach purchase decisions for a complete data analysis and decision-making platform.

Large enterprises' demand for modern BI is more like the result of favorable timing, positioning, and conditions coming together. On one hand, foreign BI vendors represented by Tableau have gradually exited the Chinese market, making localization and domestic substitution the dominant theme. This series of changes has required companies to reselect vendors, making domestic products the preferred choice at this juncture.

Another important factor is macro-market changes. Over the past five years, whether in finance or retail, the primary data analysis model at large enterprises involved business departments submitting requests, with IT responding on a weekly or monthly basis to produce relatively fixed analytical reports. As external market uncertainty has intensified and changes accelerated over the past two years, enterprises must improve decision-making efficiency while "making every bullet count." Decision granularity has shifted from ten-thousand-yuan units under coarse management to thousands and hundreds. From 12 times monthly to 52 times weekly, to 365 times daily — constantly seizing every one-in-ten-thousand or one-in-a-thousand decision opportunity for GMV and profit.

In this environment, the response speed of traditional data analysis models can no longer meet current needs, and has even gradually evolved into a situation where business is constrained by IT. To improve the immediacy and granularity of data analysis, we must "let business users get hands-on." At the same time, requirements for BI platform construction cycles have shifted from the past 1-3 years of phased foundation-building to 6-12 months for delivering business value.

iResearch: To address the supply-demand contradiction of slow response times under traditional models, there are theoretically two paths: one is providing better tools for IT departments to improve their productivity and responsiveness, and the other is the "let business users get hands-on" approach mentioned above. How are these two paths playing out in practice?

Chunyuan Su: Both paths are viable, and the choice depends on each enterprise's specific situation. Guandata has many large clients that still use IT departments for deep BI work. For these clients, Guandata BI provides complete data pipeline tools, allowing IT departments to build analytical models through low-code drag-and-drop. We also provide mobile BI capabilities, enabling IT staff to quickly publish lightweight mobile applications for different business departments in one-fifth of the time previously required, increasing business stickiness to data analysis terminals and systematically solving the matching problem between capacity and demand.

As for "letting business users get hands-on," indeed many leading clients have already entered the stage where business departments use data directly. For example, China Merchants Bank and Genki Forest — different industries, but alike in having analysts from business teams doing data exploration from headquarters to regional levels. Meanwhile, IT departments take on more meaningful, higher-value roles: data governance and analytical empowerment. This is one of the biggest differences between modern BI and traditional reporting.

It's clear that business users getting hands-on with data still relies heavily on IT departments' strong support in data production, processing, and operations, but business and IT are creating new chemistry together.

02

"Active User Count" Is the Metric for Whether Modern BI Has Successfully Taken Root

iResearch: What types of enterprises are best suited to practice and promote the "let business users get hands-on" philosophy and corresponding modern BI tools?

Chunyuan Su: Overall, these modern BI tools primarily serve medium and large enterprises, because their business and management complexity is higher, managers have strong demand for visual data analysis, and subordinate business departments have more diverse data requirements.

For small and micro enterprises, such as e-commerce merchants, using data services like Alibaba's merchant backend is sufficient to meet their data needs. But generally speaking, if a new consumer brand reaches GMV of 500 million yuan or more, with 3-5 or more sales channels, it means traditional data analysis methods will start to fall short, and BI becomes essential.

iResearch: Business departments' demand for data analysis is increasingly robust. Looking across industries, what are some typical business application scenarios?

Chunyuan Su: Enterprises with analytical needs on the business side share several characteristics. First, strong B2C orientation. Second, higher-frequency decision-making. And finally, greater demands for analytical timeliness.

Looking at specific application scenarios, industry differences are indeed quite significant. In retail, the top scenario is the CEO operations analysis cockpit, followed by analysis across the complete operations chain from headquarters to regions to stores. In consumer goods, the CEO cockpit is equally important, followed by e-commerce operations analysis. In finance, retail banking business analysis is crucial; in back-office departments, risk control analysis takes center stage. The broad internet sector focuses on user growth analysis and related areas.

There's one more point: in this year's economic winter, CEOs and senior leaders across all industries share one commonality — they all place extraordinary emphasis on BI analysis that integrates financial and business data. Only through bottom-line-oriented refined management can sustainable growth be ensured.

iResearch: How should we evaluate whether a modern BI implementation is successful within an enterprise?

Chunyuan Su: We believe user count is the most important and intuitive metric for evaluating whether a BI product has successfully taken root. More precisely, we believe it should be "active user count." Many of the typical enterprise clients mentioned earlier expanded from 1-2 departments to the entire company within six months of launching Guandata BI, growing from 100 users to over 1,000. What delights us is that more and more clients have achieved getting 20% of their business teams actively using it first, then quickly extending to broader business teams within the organization. Why 20%? Over the years, Guandata has identified this as an interesting golden point — leading organizations have reached or exceeded this penetration rate.

iResearch: In the transition from reporting and traditional BI to modern BI, particularly with more business personnel directly using BI, has the logic for how enterprises consider ROI changed?

Chunyuan Su: Foreign institutions have defined some authoritative models that broadly score estimated BI application effects across different scenarios from dimensions like revenue growth, cost reduction, and efficiency improvement. Guandata also applies such models, but we hope to communicate a more pragmatic evaluation tone to the industry. In my view, BI cannot directly bring growth to enterprises; what it brings more is the capability for growth — discovering growth opportunities continuously through more scientific data analysis, then constantly seizing every finest-grained growth opportunity. For cost reduction and efficiency improvement, it similarly provides enterprises with ongoing capability. Some results can be directly quantified; others show up as efficiency metric improvements.

In today's economic environment, one very notable point is cost control. This year, many of our clients shifted from self-developed solutions to purchasing third-party products. Human costs are too high — compared to enterprises building equivalent capabilities in-house, our products can reduce costs by 80%, while improving internal experience and response speed by 5x. Take one new energy vehicle brand: after replacing their existing BI tool with Guandata BI, they saved at least 5+ headcount in human resources investment, while business responsiveness improved to over 5x previous levels because business users could actively use it themselves.

03

Product Ease of Use Is Key to "Letting Business Users Get Hands-On"

iResearch: You mentioned that modern BI primarily serves large enterprises with complex internal management and operations. How does Guandata address this complexity to better serve them?

Chunyuan Su: First, on the technology front. At a large enterprise, personnel across different departments and roles need to conduct data analysis and exploration. Internal BI users often exceed thousands, creating high-concurrency situations with demanding technical architecture requirements. Guandata BI is built on a horizontally scalable big data architecture that can support large user volumes.

Second, on the product front. In large enterprises, different roles use data and have analytical needs in different ways — from data queries, simple data calculations, to advanced data analysis using data models and algorithms. Previously, several different tools had to be combined to meet this full range of needs, creating enormous operational challenges. Guandata provides an integrated one-stop solution that simultaneously meets varying depths of data analysis needs, as well as requirements for data real-time and permission control. For example, at one enterprise, both the CEO and frontline business staff need to check numbers Monday morning, but from an importance perspective, management needs must be prioritized, while frontline staff can accept some wait time. Therefore, by isolating computing resources for different user tiers, we ensure sub-second response for important user roles.

Third, on the services front. Deploying BI tools at large enterprises involves operations, training, promotion, performance planning, and multiple other stages, placing high demands on vendor service professionalism. Guandata has established a relatively comprehensive enterprise-grade BI implementation service system.

Overall, Guandata proactively empowers clients through advanced products, technology, and services, ensuring successful BI implementation and building data-driven organizations.

iResearch: What targeted designs has Guandata BI made to "let business users get hands-on"?

Chunyuan Su: From a product perspective, the biggest keywords are ease of use and enterprise-grade. Over the past six years, Guandata BI has continuously innovated around these two keywords.

First, improving user experience. Guandata enhances user experience by optimizing product interface design — for example, reducing the number of clicks required for an operation. This has received very positive client feedback.

Second, providing a comprehensive product functionality matrix. Take one scenario: according to Guandata's observations, before conducting data analysis, business personnel need to import their own data 90% of the time, and merge IT-processed data with their own data or perform other flexible processing. Guandata BI provides a data preparation module for this scenario, not only solving the business-side data import and merging problem, but also more clearly displaying to business personnel the data sources, data volumes, and data types provided by the IT department. Guandata was the first domestic vendor to offer this functionality.

Another example: Guandata's mobile BI functionality supports enterprises in combining high-attention scenarios and commonly used metric dashboards into mobile apps for publication to business users, making it convenient for business personnel to view and use. Abnormal data directly connects to Lark, WeCom, and DingTalk, reaching business decision-makers immediately.

Furthermore, enterprise-grade capability is critically important. Many enterprises using traditional BI encounter performance problems once user numbers grow. Guandata BI's enterprise-grade capabilities, refined over six years, have achieved generational leadership, supporting industry-leading enterprises including China Merchants Bank and Genki Forest in active usage by tens of thousands of business users.

iResearch: Letting business users get hands-on requires products that can integrate with specific business scenarios. How does Guandata embed industry best practices into industry-specific solutions within its product?

Chunyuan Su: Based on industry best analytical practices, Guandata BI's user-facing application marketplace provides highly packaged scenario-based analysis suites in a SaaS-like manner. These are supported by data analysis models and specific data, structured through three layers — data, model, and analysis tools — providing scenario-based analytical applications for retail, FMCG, finance, broad internet, and other industries. Going forward, Guandata will continue abstracting and refining more analytical scenarios through extensive service experience, sharing them with users at lower cost.

iResearch: Beyond the tool itself, what requirements does "letting business users get hands-on" place on an enterprise's own organization, culture, and data foundation? Combining Guandata's implementation experience, please discuss what enterprises need to do to truly achieve widespread BI adoption on the business side.

Chunyuan Su: From past observations, enterprises that enable business-side data analysis have generally established relatively mature and stable data foundations, preparing the data groundwork for analysis. Additionally, overall enterprise data literacy is a decisive factor, including data culture and management's emphasis on data. These two elements — data foundation and data literacy — don't need to be simultaneously satisfied; they can compensate for each other, with either one in place being sufficient to advance an enterprise's data analysis capabilities.

Guandata will soon release its newly defined data analysis methodology in October, which will classify enterprise data analysis maturity levels across dimensions including organizational data literacy, data talent, infrastructure, and business processes. This model can also comprehensively assess and diagnose an enterprise's current data state. Guandata will provide corresponding data analysis operations training to ensure BI implementation effectiveness and truly let business users actively get hands-on. Over the next decade, we believe the BI industry needs a standard for customer success: BI analysis going live is just the starting point; getting business users to truly actively use it is the industry's value commitment to clients.