Shulou(Shulou.com)11/24 Report--

On October 20, "Agile to the Future | Guanyuan data 2023 Intelligent decision Summit and Product launch" was successfully concluded at Pullman Hotel in Shanghai. The summit attracted 500 + retail consumption, high-tech, Internet and other industry executives and industry elites to participate offline, and 50000 + online viewers to share data-driven agile business value. From Procter & Gamble China, Zero Motor, Anxin Securities, Unilever, Budweiser Asia Pacific, Sihe Technology, Kaisheng Haofeng, Shandai Wine, Naxue Tea, Vitasoy, Giga Pet, Star charging, KPMG China, Yiou think tank and other top 20 + top 500 companies, industry leaders and authoritative institutions bring wonderful sharing.

At the summit, Su Chunyuan, founder of Guanyuan data & CEO, shared to the guests and the audience the exploration and practice of Guanyuan data on enterprise data-driven agile management capabilities, summed up the landing traps and corresponding strategies of agile decision-making capabilities, shared the best practices of industry benchmark enterprises, and predicted the latest services and product upgrades of Guanyuan data enabling enterprises to help more enterprises in complex and changeable uncertainty. Seize the growth of certainty.

The following is a record of Su Chunyuan's wonderful sharing:

Agile decision-making is the future

Agile decision-making is the future, but agile decision-making is nothing new. Agile decision-making has occurred at different stages of history. In the famous story of the Red Army's long March in modern history, every historical landmark we see today on the route from Ruijin to Yan'an is an example of agile decision-making; the process of reform and opening up is the process of crossing the river by feeling the stones. It not only verifies that "practice is the only criterion for testing truth", but also shows a process of agile decision-making.

The essence of agile management is the continuous feedback and iteration between "strategy" and "action". In the enterprise, the mission and vision of the top level remain unchanged in the medium and long term, while the strategy from down to action is constantly changing with the external environment.

In recent years, this trend has become more and more obvious. In the Gartner survey of CEO, more than 50% of CEO and the board of directors want to increase data-driven decision-making and regard data analysis capability as the first important strategic capability. At the same time, data analysis ranked first in a 2023 Gartner survey of CIO on "where digital investment will be increased".

Data analysis plays an important role in supporting enterprise agile decision-making in the future. However, from an industry point of view, there are still great challenges in the landing of data analysis. In fact, this challenge is not a skill or tool, but more often points to a "consensus". How does data analysis make decisions more agile in an enterprise? What level of decision-making problems does data analysis solve? How to measure the value between data analysis and business results? From cognition to landing, the lack of consensus at the top level is constantly magnifying the various deviations in the process of data analysis. How to form a consensus and how to integrate knowledge and practice is the biggest challenge of data analysis.

Five traps and coping Strategies

The direction of the challenge is clear, and the challenge itself will become an opportunity. Guanyuan data team often says that "problems are opportunities". The problem in society is the opportunity of every enterprise, and the problem of every enterprise is the opportunity of everyone. We are divided into two steps: first, to find the more essential thing behind the problem to solve the cognitive problem; second, to find a way to act to solve the landing problem. Here are our summary of the five pitfalls and coping strategies for data-driven agile decision-making.

Trap 1: the strategic inertia of past success has become the natural enemy of today's agility.

On the contrary, the successful strategic inertia of many enterprises in the past will become the natural enemies of today's agility. In the past few years, many successful organizations have deeply practiced some classic strategic execution systems, starting with market insight and defining each team's monthly or even weekly action plan with the finest granularity through strategic decoding. Under the assumption that the external environment is relatively stable and the business and organization are relatively unchanged, it will bring super execution. However, in today's ever-changing external environment, all the nerve endings in each battlefield are facing a rapidly changing business environment, and each finer-grained plan will lose the agility of external response. therefore, we need to think about how to put various changes as constants and primary elements into the whole process of strategy generation and adjustment, and practice agile strategy and execution.

Trap 2: a few people make decisions at the top, and more people get lost in gunfire

The second trap is about the way of decision-making in an enterprise. This is the representative decision-making style and organizational structure of some of the most famous companies in Silicon Valley. can we compare which of our companies are more like, is it that a few people make decisions at the top, or are more people able to make decisions under fire? In today's environment, what we advocate is to start distributed decision-making, to transform the organization to agile, so that more people who hear the gunfire can make decisions.

Trap 3: optimize local goals, lack of global thinking

When the local begins to make decisions, optimizing local goals and lack of global thinking is the most common trap. The cognition of the local perspective is limited, and the key scene or critical path found from the local goal may not be important in the global perspective. Only by paying attention to the overall goal can we bypass some risk scenarios, add new, more important and more valuable scenes and paths, and solve the real problems that are important to the whole world in each part.

Trap 4: focus on outcome indicators and lack of actionable process indicators

When the key scenarios and paths are identified, we observe that many organizations are easy to fall into the trap of narrow result orientation and excessive attention to result indicators. In the new normal of agile decision-making, when the real business is dismantled down, what is more important is the process indicators, especially the actionable process indicators, such as prior indicators, leading indicators or atomic indicators that you may hear. Enterprises that do well will pay more attention to process indicators in terms of finer granularity. For example, SAIC Feifan, the customer we serve, also pays attention to GMV, but pays more attention to the conversion rate under GMV, intention conversion rate, and the change of intention conversion rate behind each 4S store-to-store contact to the next stage, etc., to establish the relationship between action and process indicators.

Trap 5: qualitative and quantitative opposition, lack of scientific methods of hypothesis and testing

The last trap is the opposition between qualitative and quantitative, lack of scientific thinking and methods of hypothesis and testing. In a company, there are simply two types of people, one who is better at storytelling and more emphasis on experience and intuition, but others prefer to see the logic of science and the rigor of decision-making, which makes it difficult to form quality decisions to move forward.

With regard to the opposition between qualitative and quantitative, we can see that behind the opposition between qualitative and quantitative is a higher-dimensional scientific thinking, which unifies the two based on hypothesis and verification. Through experience and personal intuition, we can form some meaningful hypotheses, constantly verify the hypotheses in data analysis, and organically unify qualitative and quantitative.

In the past year, Guanyuan data itself has been practicing the agile workstation model based on the "five-step method of problem solving", starting with defining and analyzing problems, putting forward hypotheses of solving problems, constantly verifying them and landing on the ground. this also includes the falsification of the hypothesis, as well as going back to the problem and path itself to iterate over the analysis and definition of the problem.

In summary, in the face of the above cognitive traps in which agile decision-making is difficult to land, the countermeasures include two aspects: cognitive, through overall thinking, process thinking and hypothetical thinking to achieve the unity of the cognitive level, continue to form and strengthen consensus; action is the continuous improvement of the ability after cognitive consensus, so that business teams that hear gunfire can use data to make decisions.

It is critical to enable business teams that hear gunfire to use data to make good decisions. In the past, business users in the business team would put forward a lot of requirements to the IT team, including reports, dashboards, metrics, etc., which correspond to the real business problems they really wanted to solve, but they also needed to rely on the IT team. How to make the business team have the ability to solve these problems and make good decisions with data? According to the survey data of external institutions, 67% of CEO want data analysis and decision-making to be closed-loop within the business, rather than in the IT field, so as to make the business agile and responsive; 73% of business leaders want to have more data analysts and technicians in the business team, so that the business team can make better decisions with data.

Viewing distant practice: the way of data-driven Agile decision-making

How to improve data-driven agile decision-making ability? In the past few years, Guanyuan data has co-created with customers and continued to practice itself, exploring how to achieve the "unity of knowledge and action" from cognition to action, and then share with you what we think is effective practice for your reference.

At the cognitive level, first of all, each enterprise has its own hierarchical business logic from strategy (goal), tactics (index) to implementation (action). Based on this, we disassemble and optimize through three steps to form the cognition of agile decision-making and the action basis of data analysis:

The first step is to find the real problem of the overall situation. From the first global perspective, starting from understanding the company's Polaris goals, identify the local key scenes and paths that support the overall situation, and solve the global rather than local high-value real problems, real scenes, true paths.

The second step is to disassemble the actionable indicator logic. Disassemble the key scenes and paths into the corresponding most important related indicators, with particular emphasis on process indicators and actionable original indicators, pay more attention to the changes of the process, and the results will occur naturally.

The third step is to constantly imagine and verify in the implementation. Deliberately build the relationship between actions and process indicators, specific to each day's action tasks, track the smallest granularity of process changes, constantly verify or adjust hypotheses, consolidate opportunities, and win step by step.

From strategic disassembly to action, constantly verify the assumptions and paths of the business in the action. Top-down alignment, bottom-up feedback, and two-way feedback iteration are the key perceptions of agile management.

From cognition to action, mainly through the empowerment of science and technology, the data analysis products and capabilities that can be used by the business are the biggest leverage to support agile decision-making.

Old customers who are familiar with Guanyuan data know that Guanyuan's one-stop intelligent analysis platform has three characteristics: enterprise level, ease of use and scene. In particular, it is easy to use, which makes it easy for business people to look at data and make decisions in a few days. This stems from the design concept of Guanyuan data products, the pursuit of product value and the direction of product optimization, which is to let the business team produce more data producers. Increase the number of data producers within the organization by 10 times and empower 100 times the number of data consumers through easy-to-use products. From the headquarters to the front line, so that the personnel of all business units from CEO to the front line can return to the decision-making logic of gunfire, pay attention to the actions and indicators that can be produced at the most atomic level, and constantly seize opportunities in external changes through layers of coordination, decision feedback, and continuous action.

Industry leading practice of setting off a prairie fire

The agile decision-making practice driven by data has also started a prairie fire and landed among customers in various industries of Guanyuan data service. The following are three representative cases of agile decision-making of customers representing hundreds, thousands and tens of thousands of people at different stages of development and in different industries.

An Internet pan-agricultural platform: index disassembly and data decision-making from CEO to front-line

The enterprise is not long ago, about the size of nearly a thousand people, in the following desensitization diagram, you can feel the enterprise's data-driven agile decision-making logic. On the far left is the Polaris indicator, and down is the key business scenario indicator, known as the Big Dipper Index. The middle part is transformed into the core index tree, which strengthens, decomposes and transparently the decision-making logic, making it easier for everyone to understand. On the right is the index grading, post to person.

The data driver of this enterprise has three small details that I would like to share with you in particular:

First, let all the staff in the company have certain authority and control, but at the same time, basically let the whole staff know that they have the overall thinking, let the employees know the relationship between what they are doing and the overall link indicators, and improve the data analysis and decision-making ability of the whole staff through continuous training and continuous examinations.

Second, there are 20 robots within the enterprise, constantly synchronizing the completion of the most atomic tasks and indicators, so that the business can iterate quickly.

Third, the internal implementation of a sentence, "from data-driven to index-driven, from index-driven to goal-driven, and finally from goal-driven to vision-driven."

A head drink brand: data iteration and action closed loop for 365 days in 52 weeks

Guanyuan data has been working with the brand for 3 years, from dozens of people using Guanyuan to now more than thousands of people are using the Guanyuan data platform, and the brand has achieved data iteration from 12 months and 52 weeks to 365 days now. With the change of enterprise strategy, the solution layout of agile decision-making of the enterprise is still being enriched.

Taking the scenario of strengthening network management through data and guiding front-line execution actions to lead to better network sales as an example, Step 1 shows the feedback observation behavior of each network based on the most front-line, and summarizes the relationship between each specific action and the corresponding original indicators. For example, the delivery rate of a certain network, the ratio of joint visits by supervisors and front-line sales representatives, and so on. Step by step to Step 2, the relationship between the data association generated by every day, every network, each format and the process indicators, the highlighted abnormal indicators. Then Step 3 further forms a closed loop of feedback, leading to executable actions.

Such a decision-making scenario in the brand is only the tip of the iceberg, enterprises have thousands of such scenarios or more, the economic benefits of each scenario are less than 1 million or even greater. What this case would like to share with you is that in data-driven agile decision-making, in the key scenario links of the enterprise, build the smallest relationship between actions and indicators, and constantly feedback iterations.

A head joint-stock bank: the distributed decision-making practice of more than 10,000 people from the head office to the branch

Guanyuan data has also cooperated with the bank for more than 3 years, from hundreds of people at the beginning to tens of thousands of people in the bank now using Guanyuan BI. The new growth business of the industry in recent years, the agency development business, many banks in the past and even today are still the traditional mode for the head office to tell branches and sub-branches which enterprises can do local agency business expansion. But through agile model building, the bank empowers this capability to branches, so that branches can combine with well-known enterprises, through many different analytical dimensions, while referring to head office or other branch data, continue to identify local portraits in the process, and choose enterprises with better transformation, better potential and more long-term value to expand.

Behind the distributed decision-making model that the bank can support more than ten thousand from the head office to the branch, three layers of influence circles are established, so that each branch has the ability to make distributed decisions under relatively unified rules.

In the past year, many new friends have been added to the customer list of Guanyuan data service. We are glad to see that more and more customers continue to become the digital benchmark of their industry. But what makes us feel more deeply and full of respect is not more and more customer logo, but the teams, specific leaders and promoters behind these logo, who are making things happen, changing the direction of the tide, making this enterprise shine under the digital tide, becoming a more advanced organization, but also promoting the evolution of the entire industry and society.

The mission of viewing the distance and long-term companionship

This year, Guanyuan data ushered in its seventh anniversary. At the seventh anniversary celebration in September, we released upgraded values. The reason for the renewal of values is that as we increasingly realize that agile with our customers is the trend of the future, and the relationship between data-driven and agile decision-making, we are determined to become a more agile organization.

The six values of refurbishment are jointly created by all the friends of Guanyuan data. Two of them are shared here: "always put customer value first" is our first value, firmly create value for customers, but also a commitment to all old customers and new friends in the future. "solving problems scientifically and realistically", through data thinking and scientific thinking, seeking truth and solving real problems in a pragmatic way is an important behavior of agile organizations, which is shared with you on the road of agile practice.

Another key upgrade for the seventh anniversary is the 6S model that Guanyuan data began to run this year, which is one of the most important aspects of our service upgrade. Through the 6S model, we can match the digital development stage of our enterprise, whether it is the activation period from scratch, or has entered the penetration period, replication period, or extensive self-service period, business integration period, or has begun to explore the AI. In this roadmap, we will give some suggestions to enterprises from different dimensions, work with enterprises to combine their own situation, choose the most appropriate way to advance, and match all kinds of services provided by Guanyuan to help enterprises move forward steadily.

In terms of products, Guanyuan data one-stop intelligent analysis platform 6.0 has been officially released. The core of the upgrade is the strengthening and updating of one-stop capabilities, because we see that more and more customers have various needs that want to be realized on one platform, so the new features or optimization upgrades of Guanyuan BI 6.0 will place special emphasis on one-stop shop, so that enterprises can better manage on one platform and business students can better cooperate on a unified platform.

There are many upgrades in product 6.0. one of the highlights is the integration of BI and ChatGPT that many customers are looking forward to-- BI Copilot series products. Guanyuan data immediately put forward the concept of AI+BI in the industry since 2016, and has been constantly exploring in this direction, accumulating a lot of low-level cognition and ability. This year we are very happy to see that because of the arrival of ChatGPT and large model technology, the process of AI+BI will be greatly accelerated, the threshold for the use of BI in enterprises will be greatly reduced, and the goal of "making every enterprise have 100 times more data consumers" is more and more within reach in the future.

For more than half a year, we have been preparing for the BI Copilot series products that integrate BI and ChatGPT. BI Copilot series products reshape the full link of data analysis through the fusion of BI and large language model, which will further lower the application threshold of data analysis for enterprises, make organizations more agile and make decisions more agile.

Become a data-driven agile decision maker

"in uncertain times, act firmly in a definite direction." this is the last sentence I would like to give to you at the end of sharing. In the past few years, everyone has been saying not to "inner volume", so what is the opposite of "inner volume"? Instead of anti-involution, let alone lying flat, the antonym of inroll is, in our view, "evolution", the evolution of the individual, the evolution of the organization, and the ability to build the future to find certainty in uncertainty.

Agility to the future. Share with you to become a data-driven agile decision maker.

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