Online Interactive Training: Improving decision-making effectiveness through the use of data thinking (Chinese Session) (Full-day)

课程大纲

一：数据思维：面对复杂多变的业务场景，数据如何提升决策效能

1、 VUCA时代我们应该如何更好的突破决策困境？

2、 数据如何帮助我们更好的突破职场困境实现自我跃迁

3、 理清数字/数据/数据分析基本概念告别职场做表工具人

4、 用数据分析解决问题的三步骤：理清目标——拆解指标——分析判断

• 理清目标—从构建一个好的业务问题开始
• 定义指标—统一数据口径做好数据收集
• 分析判断—基于数据得出结论并传递数据结论

二：目标导向：理清业务目标，少做无用功，高效解决真问题

1.  找到核心业务目标，从定性分析走向定量分析

2.  构建好的业务问题，迈出数据分析重要一步

3.  理清业务上下游，让你的数据流动起来告别单点决策

4.  三个思考框架帮梳理各项业务逻辑找到核心目标因子

三、目标拆解：根据目标梳理业务流程，拆分目标体系找到关键点

1、 构成逻辑：把总业绩分解到小渠道，谁优谁劣一目了然

• 用好5W2H让你穷尽可能找到各项影响因素
• 用好逻辑树把复杂问题拆解成若干简单问题
• 公式拆解将大目标拆分成可以指导执行的工作包

2、 转化逻辑：把大流程拆解成小步骤，快速发现关键环节

• 资源有限情况下如何发挥优势取得最好成绩？
• 拆解业务流程找到核心瓶颈才能集中资源重点突破
• 无论是海盗模型还是招聘漏斗背后都是业务流程
• 学会分析用户行为路径才能抓住核心转化流程
• 结合用户决策模型，提升关键业务转化率

3、 拆解逻辑：把大目标拆分成小指标，排除干扰，找到核心指标

• 重点关注三大核心领域，找到业务流程核心指标元素
• OSM模型将北极性指标拆解成业务指标实现可落地执行
• 区分过程类和结果类指标，力出一孔做好关键因素监控

四、分析判断：3种数据分析法，让规律有迹可循

1、 对比分析：4种对比分析法，帮你洞察业务指标间的规律

• 对比分析核心逻辑：求同存异
• 学会四种对比方式，迅速定位问题核心
• 比率才是发现问题的关键指标

2、 趋势分析：面对未来不确定性，如何制定目标更精准

• 阿里双十一的交易额给我们带来的一些启示
• 基于趋势测算告别靠拍脑袋的方式定目标
• 预测分析的两种类型及背后的统计学方法运用

3、 分布分析：不怕影响因素多，2套万能模型，帮你轻松判断优先级

• 用好分布分析，告别被平均，让分析结果更接近真相
• 面对海量数据，借助分布分析实现精准化管理运营
• 划分分布标准，让数据回归平面，结果呈现一目了然

五、数据呈现：可视化呈现分析结果，提升汇报决策效率

1、 数据图表：为决策增加说服力，轻松打动老板和同事

• 做具备业务属性的图表而不是好看的图表
• 六个维度理实现数据图表发现信息辅助决策
• 构建数据可视化图表辅助实现业务监控运营

2、 数据报表：让数据更新自动化，全面提升效率和竞争力

• 建立决策驾驶舱实现站在上帝视角全局查看数据需求
• 借助各项工具完成数据仪表板设计及数据自动化操作

Course Outline

I. Data-driven thinking: How data can improve decision-making in the face of complex and changing business scenarios.

1. In the VUCA era, how should we better break through the decision-making dilemma?

2. How data-thinking can help us break through workplace dilemmas to achieve self-improvement?

3. Understand the basic concepts of data analysis and stop being a watchmaker in the workplace.

4. The three steps to solve problems with data analysis: clarify the target - disassemble the indicators - analysis and judgment.

• Clarify the target - start with building a good business problem.
• Define indicators- Harmonisation of data calibre and completion of data collection.
• Analysis and judgment - Draw conclusions based on data and communicate data findings between teams.

II. Goal-oriented: clarify the business objectives, and efficiently solve real problems.

1. Find the core business objectives, from qualitative analysis to quantitative analysis.

2. Build a well-constructed business issue, and take an important step in data analysis.

3. Sort out the business upstream and downstream, No more single point of decision making between teams through data transfer.

4. Use three thinking frameworks to sort out the business logic and find the core target factors.

III. Objective disassembly: sort out business processes according to the objectives and find the key points by disassembling the objective system.

1. Composition logic: break down the total performance to small channels, so that the strengths and weaknesses are clear at a glance

• The 5W2H allows you to find all the possible influencing factors.
•  Use logic tree to break down complex problems into a number of simple ones
• With formula disassembly, to break down the big goal into work packages that can guide the implementation

2.Transformation logic: break down the large process into small steps to quickly discover the key components

• Resources are limited, how to take advantage to get the best results?
• Dismantle business processes and find the core bottlenecks, the only way to focus resources on breakthroughs.
• Whether a pirate model or recruitment funnel, behind all are business processes.
• Learn to analyze the path of user behavior, in order to seize the core conversion process.
• Combine the user decision model to improve the key business conversion rate.

3. Disassemble the logic: split the big goal into small indicators, eliminate interference and find the core.

• Focus on the three core areas to find the core indicator elements in the business process.
• Use OSM model to break down arctic indicators into business indicators to achieve grounded execution.
• Distinguish between process and result-based indicators, force to do a good job of monitoring key factors.

IV. Analysis and judgment: 3 data analysis methods to keep track of patterns.

1. Contrast analysis: 4 kinds of comparative analysis method, 1. Help trainees gain insight into the patterns between business indicators.

• Core logic of comparative analysis: seek common ground while reserving differences.
• Learn four ways to compare and contrast to quickly get to the heart of the matter.
• Ratio is the key indicator to find the problem

2. Trend analysis: how to set goals more precisely in the face of an uncertain future.

• Ali's Double Eleven trading volume gives us inspirations.
• Target setting based on trend measurement rather than a pipe dream approach.
• Two types of predictive analysis and the use of statistical methods.

3. Distribution analysis: two sets of universal model to help you easily determine the priority.

• With distribution analysis, no longer be averaged, make the analysis results closer to the truth.
• In the face of massive data, use distribution analysis to achieve accurate management operations.
• Divide the distribution criteria, let the data return to the plane, so that the results are presented at a glance.

V. Data presentation: visualize the analysis results to improve the efficiency of reporting and decision-making.

1.Data chart: add persuasive power to decisions and easily impress leaders and colleagues.

• Make a chart with business attributes, not a good-looking chart.
• Six dimensions to achieve data charts to discover information and assist in decision-making.
• Build data visualization charts to assist the realization of business monitoring operations

2. Data reports: Overall efficiency and competitiveness through automated data updates.

• Create a decision cockpit to see data needs from a global perspective.
• Complete data dashboard design and data automation operations with the help of various tools.