Print advertising includes printed advertisements in newspapers, magazines, brochures, posters, and outdoor boards
Print provides more detailed information, rich imagery, and a longer message life
Print advertising includes printed advertisements in newspapers, magazines, brochures, posters, and outdoor boards
Print provides more detailed information, rich imagery, and a longer message life
We make use of effective promotional tool Sales Promotion in short term to stimulate quicker or greater purchase of particular products or services by consumer or the trade.
The right ad mix brings customers in without over spending.
The following presentation on the structure of an advertising agency was made by Advertising and Public Relations students of Indian Institute of Mass Communication, New Delhi.
We make use of effective promotional tool Sales Promotion in short term to stimulate quicker or greater purchase of particular products or services by consumer or the trade.
The right ad mix brings customers in without over spending.
The following presentation on the structure of an advertising agency was made by Advertising and Public Relations students of Indian Institute of Mass Communication, New Delhi.
1. 数据驱动决策和大数据分析
GANGMIN LI
gangmin .li@xjtlu.edu.cn
GOOGLE SCHOLAR: HTTPS://SCHOLAR.GOOGLE.COM/CITATIONS?USER=_GSTEOIAAAAJ&HL=EN
RESEARCH GATE: HTTPS://WWW.RESEARCHGATE.NET/PROFILE/GANGMIN_LI
LINKEDIN: HTTPS://WWW.LINKEDIN.COM/IN/GARY-GANGMIN-LI-758562B/
6. 1. 什么是决策 DECISION MAKING
• 认知学说: 决策是一个导致在几种可能的替代选
择中产生一种信念或一种行动方式的认知过程。
A cognitive process resulting in the selection of a
belief or a course of action among several possible
alternative options.
• 决策一定会产生结果! Every decision-making process
produces a final choice,
?
• 行为学说:决策是基于决策者的价值观,偏好和信念来确定和选择替代方案
的过程。Decision-making is the process of identifying and choosing alternatives
based on the values, preferences and beliefs of the decision-maker.
• 这个结果未必默认行动。The decision may or may not prompt action.
7. 2. 决策模型DECISION MAKING MODELS
• 理性决策 Rational decision making
• 目标决策 Goal-driven decision making
• 偏爱决策 Retrospective decision making
决策被广泛认为是一种解决问题的能力。(最佳决策和最基本的、满意决策)
Decision-making can be regarded as a problem-solving activity yielding a solution
deemed to be optimal, or at least satisfactory.
合理与不合理决策(以及决策依据的知识是否可以描述)
It is therefore a process which can be more or less rational or irrational
显示与隐式决策
can be based on explicit or tacit knowledge and beliefs.
• Emotion based decision
• Experience (scenario) decision making
• Knowledge based decision making
8. MOD 1. 理性决策 RATIONAL DECISION MAKING
(Classical Model, ECONOMIC MAN MODEL)
• Analysis of a finite set of
alternatives described in terms of
evaluative criteria.Then the task
is to rank these alternatives in
terms of how attractive they are
to the decision-maker(s) when all
the criteria are considered
simultaneously.
针对有限的选择集, 根据确定的
评估标准, 将它们按照对决策者
的吸引力进行排名。根据需要确
定决策结果。
经纪人模型
10. Looking for the best solution among alternatives. explicit, multiple alternatives,
Well defined objective criteria, Limited Biases and timely & Decisive.
理性决策模型的特点
• 在替代方案中寻找最佳解决
方案。
• 明确的多种选择
• 明确定义的客观标准,
• 有限的偏见
• 及时而果断的决定。
11. MOD 2. 有界理性模型
BOUNDED RATIONAL MODEL
(Behavior Model, Administrative Man Model)
Decision-making involve the achievement of a goal.
Rationality demands that the decision-maker should
properly understand the alternative courses of
action for reaching the goals.
Herbert A. Simon defines rationality in terms of
objective and intelligent action. It is characterized by
behavioral nexus between ends and means. If
appropriate means are chosen to reach desired ends
the decision is rational.
Herbert A. Simon
An American economist, political
scientist and cognitive psychologist.
He received the Nobel Prize in
Economics in 1978 and the Turing Award
1975.
目标驱动,
客观合理性结合,
目标和实现手段的联系
管理者模型:
12. MOD 3. 追溯决策模型
RETROSPECTIVE DECISION
MODEL
(Implicit Favorite Model)
这种决策模型侧重于决策者在做出选择
之后如何试图合理化他们的选择,并试
图证明其决策的合理性。
This decision-making model focuses on how
decision-makers attempt to rationalize their
choices after they have been made and try to
justify their decisions.
Per Soelberg
He made an observation regarding the job choice processes of
graduating business students and noted that, in many cases,
the students identified implicit favorites (i.e. the alternative
they wanted) very early in the recruiting and choice process.
However, students continued their search for additional
alternatives and quickly selected the best alternative.
Intuitive, feeling and
implicit first
Logic and rationality last
13. 不同的决策风格
DAFFERENT DECISION MAKING STYLES
• The rational style is an in-depth search for, and a strong consideration of, other options and/or
information prior to making a decision. In this style, the individual would research the new job
being offered, review their current job, and look at the pros and cons of taking the new job versus
staying with their current company.
• The intuitive style is confidence in one's initial feelings and gut reactions. In this style, if the
individual initially prefers the new job because they have a feeling that the work environment is
better suited for them, then they would decide to take the new job.The individual might not make
this decision as soon as the job is offered.
• The dependent style is asking for other people's input and instructions on what decision should be
made. In this style, the individual could ask friends, family, coworkers, etc., but the individual might
not ask all of these people.
• The avoidant style is averting the responsibility of making a decision. In this style, the individual
would not make a decision.Therefore, the individual would stick with their current job.
• The spontaneous style is a need to make a decision as soon as possible rather than waiting to make
a decision. In this style, the individual would either reject or accept the job as soon as it is offered.
18. Data produced every two days
greater than data produced in first
200,000 years of human existence…
Everything is growing Exponentially!
大数据时代 BIG DATA ERA!
19. Data produced every two days
greater than data produced in first
200,000 years of human existence…
Everything is growing Exponentially!
大数据时代 BIG DATA ERA!
大数据正在从2010年的1泽字节增长到2020年的40泽字
节, 预计125 ZB 2025!
20. Data produced every two days
greater than data produced in first
200,000 years of human existence…
Everything is growing Exponentially!
大数据时代 BIG DATA ERA!
大数据,不仅在于数量大,更在于复杂度高!
大数据正在从2010年的1泽字节增长到2020年的40泽字
节, 预计125 ZB 2025!
27. POWER OF BIG DATA ( OPOPPORTUNITY )
大数据带来什么?改变了什么?能做什么?
• The first time in human history we are able to
describe the “whole” rather than “sample”. It help
us to understand things more complete and
thorough.
• Larger collection of data make the pattern of
“things” becomes visible
• Under stand the history and now make it possible
to predict future.
• Large collections of data recording the nature of
things make it possible to be learnt by machine
• Continue update things with high speed make it
possible NOT to miss any development.
• 人类史无前例的可以描述全部,而不是
“样本”! 改变了科学的观念和手段。
• 大规模的数据是的一些未知的开始显现
其规律和样板。 天气预报
• 不间断的、近乎实时的数据更新使得任
何信息不会被错过。工业监控
• 可以通过过去和现在,预测未来。 防灾。
• 学习成为可能从而可能制造出智能机器。
AlphaGo。
28. DATA IS FOOD 数据就是食品
How the data linked with
decision making?
数据和决策的联系?
DATA IS OIL 数据就是原油
DATA IS SOIL数据就是土壤
DATA ISAIR 数据就是空气和氧气
DATA IS MONEY 数据就是财富
32. OUTLINE 提纲
I. 什么是数据驱动的决策?
1. 决策理论和模型概述
2. 大数据时代:数据驱动决策的源动力
3. 数据驱动决策模型
II. 支持数据驱动决策的大数据分析技术
1. 描述性数据分析(陈述数据反映的事实)
2. 探索性分析(针对数据描述的事实,做引申、推理、联想
的探索)
3. 预测性分析 (根据已经掌握事实和知识对特定属性进行预
测)
III. 建立数据驱动决策的企业文化助力企业成功
1. 数据驱动决策的企业的基本要素
2. 如何建立数据驱动决策的企业文化
3. 数据驱动决策的未来
33. 3. 什么是数据驱动决策? (DATA DRIVEN DECISION MAKING -
DDDM)
• Data driven decision making (DDDM) is a
process that involves collecting data based on
measurable goals or KPIs, analyzing patterns
and facts from these insights, and utilizing them
to develop strategies and activities that benefit
the business in a number of areas.
数据驱动决策(DDDM)是一个过程,
涉及基于可衡量的目标或关键表现指
标( KPI )收集数据,从这些见解中
分析模式和事实,并利用它们来开发
使业务在许多领域 中受益的策略和活
动。
-- BI and BDA term
数据驱动决策(DDDM)定义为使用事
实,指标和数据来指导与业务目标,宗
旨和计划相一致的战略业务决策。
Data-driven decision making (DDDM) is
defined as using facts, metrics, and data to
guide strategic business decisions that align
with business goals, objectives, and
initiatives. -- management
34. 数据驱动决策模型 (6步模型)
DATA-DRIVEN DECISIONS MODEL
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
Step 3 - Collect and prepare the data you need
Step 4 -View and explore data
Step 5 - Develop insights
确定业务目标
咨询业务团队以获取关键数据源
收集和准备你需要的数据
审视和探索你的数据
发展形成见解
Step 6 - Act on and share your insights 分享您的见解并采取行动:
36. 数据驱动决策的6个有效步骤
6 STEPSTO EFFECTIVELY MAKE DATA-DRIVEN DECISIONS
要确保决策的正确性合理性。你必须广泛征求意见。
了解机构中不同角色对于机构目标的理解,以及他
们可能提出的问题以及该问题涉及的数据来源。
来自整个组织的宝贵意见将有助于指导您的分析部
署和未来状态,包括角色,职责,体系结构和流程,
以及进度的理解和成功的度量。
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
确定业务目标
咨询业务团队以获取关键数据源
37. 数据驱动决策的6个有效步骤
6 STEPSTO EFFECTIVELY MAKE DATA-DRIVEN DECISIONS
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
确定业务目标
咨询业务团队以获取关键数据源
Step 3 - Collect and prepare the data you need
收集和准备你需要的数据
数据准备也叫数据预处理
如果您的业务信息位于许多不连贯的来源中,那么访问质
量可靠的数据可能会成为一大障碍。 一旦了解了整个组织
中数据源的广度,便可以开始数据准备。执行数据预处理。
首先准备具有高影响力,高质量和低复杂性的数据源。 优
先考虑具有最大受众群体的数据源,以便您立即产生影响。
使用这些资源来开始构建高影响力的仪表板。
38. 数据驱动决策的6个有效步骤
6 STEPSTO EFFECTIVELY MAKE DATA-DRIVEN DECISIONS
Step 4 -View and explore data
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
确定业务目标
咨询业务团队以获取关键数据源
Step 3 - Collect and prepare the data you need 收集和准备你需要的数据
审视和探索你的数据
查看和浏览数据:可视化数据对DDDM至关重要。 以具有
视觉冲击力的方式表达您的见解,意味着您将更有机会影
响高层领导和其他员工的决策。
借助图表,图表和地图等许多可视元素,数据可视化是查
看和理解数据趋势,离群值和模式的一种便捷方式。 有许
多流行的可视化类型可以有效地显示信息:用于比较的条
形图,用于空间数据的图,用于时间数据的折线图,用于
比较两个度量的散点图等等。
39. 数据驱动决策的6个有效步骤
6 STEPSTO EFFECTIVELY MAKE DATA-DRIVEN DECISIONS
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
Step 3 - Collect and prepare the data you need
Step 4 -View and explore data
Step 5 - Develop insights
确定业务目标
咨询业务团队以获取关键数据源
收集和准备你需要的数据
审视和探索你的数据
发展形成见解
用数据进行批判性思维意味着找到有用的见解并以有用的
参与方式进行交流。
视觉分析是一种询问和回答数据问题的直观方法。 预测分
析可以验证和推翻之前的假设。
从而发现影响成功或解决问题的机会或风险。
40. 数据驱动决策的6个有效步骤
6 STEPSTO EFFECTIVELY MAKE DATA-DRIVEN DECISIONS
Step 1 - Identify business objectives
Step 2 - Survey business teams for key sources of data
Step 3 - Collect and prepare the data you need
Step 4 -View and explore data
Step 5 - Develop insights
确定业务目标
咨询业务团队以获取关键数据源
收集和准备你需要的数据
审视和探索你的数据
发展形成见解
Step 6 - Act on and share your insights
发现见解后,您需要采取行动或与他人分享以进行协作。
一种通常的方法是共享仪表板。 通过使用信息丰富的文本
和交互式可视化来突出显示关键见解,可能会影响听众的
决定,并帮助他们在日常工作中采取更为明智的行动。
分享您的见解并采取行动:
43. • Big data demand new way of process data.
• Excessive information affects problem
processing and tasking, which affects decision-
making.
• Information overload is "a gap between the
volume of information and the tools we have to
assimilate" it.
• too much knowledge can interfere with human
ability to make rational decisions.
以数据为依据的决策的挑战
• 数据量的增加加大了获得数据
内在价值的难度。
• 信息过载增加了“可以处理的
信息量与信息价值的差距”。
• 太多的知识会干扰人类做出理
性决定的能力。
大数据需要新的数据处理方式。
44. OUTLINE 提纲
I. 什么是数据驱动的决策?
1. 决策理论和模型概述
2. 大数据时代:数据驱动决策的源动力
3. 数据驱动决策模型
II. 支持数据驱动决策的大数据分析技术
1. 描述性数据分析(陈述数据反映的事实)
2. 探索性分析(针对数据描述的事实,做引申、推理、联想
的探索)
3. 预测性分析 (根据已经掌握事实和知识对特定属性进行预
测)
III. 建立数据驱动决策的企业文化助力企业成功
1. 数据驱动决策的企业的基本要素
2. 如何建立数据驱动决策的企业文化
3. 数据驱动决策的未来
59. 1. 数据驱动型企业 的要素(标志)
Creating a Data-Driven Organization
Practical Advice from theTrenches
By Carl Anderson
Not big data toolset, but it is culture.
Culture is the dominant aspect that sets expectations of how data are
used and viewed across the organization, and the resources and
training invested in using data as a strategic asset.
1. Data driven culture:数据驱动的企业文化
2. Data democracy: 数据民主的集体结构
a large number of stakeholders throughout the organization
who are vested in data and data quality, the best use of data to
make fact-based decisions, and to leverage data for
competitive advantage.
60. 数据驱动型企业
Collect data
Data quality assurance
Data accessible and queryable
Data expertise and experts
Data Analysis tools
Data report , communication and sharing
3. Data access and control:数据操控的实践
4. Data sustainability : 可持续的数据操作规则
Continuously updating, testing, transforming data
Continuous improvement mindset
Continue using prediction
Continue access situation use weighted variables (data)
61. 2. 如何构建数据驱动企业?
锦囊1: 构建大数据团队 Build the Big DataTeam
Without human involvement or interpretation, Big Data analytics becomes useless, having no
purpose and no value.The execution and the success of a Big Data project depends on the strength
of a team. Data Scientist Data analyst
1. Data mining
2. Data visualization
3. Data analysis
4. Data manipulation
5. Data discovery
Adopt Best practices with Big Data
Start with Big data thinking (start small and thinking big)
Understand and follows Big data analytics process
Collect rightful data with proper means
Adopt proper data analysis methods
Has proper data report and sharing tools
锦囊3: 采用行业的最佳实践
锦囊2: 构建大数据分析技能 Build Big Data Skills
62. Course Name Platform Taught by Free/Paid
Data-Driven Decision Making Coursera (online) Alex Mannella, PwC Paid (but financial aid available)
BusinessAnalytics for Data-Driven
Decision Making
EdX (online)
JohnW. Byers, Chris Dellarocas,
Andrei Lapets, Boston University
Free (without certificate, paid for
certificate)
Data-Driven Decision Making
ManagementConcepts (live
classroom, virtual classroom)
- Paid
Data-Driven Decision Making
Hyper Island In Person (international,
in person)
- Paid (with waivers available)
DataAnalytics Bootcamps
Northeastern University (live
classroom, online)
- Paid
Data Science: Data-Driven Decision
Making
Learn@Forbes (online) Frank Kane, Sundog Education Paid
Data-Driven Decision-Making
Duke’s Fuqua School of Business (in
person)
Alexandre Belloni, Peng Sun, Saša
Pekeč
Paid
Data Driven Decision Making Udemy (online) DouglasClark Paid
Data-Driven Decision Making
Certificate Program
Cleveland State University (online) - Paid
63. 2. 如何构建数据驱动企业?
锦囊4: 避免槽糕实践 Avoiding worst practices
收集重于使用(分析和利用) 数据的价值等于你从中获得的利润,而不是你收集的数据数量
承诺和贪图过多。 忽视了大数据的复杂性,导致决策在没有数据支持下完成,因为不知道哪些数据可用。
过程错误: 问题-决策-数据, 首先明确数据的目的(如何使用)才去确定需要的数据。
而不是收集数据,等待使用;或者收集数据,再找其用途。
gathering data and then trying to find a use for it is wasteful at best and useless at worst.
涉水过深。 忽略数据驱动是一个循序渐进的过程。不可一蹴而就,一夜变天。人们需要理解数据的决
策后果的关系
只要搭了台子,剧情就会展开。“If we build it, they will come.” 过分依赖平台,流程和软件,忘记监督,
更新和提高
错把生产实践当做科研项目。试试看的态度。
忘记跌倒的地方。继续犯同样的错误
人性的弱点: 热情高于行动 ; 胆量大于数字; 认知偏见:Cheery-pick 数据,HiPPO 意见的采纳取决于
薪水,随波逐流- 认同多数人意见groupthinking)
认知的误区:从损失规避和沉没的成本谬误(基于不希望浪费已经花费且无法恢复的资源来做出决定)
到德克萨斯神枪手的谬误(忽略数据差异而强调类同),IKEA效应等 许多其他的现象使我们的想法发
生了偏差。 )
过分和不正当收集(侵害隐私)
64. EXAMPLES: 实例
1. Fact-based Decision-Making at Google:The Oxygen
Project
2. Amazon’s Recommendation Secret
3. UK policy on covit19 at Feb:
-- Delay (not Contain) and herd immunity community immunity
65. AMAZON’S RECOMMENDATION SECRET
Google has created a people analytics department that supports the organisation with making HR
decisions with data. One question google wanted to have an answer to was: do managers actually
matter?
This is a question google has been wrestling with from the outset, where its founders were questioning
the contribution managers make. At some point they actually got rid of all managers and made
everyone an individual contributor, which didn’t really work and managers were brought back in.
Fact-based Decision-Making at Google:The Oxygen Project
UK policy on covit19 at Feb:
-- Delay (not Contain) and herd immunity community immunity
66. 数据驱动成功典型和实例:
1. Covit19:
8 to 15 percent lead (20 July, 2020)
He leads President DonaldTrump 55% to 40% among registered voters. (It's a slightly tighter 54% to 44%
among likely voters).The poll comes on top of other surveys last week from Fox News, NBC News/Wall Street
Journal and Quinnipiac University giving Biden anywhere from an 8 to 15 point advantage.
We are human. All of us are a mixture of emotions and rationality. From the time we were born to now, our brains, thought patterns, personality, and intuition have been developing from the alchemy of our DNA, experiences, education, and life lessons. And, as much as we think we are objective rational thinkers and decision-makers, often we are looking through biased eyes, either fueled by our emotions, or driven by our misguided framing of a situation, and most of the time we don’t even know it.
Rational decision making harnesses rationality and logic to make decisions, leaving emotions and biases behind to ensure objective decisions. There are 5 core best practices for rational decision making.
分析根据评估标准描述的有限选择集。 然后的任务是,在同时考虑所有标准时,根据这些选择对决策者的吸引力来对这些选择进行排名。
其特点是: 在替代方案中寻找最佳解决方案。 明确的多种选择,
明确定义的客观标准,有限的偏见以及及时而果断的决定。
然而, 我们是人类。 我们所有人都是情感与理性的混合体。 从我们出生到现在,我们的大脑,思维方式,性格和直觉已经从我们的DNA炼金术,经验,教育和生活课程中得到发展。 而且,尽管我们认为自己是客观理性的思想家和决策者,但常常是有偏见的人看,要么是由于情绪激动,要么是由于误导情况造成的,大多数时候我们不 甚至知道这一点。
理性决策制定利用理性和逻辑进行决策,而留下情感和偏见以确保客观决策。
理性决策有5种核心最佳实践。
有界理性模型 建立与理性模型基础之上,
不是一味追求理性,而是考虑实际和客观限制和约束。
决策的根本是保证目标的实现。
有界理性模型要求决策者应正确理解实现目标和实现这一目标的替代行动方案。
赫伯特·西蒙(Herbert A. Simon)根据客观和明智的行动来定义理性。 它的特征是目标和手段之间的行为联系。 如果选择适当的方法达到预期的目的,则此决定是合理的。
Derek Wilson, CEO of predictive analytics firm CDO Advisors, says that companies new to DDDM can run into problems by jumping into the deep end. “Start with easy-to-understand problems and solutions. Team members need to be able to understand the data and decisions,” he urges. A common mistake, he says, is “businesses that start with too many process changes at once. Implement data-driven decisions one by one and get quick wins to build confidence in the team.”
Another pitfall involves the inherent complexities of big data. Collecting massive amount of information, integrating it, extracting insights, and turning those insights into action-oriented recommendations in real time present challenges. So, expect to put a lot of work into designing your process, and realize that errors can arise at any stage. Failure to spot them can result in problems or poor decisions, so even with analytics, you can’t work on autopilot.
Human nature also confounds our attempts to make decisions based on data, as we have a maddening tendency to favor our guts over the numbers. While this can sometimes lead to home runs, the tendency subverts the intention of DDDM.
Human cognitive biases can muddy the waters further. We may cherry-pick numbers, falling victim to confirmation bias. Or, the highest paid person’s opinion (the delightfully named HiPPO) may sway us, or we may subscribe to the same assumptions about the data as everyone else (groupthink).
There’s a litany of other phenomena that skews our thinking, from loss aversion and the sunk cost fallacy (making a decision based on not wanting to waste resources that have already been spent and cannot be recovered) to the Texas sharpshooter fallacy (when differences in data are ignored, but similarities are stressed, leading to false conclusions) and the IKEA effect (a cognitive bias in which we place an irrationally high value on things we had a hand in creating).
As a final caution, critics say that over-reliance on data may go too far in removing human judgment from decisions or may dehumanize the subjects of the data, turning staff and customers into “just a number.” Many people also have privacy concerns around large-scale data collection and storage.
Google:
Google is a company in which fact-based decision-making is part of the DNA and where Googlers (that is what Google calls its employees) speak the language of data as part of their culture. In Google the aim is that all decisions are based on data, analytics and scientific experimentation.
Project Oxygen
Within the people analytics department Google has created a group called the Information Lab, which comprises of social scientists who are part of the people analytics department but focus on longer term questions with the aim of conducting innovative research that transforms organisational practice within Google and beyond. This team took on the project of answering the question: Do Managers Matter – codenamed ‘Project Oxygen’.
So the objectives and information needs were clearly defined.
What Data to Use?
The team first looked at the data sources that already existed, which were performance reviews (top down review of managers) & employee survey (bottom up review of managers).
The team took this data and plotted them on a graph which revealed the managers were generally perceived as good.
The problem was that the data didn’t really show a lot of variation so the team decided to split the data into the top and bottom quartile.
Analytics
Using a regression analysis the team was able to show a big difference between these two groups in terms of team productivity, employee happiness, and employee turnover.
In summary, the teams with the better managers were performing better and employees were happier and more likely to stay.
While this has confirmed that good managers do actually make a difference,
it wouldn’t allow Google to act on the data.
The next question they needed an answer to was: What makes a good manager at Google? Answering this question would provide much more usable insights.
New Data Collection
So the team introduced two new data collections. The first was a ‘Great Managers Award’ through which employees could nominate managers they feel were particularly good. As part of the nomination employees had to provide examples of behaviours that they felt showed that the managers were good managers.
The second data set came from interviews with the managers in each of the two quartiles (bottom and top) to understand what they were doing (the managers didn’t know which quartile they were in). The data from the interviews and from the Great Manager Award nominations was then coded using text analysis.
Based on this the analytics team was able to extract the top 8 behaviours of a high scoring manager as well as the top 3 causes why managers are struggling in their role.
If you would like to know the eight factors that make a great manager in Google and the three that don’t then read my separate post on it: 8 Behavious that make a Great Manager at Google – and 3 that don’t
Using the Insights
Google used different ways of sharing these insights with the relevant people including a new manager communication that outlined the findings and expectations. But only sharing the insights wasn’t enough, Google saw a need to act on the insights. There were many concrete actions that followed this analysis, here are some key ones:
Google started to measure people against these behaviours. For that purpose it introduced a new twice-yearly feedback survey
Google decided to continue with the Great Manager Award
Google revised the management training
An Intelligent Company
Google is a great example of how good decision-making should be supported by good data and facts. Google clearly followed the five steps I outline in my book ‘The Intelligent Company: Five steps to success with Evidence-based Management’:
Defining the objectives and information needs: ‘Do managers matter?’ and ‘What makes a good manager within Google?’
Collecting the right data: using existing data from performance reviews and employee surveys and creating new data sets from the award nominations and manager’s interviews.
Analysing the data and turning it into insights: simply plotting of the results, regression analysis and text analysis.
Presenting the Information: new communications to the managers
Making evidence-based decisions: revising the training, measuring performance in line with the findings, introducing new feedback mechanisms.
Pros and cons:
Evidence based decision making is not something that someone, whoever s/he is, just starts doing. It is a process that is learned, often painfully because evidence based management is like science. It humbles you into accepting that what you know changes. It forces you from the beginning to say two very difficult things:
I don't know
I was wrong
This is very difficult for human beings to cope with, especially in companies where knowledge and experience are expected to grow over time instead of changing.