This is the first article in a 2-part series!!

这是分两部分的系列文章中的第一篇！

组织数据科学 (Organisational Data Science)

Few would argue against the importance of data in today’s highly competitive corporate world. The techniques used to transform this data into actionable insights are crucial to the performance of an organisation. A study carried out by McKinsey & Company reported that companies that lean on their customer analytics are 23 times more likely to outperform competitors in acquiring new users and 19 times more likely to achieve above-average profitability than their non-data-driven competitors.

在当今竞争激烈的企业界，很少有人会反对数据的重要性。 将这些数据转换为可行的见解的技术对于组织的绩效至关重要。 麦肯锡公司(McKinsey＆Company)进行的一项研究表明，与非数据驱动的竞争对手相比，依靠客户分析的公司在获得新用户方面胜过竞争对手的可能性要高23倍，实现高于平均水平的获利能力的可能性要高19倍。

However, the reality is that data is worth very little if you don’t have highly skilled professionals who can derive actionable insights from it. Knowledge is what drives business value, and data science is the process through which this knowledge is created. Being able to harness the power of data science is thus extremely valuable.

但是，现实情况是，如果您没有能从中获得可行见解的高技能专业人士，那么数据将毫无价值。 知识是驱动业务价值的因素，而数据科学则是创建知识的过程。 因此，能够利用数据科学的力量非常有价值。

The problem is that the advantages that could be captured by having an effective data team in place remain elusive to many organisations around the world, meaning that these businesses will continue to amass large amounts of data with no fundamental understanding of how to use it.

问题在于，通过建立有效的数据团队可以获取的优势对于全球许多组织而言仍然难以捉摸，这意味着这些企业将继续积累大量数据，而对使用数据的方式并没有根本的了解。

The reality is that data science is about giving data a purpose — and this is the job of your data team.

现实情况是，数据科学是关于赋予数据目的的，这是数据团队的工作。

使命宣言 (Mission statement)

The prevailing missions of any data team is to 1) create insights from data and 2) communicate those insights to the relevant stakeholders across the business. Within these united missions exist three basic functions that are fulfilled:

任何数据团队的主要任务是：1)从数据中创建见解，以及2) 将这些见解传达给整个企业的相关利益相关者。 在这些联合任务中，存在以下三个基本功能：

• Decision making: Across any organisation, people need to make impactful decisions. The data team creates or empowers the rest of the business to use their results that make these data-informed decisions possible.

  决策 ：在任何组织中，人们都需要做出有影响力的决策。 数据团队创建或授权其余业务使用其结果，使这些数据相关的决策成为可能。

• Objective setting: Having an effective data team in place means your organisation is on its way to quantifying all measures of success and failure. In doing so, all business objectives become measurable.

  目标设定 ：建立有效的数据团队意味着您的组织正在量化所有衡量成功与失败的指标。 这样，所有业务目标就变得可衡量。

• Monitoring: The data team, together with other business agents, define key indicators at all levels of the business, which are continuously monitored, analysed, and reported on for identifying new opportunities and issues that may arise.

  监视 ：数据团队与其他业务代理一起，在业务的各个级别定义关键指标，对其进行连续监视，分析和报告，以识别可能出现的新机会和新问题。

商业价值创造 (Business value creation)

Now that we know the business goals and functions of the data team, it’s time to consider the value they can bring to the organisation. Below are just some of the many different ways data science can provide actionable business value.

现在我们知道了数据团队的业务目标和职能，是时候考虑他们可以为组织带来的价值了。 以下只是数据科学可提供可行的业务价值的许多不同方式中的一些方式。

1.授权业务代理商 (1. Empower business agents)

By generating otherwise hidden insights from a company’s data, the data team can guide non-technical business agents across the organisation, in different departments, to make better-informed decisions, thus optimising potential outcomes.

通过从公司的数据中生成其他隐藏的见解，数据团队可以指导组织中不同部门的非技术业务代理做出更明智的决策，从而优化潜在结果。

2.帮助实现业务目标 (2. Help achieve business goals)

The data team can guide the upper management levels and the C-level executive team with their analytics to help devise business strategy in critical divisions, including the revenue drivers — marketing and sales — to ultimately improve all business operations and increase profitability.

数据团队可以通过其分析来指导高层管理人员和C级执行团队，以帮助制定关键部门的业务战略，包括收入驱动因素(营销和销售)，以最终改善所有业务运营并提高盈利能力。

3.建立更具数据知识的文化 (3. Create a more data-informed culture)

An effective data team shows everyone how data can be leveraged to generate actionable insights. By doing so they 1) encourage all teams to contribute to greater business value by making more data-informed business decisions, and 2) help the upper echelons of the organisation better understand and appreciate the advantages of data science & and its wide-scale adoption.

一个有效的数据团队会向所有人展示如何利用数据来生成可行的见解。 通过这样做，他们1)鼓励所有团队通过做出更多以数据为依据的业务决策来为更大的业务价值做出贡献，以及2)帮助组织的上层人士更好地理解和欣赏数据科学的优势及其广泛采用。

4.推动实验和创意的产生 (4. Drive experimentation & idea creation)

Companies are constantly experimenting with company data and creating models using this data that simulate a variety of potential actions to show which path is expected to bring the best business outcomes.

公司正在不断地尝试公司数据，并使用该数据创建模型，这些模型可以模拟各种潜在的行动，以显示期望哪个路径带来最佳业务成果。

They can also test the decisions made based on these models to see how they have effected business operations, to measure key metrics that are related to important changes and quantify their success.

他们还可以测试基于这些模型做出的决策，以了解它们如何影响业务运营，衡量与重要变更相关的关键指标并量化其成功。

5.识别新机会 (5. Identify new opportunities)

The job of the data team requires them to continuously and constantly improve the value that is derived from the organisation’s data. They are continuously looking for new opportunities for improvement and developing new methods of analysis, making it possible to discover new revenue streams.

数据团队的工作要求他们持续不断地提高从组织数据中获得的价值。 他们一直在寻找新的改进机会，并开发新的分析方法，从而有可能发现新的收入来源。

6.节省成本和损失 (6. Save costs and losses)

No longer do businesses need to take risks or make uneducated guesses about what will work. Instead, they can make decisions based on quantifiable, reliable data insights. Data science allows you to understand business operations on a whole another level.

企业不再需要冒险或没有根据的猜测会起作用。 相反，他们可以基于可量化，可靠的数据见解做出决策。 数据科学使您可以从另一个角度全面了解业务运营。

From modelling the business cost of retention to analysing workforce turnover, to evaluating management and overhead expenses, data teams can help their companies identify cost-saving opportunities that can potentially improve business functions & increase profitability.

从建模业务保留成本到分析员工流失，再到评估管理和管理费用，数据团队可以帮助他们的公司确定节省成本的机会，这些机会可以改善业务功能并提高盈利能力。

7.获得竞争优势 (7. Gain competitive edge)

A fundamental goal of a firm is to develop and maintain a competitive advantage in the market. But how are these advantages created and maintained in dynamic competitive environments? By identifying (and seizing upon) these market opportunities and outmanoeuvring perceived threats.

企业的基本目标是开发并保持市场竞争优势。 但是，如何在动态竞争环境中创造并保持这些优势？ 通过识别(并抓住)这些市场机会并克服已知的威胁。

All of the answers to unlocking this ability lie in company and market data that, when analysed, allows you to garner insights that drive business value, thus marginalising competitors.

解锁此功能的所有答案都取决于公司和市场数据，这些数据经过分析后，您便可以获取可推动业务价值的见解，从而使竞争对手处于边缘地位。

公司是否正在利用这种创造的价值？ (Are Companies Leveraging This Created Value?)

This brings us to the crux of the discussion — whether or not companies are really leveraging these different types of value created by data teams to help achieve business goals, identify new opportunities & stay ahead of the curve.

这使我们陷入讨论的症结所在—公司是否真的在利用数据团队创造的这些不同类型的价值来帮助实现业务目标，发现新机会并保持领先地位。

To answer this question, one must consider the crucial difference between value creation and value extraction. Any business can employ an effective data team with all the required positions filled by domain experts. And this team can be ingesting, processing, and analysing terabytes of data to generate and report on new & exciting insights (value creation).

要回答这个问题，必须考虑价值创造和价值提取之间的关键区别。 任何企业都可以聘用有效的数据团队，并由域专家填补所有必需的职位。 这个团队可以吸收，处理和分析TB级的数据，以生成和报告新的令人兴奋的见解(价值创造)。

But if these insights are not being effectively communicated to the right audiences around the organisation & thus are not being applied by the various business agents (value extraction), then what is the point in the first place?

但是，如果这些见解没有有效地传达给组织周围的正确受众，因此没有被各种业务代理所采用(价值提取)，那么首先是什么呢？

How to truly leverage the value created by your data team will be the focus of our next article — stay tuned!

如何真正利用数据团队创造的价值将是我们下一篇文章的重点-敬请期待！

Title Photo by Annie Spratt on Unsplash

标题照片， 安妮·斯普拉特 ( Annie Spratt) 在《 Unsplash》上