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

這是分兩部分的系列文章中的第一篇！

組織數據科學 (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.

在當今競爭激烈的企業界，很少有人會反對數據的重要性。 將這些數據轉換為可行的見解的技術對于組織的績效至關重要。 麥肯錫公司(McKinsey＆Company)進行的一項研究表明，與非數據驅動的競爭對手相比，依靠客戶分析的公司在獲得新用戶方面勝過競爭對手的可能性要高23倍，實現高于平均水平的獲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，現實情況是，如果您沒有能從中獲得可行見解的高技能專業人士，那么數據將毫無價值。 知識是驅動業務價值的因素，而數據科學則是創建知識的過程。 因此，能夠利用數據科學的力量非常有價值。

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.

問題在于，通過建立有效的數據團隊可以獲取的優勢對于全球許多組織而言仍然難以捉摸，這意味著這些企業將繼續積累大量數據，而對使用數據的方式并沒有根本的了解。

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

現實情況是，數據科學是關于賦予數據目的的，這是數據團隊的工作。

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:

任何數據團隊的主要任務是：1)從數據中創建見解，以及2) 將這些見解傳達給整個企業的相關利益相關者。 在這些聯合任務中，存在以下三個基本功能：

• 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.

  決策 ：在任何組織中，人們都需要做出有影響力的決策。 數據團隊創建或授權其余業務使用其結果，使這些數據相關的決策成為可能。

• 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.

  目標設定 ：建立有效的數據團隊意味著您的組織正在量化所有衡量成功與失敗的指標。 這樣，所有業務目標就變得可衡量。

• 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.

  監視 ：數據團隊與其他業務代理一起，在業務的各個級別定義關鍵指標，對其進行連續監視，分析和報告，以識別可能出現的新機會和新問題。

商業價值創造 (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.

現在我們知道了數據團隊的業務目標和職能，是時候考慮他們可以為組織帶來的價值了。 以下只是數據科學可提供可行的業務價值的許多不同方式中的一些方式。

1.授權業務代理商 (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.

通過從公司的數據中生成其他隱藏的見解，數據團隊可以指導組織中不同部門的非技術業務代理做出更明智的決策，從而優化潛在結果。

2.幫助實現業務目標 (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.

數據團隊可以通過其分析來指導高層管理人員和C級執行團隊，以幫助制定關鍵部門的業務戰略，包括收入驅動因素(營銷和銷售)，以最終改善所有業務運營并提高盈利能力。

3.建立更具數據知識的文化 (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.

一個有效的數據團隊會向所有人展示如何利用數據來生成可行的見解。 通過這樣做，他們1)鼓勵所有團隊通過做出更多以數據為依據的業務決策來為更大的業務價值做出貢獻，以及2)幫助組織的上層人士更好地理解和欣賞數據科學的優勢及其廣泛采用。

4.推動實驗和創意的產生 (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.

公司正在不斷地嘗試公司數據，并使用該數據創建模型，這些模型可以模擬各種潛在的行動，以顯示期望哪個路徑帶來最佳業務成果。

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.

他們還可以測試基于這些模型做出的決策，以了解它們如何影響業務運營，衡量與重要變更相關的關鍵指標并量化其成功。

5.識別新機會 (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.

數據團隊的工作要求他們持續不斷地提高從組織數據中獲得的價值。 他們一直在尋找新的改進機會，并開發新的分析方法，從而有可能發現新的收入來源。

6.節省成本和損失 (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.

企業不再需要冒險或沒有根據的猜測會起作用。 相反，他們可以基于可量化，可靠的數據見解做出決策。 數據科學使您可以從另一個角度全面了解業務運營。

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.

從建模業務保留成本到分析員工流失，再到評估管理和管理費用，數據團隊可以幫助他們的公司確定節省成本的機會，這些機會可以改善業務功能并提高盈利能力。

7.獲得競爭優勢 (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.

企業的基本目標是開發并保持市場競爭優勢。 但是，如何在動態競爭環境中創造并保持這些優勢？ 通過識別(并抓住)這些市場機會并克服已知的威脅。

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.

解鎖此功能的所有答案都取決于公司和市場數據，這些數據經過分析后，您便可以獲取可推動業務價值的見解，從而使競爭對手處于邊緣地位。

公司是否正在利用這種創造的價值？ (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.

這使我們陷入討論的癥結所在—公司是否真的在利用數據團隊創造的這些不同類型的價值來幫助實現業務目標，發現新機會并保持領先地位。

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).

要回答這個問題，必須考慮價值創造和價值提取之間的關鍵區別。 任何企業都可以聘用有效的數據團隊，并由域專家填補所有必需的職位。 這個團隊可以吸收，處理和分析TB級的數據，以生成和報告新的令人興奮的見解(價值創造)。

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，如果這些見解沒有有效地傳達給組織周圍的正確受眾，因此沒有被各種業務代理所采用(價值提取)，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!

如何真正利用數據團隊創造的價值將是我們下一篇文章的重點-敬請期待！

Title Photo by Annie Spratt on Unsplash

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