Data engineering, analytics, and AI are talked about constantly but surprisingly few companies have managed to leverage them.

一直在谈论数据工程，分析和AI，但令人惊讶的是，很少有公司能够利用它们。

At my current job (we’re hiring), I work with a number of companies that are working to become more data-driven. It’s hard work. Not just the technical aspects, but the cultural changes that are either a pre-requisite or a result of data-driven decision making.

在我目前的工作(我们正在招聘)中，我与许多正在变得越来越以数据为主导的公司合作。 辛苦了不仅是技术方面，还包括文化变革，这些变革是先决条件或数据驱动决策的结果。

In this post, I want to talk about the improvements that are possible with a proper data culture as well as how to start making the changes necessary to implement a data-driven culture.

在本文中，我想谈一谈使用适当的数据文化可能实现的改进，以及如何开始进行必要的更改以实施数据驱动的文化。

从哪儿开始？ (Where to Start?)

There is a huge disconnect between the ideals some people have of data-driven organizations (We’ll use AI! It will be magic!) to the reality: a lot of work by a lot of engineers to make the data consistent and usable first.

某些人对数据驱动型组织的理想与现实之间存在巨大的脱节(我们将使用AI！这将是不可思议的！)：许多工程师为确保数据的一致性和可用性首先进行了大量工作。

The key to success with data projects is having a crystal clear idea of the business problems you want to solve and then focusing your efforts on the most approachable ideas.

数据项目成功的关键是对要解决的业务问题有一个明确的想法，然后将精力集中在最平易近人的想法上。

可以(相对)直接改善数据的事情： (Things that are (relatively) straightforward to improve with data:)

• Internal operations
  内部运作
• Customer segmentation
  客户细分
• (Digital) product improvements
  (数字)产品改进

These are broad categories, but most companies can find straightforward ways to use data to have a measurable impact on results. And more than almost anything else, being able to measure the impact and ROI of data projects is critical.

这些是广泛的类别，但是大多数公司可以找到直接的方法来使用数据来对结果产生可衡量的影响。 最重要的是，能够衡量数据项目的影响和ROI至关重要。

How can we approach these topics and make an impact?

我们如何处理这些主题并产生影响？

We want to build a time-boxed experiment with the key goal of building internal skills around data. It’s critical that the learning and growing aspect of these projects is stressed. Initial attempts at being data-driven may not have huge ROI payoffs in the short term. Obviously, if they do, that’s ideal, but there are many reasons why these returns don’t happen immediately.

我们希望建立一个有时间限制的实验，其主要目标是围绕数据建立内部技能。 强调这些项目的学习和成长方面至关重要。 短期内，以数据为驱动力的初步尝试可能不会获得巨大的投资回报。 显然，如果这样做的话，那是理想的选择，但是有很多原因导致这些回报不会立即发生的原因很多。

As you do these experiments, it’s critical that you move business metrics from being descriptive to predictive.

在进行这些实验时，将业务指标从描述性变为预测性至关重要。

评估您的数据成熟度 (Evaluate your Data Maturity Level)

Every company, even every division within a company, is at a certain level of data maturity. Data maturity is a concept that measures your ability to use data to drive your business success.

每个公司，甚至公司内的每个部门，都具有一定的数据成熟度。 数据成熟度是一个衡量您使用数据来推动业务成功的能力的概念。

I would say most companies are, at best, level 3. And typically it is only parts of the company that have even gone that far such as marketing or finance where analytics have become normalized.

我要说的是，大多数公司充其量只能达到第3级。通常，只有公司的一部分才能做到这一点，例如行销或财务分析就已经标准化。

Data maturity has many sub-domains. In order to be successful, the business needs to mature across all the different sub-domains. Technology skills are not sufficient. It’s critical to have the right processes, controls, and interpretation skills to really get value out of data projects.

数据成熟度有许多子域。 为了获得成功，业务需要在所有不同的子域中成熟。 技术技能不足。 拥有正确的流程，控制和解释技能，才能真正从数据项目中获取价值至关重要。

If you aren’t sure where you in terms of data maturity, here are two quick “gut check” mechanisms:

如果不确定数据成熟度，这里有两种快速的“检查肠道”机制：

选择您的数据项目 (Pick your data project)

Once you have some sense of where you are in terms of data maturity, there are two general ways forward: Improve in an area where you have low data maturity, or drive forward by making a strong area even stronger. The right answer depends on your organization and the resources you can bring to the project.

一旦了解了数据成熟度所在的位置，便有两种通用的前进方法：在数据成熟度较低的区域中进行改进，或者通过使强大的区域变得更强而向前迈进。 正确的答案取决于您的组织和可以带给项目的资源。

Once you’ve picked your project, you need to align stakeholders across the organization around the necessary technical, procedural, and governance aspects of your project.

选择项目后，您需要围绕项目的必要技术，程序和治理方面调整组织中的利益相关者。

Finding or building data skills can take time due to skyrocketing demand. Understand your needs early (as best you can) so that you have time to find the right people or build internal skills before things become urgent.

由于需求激增，发现或建立数据技能可能会花费一些时间。 尽早了解您的需求(尽您所能)，以便在事情变得紧急之前有时间找到合适的人或建立内部技能。

The typical sequence of needs is Data Infrastructure (cloud) > Data Engineering > Data Science > Visualization/reports/metrics.

需求的典型顺序是：数据基础结构(云)>数据工程>数据科学>可视化/报告/指标。

建议：如果您尚未使用云技术，请开始。 (Suggestion: If you aren’t already using cloud tech, start.)

Data storage needs almost never get any smaller. It’s a lot easier and cheaper to scale using one of the cloud computing providers. Beyond storage and processing, AWS, Google Cloud, and Azure all provide a variety of data analysis tools that are mostly “plug-n-play” once your data is in the right place.

数据存储需求几乎永远不会变小。 使用其中一个云计算提供商进行扩展，既容易又便宜。 除了存储和处理之外，AWS，Google Cloud和Azure都提供了各种数据分析工具，一旦数据放置在正确的位置，这些工具大部分都是“即插即用”的。

建立组织范围的数据架构 (Build an organization-wide data architecture)

The maximum value in data projects is realized when data is shared and utilized across the organization. It’s best if some thought is put towards a consistent data architecture for the organization from the beginning.

在整个组织中共享和利用数据时，可以实现数据项目的最大价值。 最好是从一开始就对组织的一致数据架构进行思考。

The actual technologies used can vary depending on the nature of your company and your data sources, the cloud provider you use (or if you do not use one of the cloud options, the on-prem options). This is why it’s important to find the right people who can guide your organization, whether internally or externally.

实际使用的技术可能会因公司性质和数据源，所使用的云提供商(或者如果不使用云选项之一，即本地选项)而异。 这就是为什么找到合适的人员来指导您的组织(无论是内部还是外部)至关重要。

入门 (Getting Started)

1. Begin moving to the cloud for scale and superior tooling.

   开始迁移到云中以进行扩展和提供出色的工具。

2. Evaluate and document your key business cases and data maturity.

   评估并记录您的关键业务案例和数据成熟度。

3. Evaluate your organization’s internal skills and capacity for change. Make plans to build those skills or hire or them.

   评估组织的内部技能和变革能力。 制定计划来培养那些技能或雇用他们。

4. Create a set of predictive experiments that can answer your questions.

   创建一组可以回答您问题的预测性实验。

5. Measure results, get some wins, iterate.

   测量结果，获得一些胜利，反复进行。

If you need help, don’t be afraid to reach out to Wizeline.

如果您需要帮助，请不要害怕与Wizeline接触。

您可以在此处观看完整的演讲： (You can watch the full talk here:)