下载《数据治理》电子书

Introduction

Many theoretical articles have been written about data governance. At Artefact, we want to address this topic from an operational standpoint with our article series, “Insights from the field”, to give our readers pragmatic and actionable insights. The series will be composed of Artefact’s observations and feedback on key data governance topics (e.g. operating models), answers to questions frequently asked by our clients and prospects (e.g. how to measure the impact of data governance), and interviews with key actors in data governance (e.g. Chief Data Officers, Chief Governance Officers, data custodians, software editors, etc.). Our ambition is to create conversations and opportunities to share experiences within the data governance community. 

Understanding what data governance is, why it is on the critical path to value and how to concretely deploy it in your organisation can be a long road. When companies first launched large transformation programmes with AI and data use cases, they quickly realised these programmes required key enablers such as data platforms, data lakes, data quality management and MDM, which allow quality data to be exposed to use cases. Only recently has data governance appeared as a priority with regard to the myriad of complex systems created.

Understanding what data governance is, why it is on the critical path to value and how to concretely deploy it in your organisation can be a long road. When companies first launched large transformation programmes with AI and data use cases, they quickly realised these programmes required key enablers such as data platforms, data lakes, data quality management and MDM, which allow quality data to be exposed to use cases. Only recently has data governance appeared as a priority with regard to the myriad of complex systems created.

Today, almost everyone in the CDO/CTO ecosystem understands that data governance is a prerequisite to AI transformation. They’re familiar with all the basic organisational and operational concepts, but setting them to music to deliver concrete value is a far more complex task. As a result, data governance is often reduced to isolated documentation initiatives with little impact. We see many programmes being launched, and many heads of data governance appearing in our clients’ organisations. At first, they all encountered the same difficulties in convincing their sponsors and business partners to invest in these activities. Why? Because deploying data governance is a complete learning process that the organisation has to go through, and each step along the path is important.

本报告的目的是帮助你了解什么是数据治理的实践,带你了解每个公司不可避免地经历的数据治理学习过程。这种新的认识将使公司能够更迅速地从一个阶段进入下一个阶段。

We will achieve this by explaining each stage of the learning process in detail, using operational examples from the field:

  1. Stage 1 – Unconscious incompetence: what are the “symptoms” of a lack of data governance?
  2. Stage 2 – Conscious incompetence: what are the challenges encountered by a company when starting a data governance journey?
  3. Stage 3 – Conscious competence: what are the steps companies usually follow when launching a data governance programme?
  4. 第四阶段--无意识的能力:当数据治理成为新常态时会发生什么?

This article will be followed by more operational ones designed to offer pragmatic advice to data governance leaders in their journey.

下载《数据治理》电子书

Conclusion

By following these four learning stages, companies will begin practising data governance without even realising it.

Although the majority of actors we’ve accompanied today still hover between the second and third stages, they all feel the business urgency to move forward in this learning process.

We see three main arguments that justify this urgency. First, data transformation is a key lever in all industries to achieve ambitious goals: data and AI are leveraged to optimise companies’ core businesses and contribute to the development of new growth drivers. Becoming data and AI mature is a requirement in order to remain on the cutting edge, and data transformation isn’t sustainable without data governance. The second argument is that we’re in a fast-moving world. Obtaining speedy access to data and developing the capacity to rapidly analyse and exploit this data is essential to react quickly. And finally, companies need to attract new talents who are increasingly willing to be data-driven.

One of the first challenges for any company at the beginning of its data governance journey is to demonstrate this urgency and convince internal stakeholders of the value of concretely launching a data governance programme.

在这第一份报告中,我们 "照本宣科 "地介绍了数据治理之旅。这是一个系列的介绍性内容,旨在解决在现场实施数据治理的操作问题。我们的下一篇文章将讨论你的数据治理战略和用例的操作需求之间的复杂桥梁。

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