Justine is a data governance specialist at Artefact. Drawing on her experience with similar projects in large organizations, she discusses the importance of data governance and Artefact’s specific positioning in this area. The company, specialized in data consulting, intervenes along the entire business value chain, from data strategy to AI project deployment, including governance as an essential prerequisite for successful use case implementation and scaling.
What are the challenges of data governance today?
The amount of data and the number of use cases around data is constantly increasing. First, companies have to deal with the challenge of getting the most possible value out of their data and democratizing it. Good quality, well documented data should allow it to be accessible to the end user.
All of this applies within the framework of ethics and data protection. Data governance is becoming essential to ensure compliance with certain privacy laws. In Europe, the General Data Protection Regulation (GDPR) is in effect and is tending towards becoming the global standard.
This means that the organization must first be able to demonstrate that it knows what data is flowing through its infrastructure. It must be fully transparent about what data it is collecting from its users and be able to delete all data linked to any individual immediately.
Second, migration to the cloud is essential. Three main uses are emerging: Business Intelligence, Artificial Intelligence, and data exploration. By structuring data based on data governance, businesses will be able to offer these three types of uses as a service.
These data products constitute a common, cross-disciplinary good, which requires a dedicated team. These products must be of high quality, but also visible and usable by all. The challenge for companies is data democratization. These data products must also be secure and protected to comply with various regulatory and ethical issues.
How does Artefact support companies in implementing data governance?
At Artefact, we act as a consulting firm. We support all our clients throughout data governance implementation, from strategy to deployment. First, we perform an audit to see where they stand, then define a roadmap to identify areas to work on. Finally, we build a data asset structure into data products and help them choose the technical tools they need.
In our consulting approach, we insist on the importance of data as a vector of value for the company, then we work on deployment, quality tool selection and documentation of governance to give substance to the strategy and make it feasible.
We’ve also set up our own Artefact School of Data, which lets us train data stewards and data owners, essential roles in the implementation of data governance for businesses. Along with this professional training, we also intervene directly in companies to acculturate them to the need for advanced and supported data governance in order to succeed in their AI projects.
What is unique about Artefact’s global vision?
Our strength is that we propose a global data governance model, focusing on end-use cases first. We position data governance as an “asset” of this transformation. We’re able to transcribe use cases into tangible value and be part of a global transformation program.
We also have multidisciplinary experts. There are about 20 of us in France who specialize in data governance, with profiles from different backgrounds: data product owners who model products, data stewards who document and improve quality, but also data engineers and data analysts.
We also have an ecosystem of technology partners with whom we collaborate in an agnostic way. We’re proficient in all the new tools that appear on the market. We have both technical and strategic DNA, and are able to link all of these subjects together to treat them in a holistic and comprehensive way and deploy them to many clients.
Have you got a concrete example of support that you’ve provided?
We assisted one of our major clients with very extensive data assets in their data transformation. The project concerned a redesign of their data governance. When we arrived in mid-2017, we saw that their governance had been approached from a too-technical and not sufficiently “business” perspective. This resulted in a lack of adoption of the necessary tools. To correct this, we linked their governance to their strategic use cases. To do so, we documented the use cases, democratized their access, and improved data quality to ensure good results. The first pilots were a success! We then faced the challenge of scaling up.
In 2020, we assisted this same company in launching a program to accelerate Artificial Intelligence programs and migration to the Google Cloud Platform (GCP). Governance had been positioned as a strategic asset of their transformation and this launch was performed in two stages:
- structuring their “Data Governance Office” and setting up an operating model with data stewards and data custodians, etc.
- structuring their data assets into a large “business domain”, with the choice of tools to operate, etc.
We’re now entering a third phase of industrialization and extension of this AI program. As part of the migration to the cloud, we’re analyzing how we can structure, rationalize and pool our data assets. At the moment, we’ve moved on to the second stage, which consists of structuring our data assets according to these major business families. Next, we’re going to start thinking about the development of tomorrow’s data products, which will serve different categories of use cases.
What can we expect in the future, once everyone has implemented their data governance?
The availability of data will allow the implementation of even more use cases, particularly in the area of Artificial Intelligence. This will accelerate value creation within organizations. It will also allow us to support all the issues surrounding data democratization and decentralization, especially in terms of bringing data closer to the business. Artefact’s mission is to create this bridge between data and business, and we carry it out on a daily basis with our clients. If the data is well structured and clean, if the products are available, and if we have the push-button tools to manipulate them, theoretically in five years, everyone will be able to use data in their daily work!