Artefact Newsletter – June 29th, 2022.
Edition about data transformation and the
For this monthly edition, we are focusing on data transformation and the challenges Chief Data Officers (CDO) face through articles written from our experts.
You’ll read their views and analysis on several topics, such as data governance: a prerequisite for AI project’s success, datamesh: data path to decentralization, data engineering, RECKITT’s video testimonial about their audience engine project, and more.
The five most pressing challenges Chief Data Officers currently facing
Ghadi Hobeika, Managing Partner at Artefact USA, shares his key insights after leading a breakout session consisting of numerous Chief Data Officers during the North American Data & Analytics Digital Summit : The future of the data leader.
Key obstacles and insights they voiced include:
:
- Clearly outlining their scope of work
- Data governance is key
- Breaking business silos
- Retaining talent through
career development tracks - Building a case for change
Data Governance, a prerequisite for AI project success
In an interview, Justine Nerce, Partner at Artefact, discusses the importance of data governance and Artefact’s specific positioning in this area.
Justine draws on her experience to answer questions such as:
:
- What are the challenges of data governance today?
- How does Artefact support companies in implementing data governance?
- What is unique about Artefact’s global vision?
- Have you got a concrete example of support that you’ve provided?
- What can we expect in the future, once everyone has implemented their data governance?
Data Mesh: the path to data decentralization
Pascal Coggia, UK Managing Director and Partner and Christopher Bannocks, Former Chief Data Officer at Canon & ING Bank, share what Data Mesh is and what it isn’t, why it’s as much a mindset as an approach.
Together, they address some of the most-asked questions, including:
:
- What is Data Mesh? How is it different from a data lake?
- What is meant by “data as a product”?
- What are the benefits for business?
- What are the challenges of Data Mesh for adoption?
- When is a company ready to adopt a Data Mesh strategy?
- What kinds of companies are successfully deploying Data Mesh?
Data management strategies are always evolving and organizations need to be prepared to adapt to changes in order to stay competitive.
What does the future of data engineering look like?
Benoît Goujon, Data Engineer at Artefact, discusses how the data engineer position has evolved and how the responsibilities of the position vary from one company to another.
:
- Data engineers will massively leverage cloud technologies and SaaS products
- Data engineers will spend less time coding and more time monitoring
- Data engineers will switch teams from feature teams to foundation team
Furthermore, Benoît believes that the next generation of data engineers won’t work on a particular data product. Their objective will be to make product teams more productive.
How RECKITT optimized media spend and increased sales with the Artefact Audience Engine’s solution
Watch to see how our team helped Reckitt transform into a digital-first business and use data effectively to increase sales with the Artefact Audience Engine, which uses ML and AI models to turn data into actionable insight. Multi-disciplinary teams helped roll out and scale the Audience Engine across Reckitt’s global business. This
resulted in test and learn campaigns showing 30% increase in media spend ROI!
Whitepaper: “Maximizing the impact of Machine Learning with prescriptive analytics.”
This whitepaper, written by experts at both IBM and Artefact highlights three concrete examples:
:
- Demand planning: Finding the optimal production strategy
- Marketing campaign optimization: Doing more campaigns with less budget
- Pricing strategy: Finding the right price to increase profit
As Artefact is an IBM Silver Business Partner, Artefact Data Consultants closely work with IBM business and technical experts.
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