What makes the difference between being successful and failing with data and AI?
Today, about 80% of organizations consider data and AI as an essential part of their strategy. However, 7 out of 10 organisations report minimal to no gains from their data & AI initiatives. With businesses heavily invested in data and AI, what makes the difference between being successful and failing with data and AI? In the Data & AI Maturity Journey track we invite different companies to talk about how they became data & AI driven organizations. We share a practical — and widely applicable — maturity journey that demonstrates how organizations grow their data and AI competencies. To reach maturity, companies usually work on two axes: Analytics Capabilities — Data, People & Skills, Tools & Tech — and Business Adoption — Executive Support, Funding, Implementation. But where do you stand in this journey and how can you reach your destination? Watch the talk if you want to understand the phases and drivers of the data maturity journey. Get the chance to learn from other organizations that embarked on this journey before you.
Find Out the AI Maturity Level of Your Organization
This self-assessment provides an initial indication of your organizations' AI maturity level by rating your level of maturity on several key components.
<strong>About Enza Zaden</strong>
Enza Zaden is a global vegetable-breeding company that develops vegetable varieties of more than 30 international and local crops, and with a portfolio that comprises some 1,200 vegetable varieties. These range from sweet peppers, tomatoes, cucumbers and lettuce to bitter gourd and bird’s eyes chillies.
Besides an global vegetable-breeding company, Enza Zaden is also an independent family business. For three generations, an entrepreneurial spirit, long-term vision and focus on innovation have characterised the company’s distinctive features and healthy growth.
Part of Enza Zaden’s strategy is to become more data-driven. And a vital part for this is to have a scalable data platform to capture data from source systems, and to make that data widely available via self-service to different data consumers. One of the main bottlenecks of the current data platform was its lack of scalability, which resulted in a daily data load that filed often. Enza Zaden asked Xebia to re-implement a data platform that could scale and serve as the basis for their strategy.
Two important requirements for the data transformation pipelines were: first, that the data transformations were transparent as to what transformations took place, while second, that the pipelines were easy to handle and could scale well.
To enable this Xebia implemented dbt as Enza Zaden’s main data transformation tool, migrating from notebooks to version-controlled code, which improved transparency and will allow future data analysts from different teams to interact with the data pipelines, instead of relying on a single team/point-of-failure. dbt Also proved valuable for the organisation to increase their visibility and confidence in their data because of features such as data tests, data lineage and data freshness.
As a result, Enza Zaden now has a robust and scalable fundament for their strategically important data.