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* Dataiku
* Stories
* Data Transformation at Rabobank: Execution & Innovation
DATA TRANSFORMATION AT RABOBANK: EXECUTION & INNOVATION
IN THE PAST YEAR AND A HALF, RABOBANK HAS COMPLETED MORE THAN 100 AI PROJECTS
AND HAS REDUCED THE TIME TO ONBOARD DATA TEAM MEMBERS — IN PARTICULAR DATA
SCIENTISTS — FROM MONTHS TO WEEKS.
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100+
AI projects completed in the last year and a half
2 WEEKS
To onboard data scientists, vs. months previously
According to a 2020 report from PwC, 81% of banking CEOs are concerned about the
speed of technological change, more than any other industry sector. Instead of
backing away due to this fear, Rabobank has been able to dive in and transform
their organization to move with the pace of innovation.
Rabobank has been on their data journey since 2011, and true to their mission
and identity, it has been a team effort from the beginning with support from the
top-down at the executive level as well as from the bottom-up from the people
putting in place the technology and processes to execute. This kind of
collaboration and support certainly helps, but it’s not the only driver behind
Rabobank’s transformation around data.In the past year and a half, Rabobank has
completed more than 100 AI projects and has reduced the time to onboard data
team members — in particular data scientists — from months to weeks.
Their ability to do so comes out of their approach to:
* Organizational structure
* Tackle a wide range of use cases
* Create an innovation funnel for use cases
* The education and upskilling of staff
* Technology
We spoke to Rabobank’s Roel Dirks, Product Manager Big Data Lab, and Martin
Leijen, Business Architect Data WR, to understand their keys to success in each
of these areas.
get the full ebook
Watch Video
> “Innovating with AI is a competitive necessity — Rabobank isn’t the only one
> with lots of data and use cases. The speed with which Dataiku is releasing new
> functionality is incredible — Dataiku is leading in the data science and the
> advanced analytics market." Roel Dirks Product Manager, Big Data Lab @
> Rabobank
LESSONS LEARNED
When it comes to raising data maturity across an entire organization, there are
three main takeaways from Rabobank’s journey:
1. It’s not just about technology — the bigger challenge is organizing
everything else around it. While the right technology can certainly make the
journey smoother and be a catalyst for change, it’s not a magic bullet that
will raise an organization’s maturity around data, machine learning, or AI
in and of itself.
2. It’s all about the business — from selecting the right data projects to
delivering value, the business is front and center. Running data initiatives
from an IT team or even a siloed data team alone can never bring the depth
and value brought by domain experts. The secret is getting the two to
collaborate deeply together.
3. It’s a constantly evolving journey — the processes and approaches Rabobank
created in 2014 are no longer applicable, and the team has adjusted them
accordingly. In five years as they become even more mature, systems will
continue to shift to fit their growing needs. For companies just getting
started, Rabobank wouldn’t recommend beginning with their current approach —
it’s something you need to grow toward, customizing for particularities of
your organization.
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FP&A AT STANDARD CHARTERED BANK: BUILDING COLLECTIVE INTELLIGENCE
WITH DATAIKU
On average, two people armed with the Digital MI team's applications in Dataiku
are doing the work of about 70 people limited to spreadsheets. That means
increased analyst productivity by a factor of 30 by replacing spreadsheet-based
processes with governed self-service analytics.
Read more
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economy, deploying dozens of machine learning models to predict behaviour,
incentives, and diffusion, in order to build the next generation of insurance
products.
Learn More
Watch video
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RABOBANK: ETHICAL ENTERPRISE AI – A GUIDELINE OR COMPASS?
In this EGG talk, Martin Leijen shares how Rabobank determines and protects
their privacy and ethical standards, as well as how financial institutions can
we effectively maintain a firm commitment to moral and ethical standards while
at the same time encouraging a strong drive to optimize business opportunities
and profitability.
Learn More
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DEMOCRATIZING AUTOMATED FORECASTING AT MERCEDES-BENZ
Mercedes-Benz enables people on the finance team to combine their expertise with
state-of-the-art machine learning using Dataiku.
Learn More
BGL BNP PARIBAS:
IMPROVING FRAUD DETECTION
See how BGL BNP Paribas was able to improve fraud detection and democratize the
use of data across the organization while maintaining their high standards for
security and data governance.
Learn More
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