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Hi! My name is Iulia Stefan and I'm the Marketing Services Manager at DiamondSteam.
DiamondStream is a Marketing Analytics company and we offer
a wide variety of BI products and services. We like to help companies
mine their data for marketing insights. We were founded in 2005 and we started
out in the gaming industry,
working with a large payments provider to help them
mine their transactions data and basically built BI dashboards
to help casinos understand their market situation based on that transactional data
and also on the micro level which patrons they should focus on.
Our process for arriving at the MDM solution of Semarchy was
basically, we didn't know that we needed MDM. We just knew that we had a problem of
merging data sources and oftentimes there were complicated
consolidation rules that needed to be implemented,
and really we had no solution in place other than
home-grown scripts and APIs patched together to
address the minimum requirements. But in terms of
been poised for growth, integrating into social and online gaming, and
also other areas where multiple data sources exist which really
is everywhere in today's environment.
We basically needed a more robust system that would enable us to
implement more sophisticated consolidation rules and also be able to iterate quickly.
We had met with Semarchy in February, we discussed our
business challenges with them, we got underway with our
AWS implementation, and then we reconnected in
August of this year to kick off the project, and by the month-end
we were up and running on Semarchy and we had the MDM solution fully implemented.
The main problems that we solved with Semarchy were really
the foundation and getting something set up that wasn't brittle that was easily adaptable.
We have a lot of different regulations that we have to adhere to in the gaming industry,
and while those were addressed by our earlier system,
looking to the future for other implementations into social and online gaming,
we wanted to be able to set up a system that was going to work for
tomorrow as well as today.
So with Semarchy we were able to do that quickly and without needing
to rely heavily on IT resources.
We're really trying to keep the focus on the business end
and trying to do as much as we can with our partners and vendors
leveraging the thinking, the technology and the leadership that they put into their products
and us just being able to play the data steward role and focus what we do best.
The biggest win is just the time savings and the peace of mind of knowing that
we have a solid solution in place that we know we can rely on the result of,
and also your team can get back to focusing on some of the other challenges
that we're dealing with and
staging our new BI environment.
One is the product obviously. I think there's a lot of
elements that, especially for us as a small business, but also for larger
organizations are really important when you embark on an MDM project.
Being able to keep your eye on the ball and focus on
the business problem and not get bogged down in the technical details.
I think the product does a really nice job of that.
Letting you focus on the consolidation and matching rules which are
business-driven, and then letting the product generate all
of the transactions and physical processes that are required to
support those business rules.
On the company as a whole I think the people
that we've met and worked with, our implementation consultant
was excellent, and he was really there for us every single day,
pushing the project forward and any time if there was a minor hiccup,
we were able to get over it quickly,
within a matter of hours and every day we were not just
staying in place or going backwards. We were making steady progress every day.
We were able to do that in under a month end-to-end.
I think that the Evolutionary MDM concept began to make sense to us
once we really got into it and saw
how important the data quality is for the system not just
flowing one direction from the sources
into the BI Hub. It's that concept of
Convergence and being able to take the
cleansed data and feed it back to the source systems
in places where that make sense and overtime really
you're basically solving up more than just
what's immediately obvious. You're going back to the sources and you're making that
cleaner as well.
And I think also the fact that it supports an agile implementation philosophy.
I think that's where the whole Evolutionary component comes in.
So being able to adapt the data model easily,
as we find out new things about business requirements is critical
for us and I think for any organization because data is growing
exponentially every day and you can't know
ahead of time how the system is gonna look. So you need a tool that's
gonna let you adapt as the world does.