ICE: How to overcome barriers in data capture projects

ICE: How to overcome barriers in data capture projects

Tuesday, February 2, 2016 Totally Gaming

It is crucial to explain to colleagues the benefits of data capture and analytics in order to justify investing in major projects, according to Christopher Conroy, head of customer data science at Rank Group.

Speaking at ICE Totally Gaming at the ExCel in London, Conroy offered his company as a case study by outlining the process of building a data lake, and explaining how operators need to avoid the “spend small, fail fast” scenario.

“Many organisations are quite traditional in their structures in terms of investment decisions and there are always lots of people to go through,” said Conroy, whose company runs Grosvenor Casinos and Mecca Bingo.

“We made a decision to build a data lake and that was a big investment. We really focused on explaining the capabilities internally.

“A data lake does not give you things immediately, but it does offer the ability to do things in the future.

“Once you get the initial sign-off from finance, you then start thinking about some other aspects, as the data lake is really building blocks for other things.

“Finance colleagues will question why you can’t do it with cheap plug-ins or they say ‘can’t we do this in Excel?’.

“We thought big and asked what the ‘best in class’ would look like in terms of personalisation. What was the halcyon vision? You’ll start to talk about new information and building the business case.”

The complex process of not only implementing a data lake, but also persuading the necessary internal departments to loosen the purse strings for such an investment, is an ongoing process, according to Conroy.

“Part of the job is that you have to spend time doing work to understand what you should have,” he said. “There comes a time when you have to scale it back a bit and do things in an incremental way.

“Also, the people building it will want prioritisation of aspects, so some things will happen sooner than others.

“Outsourcing for acceleration has been important for us, and there will be times when you need to get experts to come into a room with data scientists to speed things up.

“So you have to explain the capabilities that the data lake is bringing into the business and how the process will be accelerated, but I confess it’s not an easy conversation.

“In pitching for investment, the articulation of capabilities required is a constantly beating drum.”

Conroy admitted that the intersection of data science and investment appraisal “can seem like a dichotomous place”.

However, he added: “When people buy in to capability, then you can talk about money.”

Conroy concluded: “Classic data science techniques apply. Start small, fail fast; scale in stages, prototype and visualise. These are key methods.

“Communicating to the business that the building of a data lake or similar is the beginning, rather than the end of a data science evolution, is key.”


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