Scotiabank – Big Data, Small Brains: Making Effective Decisions in a World of Data Overload by Lisa Ritchie #MRIA15 #MRX


MRIA15,MRIA2015Live blogged from the 2015 MRIA National Conference in Toronto. Any errors or bad jokes are my own.

Scotiabank – Big Data, Small Brains: Making Effective Decisions in a World of Data Overload
Lisa Ritchie, Scotiabank

  • Started department with 2 people many years ago and now there are about 180 people – data warehouse, data support, campaign execution, analytics and insights, brand and communication research
  • Major changes in big data – market research was the core, the catalyst. Companies started getting repositories, more information became relevant. It morphed and became bigger. Reliance on information became bigger.
  • Quant is rational, concise, part of the why; Qual is exploratory of the why, more emotive
  • Big data – is there such a thing as little data, little information? Vendors made this thing sound so much bigger. Technology gives us information at a faster pace.  The notion of big data came from technology vendors.
  • The information was always there, we’re just getting it faster. It is blinding. The big brain comes into play here.
  • Big data is now at your fingertips. You know how many times people have walked into your branch or made a savings deposit or what channel they use to deposit.
  • Were able to understand that their bank wasn’t doing well with young customers.  Had to figure that out – how to attract young customers to a bank, any bank. This information at their fingertips helped. Needed to see that people open an account because their parents opened it, and then they go off to college. Now, it is the best at attracting young people.
  • Canadians are the highest users of loyalty programs.
  • Biggest key to success is going back to basics and creating a structure. Data can give you any answer so research needs discipline.
  • Can you tell a powerful story with big data? It’s different for structured and unstructured data. Piecemeal doesn’t work. Some analytics people are brilliant at the data but can’t tell a story. And vice versa.
  • Secret to integration – need to hypothesis, synthesize, know what you’re looking for. It’s not about looking for one thing
  • Most data has 8 to 10 years of history. You can go back.
  • Don’t lose sight that a project from ten years ago might reveal new insights.
  • The problem is knowing what to do with the big data. Need to learn how to ask questions to use that big data. “Yeah, yeah, yeah, but what I meant was…”
  • Researchers need to be integrators, be proactive. Need to be real consultants. This is where we’re losing business – to people who ARE consultants.
  • Journey will be fast and furious. The googles of the world are using data, and we need to be intuitive as well.
  • She just got an email “Welcome to Metcan” but she’s been a customer for 10 years. They just sent a generic email even though their big data could tell them very different.
  • Learn the skill of synthesizing. Think forward.
  • Suppliers need to work with clients in a tighter relationship. Bring learning from elsewhere. This is what consultants do.
  • It’s not just presentations. It’s communicating and interpreting and suppliers can help do this.
  • She says her success has been luck [You make your own luck!]
  • 330 terabytes of data [wish my laptop had that!]
  • There is a push for everyone to have access to data. Need to make sure data is anonymous. [READ ONLY! READ ONLY!]
  • When everyone has access to data, interpretation becomes really key.
  • Don’t archive things so fast – what’s old is new again. Don’t underestimate the usefulness of old research.
  • Does technology mean faster and cheaper – no. It’s a myth. Asking questions takes time.

%d bloggers like this: