Big Data: has it lived up to our expectations? Greg Nichelsen Head of Customer Insights & Analytics

Transcription

1 Big Data: has it lived up to our expectations? Greg Nichelsen Head of Customer Insights & Analytics

2 Agenda 1. Who am I 2. Why all the attention on data and analytics 3. What is Big Data 4. Is it working as well as we hoped 5. What can we do to improve the situation 2

3 Who Am I? Greg Nichelsen Head of Customer Insights & Analytics BT Financial Group Chief Marketing Officer Head of Customer Insight & Analytics Customer Experience & Customer Centricity Customer Segments & Analytics Customer & Market Research Campaign Insights & Execution 3

4 A word before I start we are on a journey 4

5 Winning on customer experience and what part does data and analytics play? Deeply know who our current and potential customers are to ensure we always deliver on needs and make every moment with them count Deep and broad data sources delivering both insight and actionability to operational processes Anticipate what customers want next, giving them options that help make their experiences with us as easy as possible Remember your customers and personalise their experiences Create an engagement that is emotionally rewarding, captures attention and is worth talking about Build interactions for engagement, gamification and attention 5

6 What is Big Data? A technology An attitude 6

7 Something about big data 7

8 Have all our big data initiatives gone as we expected Sharing my thoughts on some of the challenges 8

9 The challenge of the data scientist: I m living with a black box on my head Definitions of data science vary greatly but in general compared to traditional statistics driven analysts, data scientists will have: broad technical skills data sourcing and consuming a drive to use entire sets of data rather than sampling strong skills in data exploration Data Scientist 9

10 The challenge of the data scientist: I m living with a black box on my head When you merge this unique set of skills it does create some challenges within the organisation. What is the appetite in my organisation for success and failure? Are we driven by business priority or interesting data? Do my data team have the business acumen to shutdown invalid hypothesis in a timely manner? Do we understand when a model is simply predicting core business processes before we present it back to the business? Are we able to describe in business terms what our models are predicting and be prepared to be challenged? 10

11 The challenge of drinking from the fire hose: Sorting the wheat from the chaff There is still a data due diligence that needs to be done Take care with true causal relationships Is there meaning in the data you are throwing away? Every day, three times per second, we produce the equivalent of the amount of data that the Library of Congress has in its entire print collection, right? But most of it is like cat videos on YouTube or 13-yearolds exchanging text messages about the next Twilight movie. Nate Silver 11

12 The challenge of low engagement: Hello! Is there anybody out there? 12

13 The challenge of low engagement: Hello! Is there anybody out there? No amount of Big Data can help when there is no engagement with the relationship 13

14 The challenge of low impact analytics: Are you and your stakeholders in the cone of silence? How would you rate these statements about your organisation? 1. We have top down support for data driven initiatives 2. Our analytics team have the appropriate position in the organisation to make a difference 3. Insights are driving decisions at the appropriate points in strategy, planning and execution 4. My organisation sees both customer and data as a way to win in our industry 5. Our data people have the stakeholder management, presentation skills and engaging consulting skills to push a data driven agenda in the organisation 14

15 The challenge of using lack of insight as an excuse: Playing the blame game 1. Miss opportunities 2. Undermines analytic creditability 3. Devalues the time of analysts 4. Creates tensions between teams Continue to get more and more analysis done well beyond the point of actionability Commission analysis to prove your point but say it was wrong when it disproves it Do you go with gut feel but say it has been driven by data and analytics Use analytics as default answer in status reports to explain poor performance 15

16 The challenge of speed of insight creation: Insight at the speed of thought! The initial Big Data pitch: speed of analysis data to insight to action That was in contrast to: traditional data sourcing traditional data consumption However, traditional methods already had competition that was far faster than anything Big Data was able to deliver Also known as: I asked a customer and My partner said.. A friend told me at a BBQ on the weekend we should I went to a conference and I had a great idea in the shower 16

17 The challenge of speed of insight creation: Insight at the speed of thought! Own Judgement and Experience 54% Asked a Customer 9% Colleagues & Peers 8% Experts 8% Managers 6% Company Data 6% Market Research 5% Third Party 4% n=291, MREB Customer Focus Survey 2011 Makeup of sources that inform customer-related decisions 17

18 How can we address these issues to ensure our Big Data initiatives are more successful? Lessons for Organisation: Lessons for your Data Teams: Lessons for your Capability: You need executive sponsorship that truly recognises the competitive advantage of rapid, agile, large scale data in customer experience You need to build beyond data scientist to a business scientist that balances business acumen with technical skills For low engagement products and experiences, build for engagement and engage analytics to ensure data capture is part of the initiative Analytics needs to be positioned in the organisation so it has an authority to drive strategies and outcomes it can t be seen only as a service to be engaged as needed Build strong skills in data story telling with backup of stakeholder engagement and consulting abilities so that analytic opportunities are equally heard at the table Don t lose sight of data quality, governance and meta-data. You can t by-pass understanding the business context and associated quality of the data from which you are making decisions. 18

19 In Summary Big Data has a huge role to play in winning on customer experience. On truly understanding our customer s needs, to personalise experiences to make them easy and to create connections that are emotionally engaging. We are missing opportunities to leverage analytics and insights as deeply in our organisations as we could due to flaws in the way we are approaching our data initiatives or the way in which we are structuring our capability. Are your data and analytics initiatives and teams appropriately sponsored and positioned in the organisation? Are you hiring for and providing training in the right skills to ensure your insights teams are equipped to drive change? Are you aware of your short comings in terms of data knowledge and data coverage and are you building for those? 19

20 Questions? 20