Gartner Data & Analytics Outcomes

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Gartner Data & Analytics Outcomes by Mind Map: Gartner Data & Analytics Outcomes

1. The Story

1.1. Exposition

1.1.1. Was this conference worthwhile?

1.1.1.1. Let's look how it went down by the numbers

1.1.1.1.1. trip

1.1.2. The timing was perfect for this conference

1.1.2.1. we are just concluding x months of research to gain knowledge of the data and analytics space

1.1.2.1.1. too focused on data

1.1.2.2. this newly minted knowledge enabled us to leverage everything the conference had to its fullest

1.1.3. broad summary

1.1.3.1. bad news

1.1.3.1.1. 85% of data science projects fail

1.1.3.2. good news

1.1.3.2.1. This conference has enabled us with the knowledge to avoid falling into the common pitfalls a data platform project can encounter

1.1.3.2.2. There are many new and interesting roles needed to make Analytics a success

1.1.4. We went in to discover where Analytics is going not where we have been

1.1.4.1. toonces

1.1.4.2. sandbox analogy

1.1.4.2.1. buckets

1.2. Rising action

1.2.1. Lessons learned

1.2.1.1. Strategies for success

1.2.1.1.1. Always focus on the business problem to be answered

1.2.1.1.2. Build platform iteratively

1.2.1.1.3. Stop managing data, go fishing

1.2.1.1.4. data literacy

1.2.1.1.5. Insight on tools to build a data platform

1.2.1.1.6. The platform is the central focus of the information infrastructure of an organization

1.2.1.2. trends

1.2.1.2.1. top 10 things keeping CIOs up at night

1.2.1.2.2. all in one product

1.2.1.2.3. big data

1.2.1.2.4. 47% of time spent talking about data models

1.2.1.2.5. only 13% of data science teams report to IT

1.2.1.3. data for good not evil

1.2.1.3.1. Privacy

1.2.1.3.2. unintended consequences

1.2.1.4. Tech insights

1.2.1.4.1. data lakes

1.2.1.4.2. data hubs

1.2.1.5. we initially focused very narrowly

1.2.1.5.1. take one step back and assess other business cases

1.3. Climax

1.3.1. With these lessons we will

1.3.1.1. We are aligning ourselves with the ITIL, DevOps, Agile practices that ITS would like to follow

1.3.1.2. Take a right sized business problem

1.3.1.2.1. has good visibility

1.3.1.2.2. not too much work to complete

1.3.1.2.3. demonstrates a solid improvement over the existing process

1.3.1.3. Expand our list of existing business cases

1.3.1.3.1. Faculty of Arts & Science

1.3.1.3.2. School of Graduate Studies

1.3.1.3.3. IRP

1.3.1.4. tooling

1.3.1.4.1. erwin

1.3.1.4.2. cataloging

1.3.1.4.3. cloud service provider

1.3.2. What is the data platform?

1.4. Falling Action

1.4.1. So many opportunities!

1.4.1.1. How to choose?

1.4.1.1.1. Advancement

1.4.1.1.2. Administration

1.4.1.1.3. Research

1.4.2. vendors

1.4.2.1. types

1.4.2.1.1. tools to help with platform

1.4.2.1.2. All in one solutions

1.5. Resolution

1.5.1. Was the conference worthwhile?

1.5.1.1. YES!

2. Glenn's sessions

2.1. Keynote 1 Clarity of Purpose

2.1.1. Questions

2.1.1.1. What do we want to do with our data?

2.1.1.2. How could we use our data to make Queen's better?

2.1.1.2.1. What is better?

2.1.2. insights

2.1.2.1. focus on metrics that DRIVE change not just final summary reports or KPIs

2.1.2.2. meaningful metrics require experimentation

2.1.2.2.1. adopt an experimental mindset

2.1.2.3. Be the guide and guardian of your customer's data

2.1.2.4. Demonstrate how you use the data for their benefit

2.1.2.5. Principles lead, Rules follow

2.2. Mon 1:30 Foundation Metrics that matter

2.2.1. insights

2.2.1.1. How do good metrics deliver value?

2.2.1.1.1. Improve ability to measure, classify, decide

2.2.1.2. Data Literacy is key

2.2.1.2.1. plain language

2.2.1.3. Make sure the people being measured have buy in

2.2.2. questions

2.2.2.1. could we personalize learning?

2.2.2.1.1. classify students based on learning styles

2.3. Mon 11:30 from BI to AI

2.3.1. insights

2.3.1.1. clients ask for dashboards & self service

2.3.1.1.1. what they want is to improve business process and outcomes

2.3.1.2. Analytics moments

2.3.1.2.1. processes that support the delivery of your business outcomes

2.3.1.3. How to build an architecture that supports the business outcomes

2.3.1.3.1. Start with the outcome and work your way back

2.3.1.3.2. Blend each new Data & Analytics block with the existing landscape

2.3.1.3.3. example

2.4. Mon 1:30 The future of BI isn't a BI tool (Looker)

2.4.1. insights

2.4.1.1. does transform on read

2.4.1.2. good at addressing ETL explosion

2.5. Mon 3:15 AI with Power BI

2.5.1. insights

2.5.1.1. Data Flows

2.5.1.1.1. reusable ETL

2.6. Mon 4:15 How to Start, Evolve and Expand Self-Service Analytics

2.6.1. insights

2.6.1.1. What is self service

2.6.1.1.1. Some BI

2.6.1.1.2. Mostly analytics (Tableau)

2.6.1.2. Why does BI fail?

2.6.1.2.1. workflow too long

2.6.1.3. solution

2.6.1.3.1. the Analytics sandbox

2.6.1.3.2. Plan-> Pilot -> Deploy

2.7. Tues Keynote

2.7.1. People believe first and vet later

2.7.2. we are poor belief collaborators

2.7.2.1. once we have a belief

2.7.2.1.1. notice and seek out confirming evidence

2.7.2.1.2. actively work to discredit disconfirming evidence

2.7.2.1.3. even when we find out the evidence is wrong our beliefs are still affected

2.7.3. Smart people are better at supporting their beliefs with data ( not necessarily a good thing)

2.7.4. promote transparency in process

2.7.4.1. people will be more forgiving for failure

3. stuff cut out

3.1. IRP may be too large for an initial iteration

3.1.1. work done on a smaller project will be good prep for this larger project

3.2. climax

3.2.1. With these lessons we will

3.2.1.1. build infrastructure iteratively

3.2.1.1.1. A. build first iteration solid base

3.2.1.1.2. or B. Take the right sized use case

3.2.1.2. use business problems to drive growth of platform

3.2.1.2.1. Advancement

3.2.1.2.2. Administration

3.2.1.2.3. Research