Chapter 1 - Introduction

1.01 Introduction to the Course (7 min.) Sample Lesson
1.02 The Analytics Value Chain (6 min.) Quiz: 1.02 The Analytics Value Chain
1.03 Three Pillars of High Functioning Technology Practices (7 min.)
1.04 Six Key Themes for Delivering Value (19 min.) Quiz: 1.04 Six Key Themes for Delivering Value

Chapter 2 - Strategy

2.01 Introduction to Legacy Technology Strategy and the Role of Analytics (11 min.)
2.02 Understanding the Current State (10 min.) Quiz: 2.02 Understanding the Current State
2.03 Defining the Mandate and Mission (11 min.) Quiz: 2.03 Defining the Mandate and Mission
2.04 Target Operating Model Design Principles (9 min.) Quiz: 2.04 Target Operating Model Design Principles
2.05 Defining Functions & Services (8 min.) Quiz: 2.05 Defining Functions & Services
2.06 Capability Mapping (18 min.) Quiz: 2.06 Capability Mapping
2.07 Interaction Models (13 min.) Quiz: 2.07 Interaction Models
2.08 Team Composition & Personas (8 min.) Quiz: 2.08 Team Composition & Personas
2.09a Organizational Design and Target Operating Models - Part 1 (11 min.)
2.09b Organizational Design and Target Operating Models - Part 2 (13 min.) Quiz: 2.09b Organizational Design and Target Operating Models - Part 2
2.10 Strategy Summary (7 min.)

Chapter 3 - Process

3.01 Delivery Model Introduction (16 min.)
3.02 Governance (18 min.) Quiz: 3.02 Governance
3.03 Intake and Prioritization (13 min.) Quiz: 3.03 Intake and Prioritization
3.04a Scoping and Planning - Part 1 (15 min.)
3.04b Scoping and Planning - Part 2 (13 min.) Quiz: 3.04b Scoping and Planning - Part 2
3.05a Project Execution - Part 1 (14 min.)
3.05b Project Execution - Part 2 (22 min.) Quiz: 3.05b Project Execution - Part 2
3.06 Process Summary (6 min.)

Chapter 4 - People

4.01 Practitioners as Artisans (14 min.)
4.02a Hiring and Onboarding - Part 1 (13 min.)
4.02b Hiring and Onboarding - Part 2 (17 min.) Quiz: 4.02b Hiring and Onboarding - Part 2
4.03 Talent Development and Retention (14 min.) Quiz: 4.03 Talent Development and Retention
4.04 Succession Planning (13 min.) Quiz: 4.04 Succession Planning
4.05 Relationship Mapping (16 min.) Quiz: 4.05 Relationship Mapping
4.06 Team Conventions and Culture (16 min.) Quiz: 4.06 Team Conventions and Culture
4.07 Mentorship and Coaching (11 min.) Quiz: 4.07 Mentorship and Coaching
4.08 Eminence Building (7 min.) Quiz: 4.08 Eminence Building
4.09 Owning the Narrative and Community of Practice (11 min.) Quiz: 4.09 Owning the Narrative and Community of Practice
4.10 Managing Conflict (16 min.) Quiz: 4.10 Managing Conflict
4.11 People Summary (8 min.)

Technical Strategy & Leadership / Chapter 1 - Introduction

Lesson 1.01 Introduction to the Course

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Transcript

01. Lesson 1.01: Introduction to the Course 02. Jeremy Adamson Bio 03. Thought Leaders: PWC 04. Failure to launch 05. Thought Leaders: New Vantage Partners 06. Thought Leaders: McKinsey 07. Minding the Machines 08. Summary

01. Lesson 1.01: Introduction to the Course

Hi there, welcome to Technical Strategy & Leadership. This is Session 1 which is an Introduction to the Course.

02. Jeremy Adamson Bio

My name's Jeremy Adamson and I'll be your instructor. My background is a little bit nontraditional. I'm not a computer scientist, I'm not a software developer. I was actually originally a civil engineer focusing on capital projects. But through a lot of great opportunities, I found myself in the field of data science and analytics. So for the last 10 years, I've been working with organizations in oil and gas, in financial services, and the public sector, really to help them unlock value from their investments in the technology practice. So what my background has given me, I would say, is a little bit of a different perspective. I'm a business guy trying to solve business problems using analytics. So this course won't have any tough mathematics, it won't have any coding. It's going to be focused on how we can make analytics work in a bigger picture. And this course is, for the most part, function and industry-agnostic but wherever possible, I'm going to draw on examples from energy and resources. But we shouldn't restrain ourselves or limit ourselves just to that industry. There's definitely a lot to learn from other industries in terms of best practices with organizing technology and analytics.
Secondly, this course covers a lot of material. It touches on creating an operating model on Agile, software development methodologies, all the way down to how to conduct one-on-one's with your reports. So the intention will be to compress basically all of the non-technical aspects of a technical practice into as dense a version as I can. And in that compression, of course, there may be missing detail, but as much as possible, I tried to find a good balance there.
And then the last thing I'll say on myself is that helping companies realize value from data science and analytics as well as helping individual practitioners along in their careers, both are an absolute passion of mine. So, please reach out to me any time if you have any specific questions that I can help you through.

03. Thought Leaders: PWC

So why is the topic of strategy and leadership of analytics and technology teams so important? First off, it's becoming much, much more commonplace. You think back as recently as a few years ago, investments in data science and analytics, it was seen as an investment in the future. More and more though, it is becoming a cornerstone of corporate strategy. So I have here a quote from a recent survey by PricewaterhouseCoopers and they found that 86% of American executives thought that AI was going to be a "mainstream technology" in their organizations. And it just isn't an investment in the future anymore, it's touching every part of the business.

04. Failure to launch

Another survey by Deloitte found that half of Canadian executives said thatdata science and analytics was going to fundamentally change the way the organizations were run. So that's a very bold statement. But at the same time, the vast majority of projects in this field are ending failure. We've got several studies here, Gartner finding 85%, TechRepublic 80%, Capgemini 70%. So executives in North America, they know it works. They know that it is absolutely essential to being competitive in the future, but for the most part, these organizations just aren't able to execute.

05. Thought Leaders: New Vantage Partners

So what's the reason? If you asked anybody in the field 5 years ago, the answer would be technology. We in practice, we thought that once we had that Hadoop cluster, once we had a data lake, once we sunset all of these old infrastructures, that value would just emerge on its own. And over time, we're coming to realize that this isn't a technical problem but a cultural one. I've got a survey here from New Vantage Partners, and what are they saying? In 2018, 80% of respondents were saying that the main obstacle to success for their technology function was people, process and culture. And at that point, only 20% were saying that it was technology. This year, that's up to 92% that are saying it's a cultural problem. This is a real issue and more and more people are realizing it's not just technology.

06. Thought Leaders: McKinsey

McKinsey has a similar view. They see technology as enabling the process, but that is defined by people. And if we're going to get value out of the data and organizations, we need to have the right people and the right processes. They see technology just as a consequence of that and not a driver.

07. Minding the Machines

And data science and analytics, it's evolved a lot from its origins in business intelligence and reporting. It's becoming a core component of corporate strategy and an enabler for all parts of the business. And throughout this course, it's my intention to help you understand how to move from traditional analytics where you have a backroom function, largely order takers who are basically providing insight generating activities a la carte to more of a value-driven analytics where they're involved in the scoping of these projects, where they are injecting their knowledge into those processes, where they have a seat at the table and they are actively looking through the organization for opportunities to pull value from analytics. And this material comes from my book "Mining the Machines", which focuses on how to build and lead high-performing data science and analytics teams. So as a consequence whether you are a data scientist working in oil and gas trying to get a model into production, whether you are a senior leader trying to understand the practice or even a reservoir engineer interfacing with the data engineering group, I really want to help you to unlock the value of data science and analytics in your organizations and to be able to make meaningful change.

08. Summary

So what are the key takeaways for this session? Firstly, even though almost everybody agrees that it's absolutely critical to the organizations, most data science and analytics initiatives are ending in failure.
The main reason for that is a focus on technology above all else.
And if we're going to make meaningful change in our organizations, we really need to reconsider our old approaches and look at the practice more holistically.
So in the next section, I'm going to get into what I see as the 3-key pillars to being able to unlock value from analytics. Thank you.
Ammanath, Beena, David Jarvis, and Susanne Hupfer. "Thriving in the era of pervasive AI." Deloitte, rep (2020).NewVantage Partners Releases 2021 Big Data and AI Executive Survey McKinsey: Winning with AI is a State of Mind 2021 Adamson, Jeremy. Minding the Machines: Building and Leading Data Science and Analytics Teams. John Wiley & Sons, 2021.