Data-Informed Strategic Thinking for Senior Analysts and Data Scientists

Course details

For data scientists, connecting your work to a firm’s business strategy can often be difficult both in terms of the business language used by leaders and the analytical tools (and simple explanations) needed to guide their thinking. In this course, learn how to frame analytical results using strategy language so you can better connect your key outputs with the important needs of a firm’s strategic thinkers and decision-makers. Dr. Paul Thurman shows you how to collaborate with business leaders to present data analytic solutions in non-scientific, business-oriented language. Learn what “strategy” means and the role data science plays, discover how to analyze a market and strategy to assess a firm’s competitiveness, and put a market-tested strategy into effect with the three-pronged thinking framework of conceptual, analytical, and operational strategy. Check out this course to help business leaders learn more from analyses and ensure your data-informed recommendations result in implementable strategies!

Learning objectives
Recognize key levels and roles that all data scientists play and how they interact with a firm’s senior
Learn how to develop and to align with a firm’s vision, mission, and values, and what these mean in both business and data science contexts as they relate to a firm’s strategy.
Learn how to quantitatively and qualitatively assess a firm’s macro-market position (relative to other key market players/stakeholders) and that firm’s relative micro-market strengths and weaknesses as they relate to important strategic opportunities and threats in the broader marketplace.
Find out how to develop scorecards (metrics and targets) for strategic success and how to devise action plans to move teams—and analytics—toward a vision.
See how to develop a “plan b” in case initial strategy efforts run into difficulties.
Connect conceptual, analytical, and operational strategic elements—using simple data analytics—so that leaders, managers, and staff all speak the same language (and use the same analyses)

Concepts

0. Introduction

01 - Data-informed strategic thinking
02 - What experience should you have for this course

1. What Is Strategy - Vision, Mission, and Values

03 - What is strategy, and what role does data science play
04 - Leadership and management - Strategic challenges
05 - Why strategy
06 - Three levels of strategic thinking and decision-making
07 - Conceptual strategy - Vision, mission, and values
08 - Practice - How to see a firm's vision

2. Analyzing a Market and a Strategy - How Competitive Is a Firm

09 - Analytical strategy - Assessing a competitive market
10 - Macrostrategy - Porter's five forces
11 - Microstrategy - SWOT
12 - Using SWOTs to derive strategic options
13 - Practice - How to assess a firm's competitiveness

3. Measuring and Planning Success - What Does Good Look Like and How Do I Get There

14 - Operational strategy - Scorecards and action plans
15 - Milestones and obstacles - Folding back and action planning
16 - Metrics and targets for success - The balanced scorecard
17 - Practice - Aligning key metrics and action plans

4. Conceptual Strategy Deep Dive - Developing and Aligning Vision, Mission, and Values

18 - Deeper conceptual thinking - Values, leadership, and conflict
19 - Creating a compelling vision
20 - Practice - Seeing a firm's vision part two
21 - Aligning mission and vision
22 - Practice - Using regression to create alignment

5. Analytical Strategy Deep Dive - Analyzing Key Market Participants

23 - Deeper analytics - Assessing key market participants
24 - Porter's Five (+2) Forces
25 - Practice - Macro-market analytics part two
26 - Assessing variability in markets - Insights from Porter
27 - Practice - Assessing market forces
28 - Competitive advantage - Creating differentiation
29 - Practice - Assessing or creating competitive differentiation

6. Operational Strategy Deep Dive - Monitoring and Measuring Success

30 - Deeper operational thinking - The balanced scorecard and action planning
31 - Practice - Using data science to create a scorecard
32 - Action planning - Designing tasks tied to scorecard metrics
33 - Practice - Assessing competitor impacts on your plans
34 - The missing piece - Contingency planning

7. Course Review - Data-Informed Strategic Thinking and Decision-Making

35 - Review of course objectives
36 - What is strategy - Defined in three levels
37 - Supporting analytics, pitfalls, and a one-page summary

Conclusion

38 - Where do you go from here - Other courses of interest