Leading with data and digital
Data, AI and agile for public service leaders.
Over several days, this flagship builds leadership in data, AI and agile: data methods and meaning, evidence in complexity, how AI works and what it delivers, AI governance and assurance, and agile delivery.
- Level
- Flagship
- Learning time
- 26 hours 30 minutes
- Where
- Complexity, decisions and innovation
- Price
- £549
When you step up to a higher level, what you paid in the last 12 months for lower-level courses inside the bigger course comes off its price, in proportion to how much of each is inside it, whichever topic it's from. Courses at the same or a lower level don't give credit against each other. A Focus course or above always costs at least £15. Read the full terms
Certificate of Completion, or Certificate of Completion and Assessment with a short assessment
Overview and Outcomes.
Who It's For
Public service data, digital and transformation leaders and their senior sponsors
Overview
Data, AI and new ways of working are changing how public services are designed and decided, and leaders stay accountable for all of it. This flagship begins with data methods and meaning: why data is never raw, how meaning is made socially, the seduction of formulas and whose data counts, drawing on Gitelman, Weick and Data Feminism. It then turns to evidence in complexity, from narrative evidence to realist and developmental evaluation, dashboards and data ethics. AI follows: how it works, the productivity evidence, data readiness, suppliers, law, fairness, oversight and assurance, with Robodebt and Horizon. It concludes with agile ways of working and leading people through adoption.
Aims
The purpose is to develop public service leaders who use data honestly, govern AI with real oversight rather than reassurance, make credible claims about impact in complex systems, and shape delivery around learning and users, staying accountable for every decision that technology touches.
In summary, you will be able to:
- Explain how meaning is made from data, and use five questions about power to examine who is counted and who decides.
- Assess headline claims about AI productivity, and distinguish hollow from meaningful human oversight of AI systems.
- Plan agile delivery and an AI adoption rhythm that keep teams learning in short cycles while protecting professional judgement.
What you'll develop
Through the programme, you will be able to:
- Explain how human choices at each of the six steps of the data journey, from first question to final decision, shape the figures you are given.
- Explain how proxies, weights and thresholds carry values inside an allocation formula, using the RAWP and NICE cases.
- Describe the steps of a cost benefit analysis and how social cost benefit analysis adapts them for public decisions.
- Describe how organisational culture shapes what data is allowed to say, and identify the features of a healthy data culture.
- Explain why AI outputs inherit the quality of their data and why named ownership of data sets matters.
- Distinguish assurance from reassurance, and describe the roles of the three lines, external bodies and the governing body in assuring AI.
- Identify waste and failure demand in your own service, using Ohno's seven wastes and Seddon's distinction between value demand and failure demand.
- Explain what shadow AI is and why bans tend to drive use underground rather than stop it.
Built from 19 parts.
26 hours 30 minutes of learning.
- 01Method follows the questionData collection methods, types and qualityBitesize · Understanding Data and Data Analysis3 modules
- 02Seeing differentlyReading data honestly and differing perceptionsBitesize · Understanding Data and Data Analysis2 modules
- 03When formulas decideSocial meaning and the seduction of objectivityBitesize · Understanding Data and Data Analysis2 modules
- 04Consequences and uncertaintyUnintended consequences and sensemaking with dataBitesize · Understanding Data and Data Analysis2 modules
- 05Making sense togetherWhose data, and collective sensemaking methodsBitesize · Understanding Data and Data Analysis2 modules
- 06Weighing the optionsCost benefit analysis and data for decisionsBitesize · Solving Complex Problems and Decision Making2 modules
- 07Stories as evidenceQualitative evidence and evaluation in complexityBitesize · Understanding Data and Data Analysis2 modules
- 08Targets and public dataMeasures, dashboards and public data in Wales and the UKBitesize · Understanding Data and Data Analysis2 modules
- 09Fair dataData ethics and algorithmic fairness, with case studiesBitesize · Understanding Data and Data Analysis2 modules
- 10Beyond the hypeAI for leaders: capability, limits and valueBitesize · AI for Leaders3 modules
- 11Good data, wise buyingData readiness and AI procurementBitesize · AI for Leaders2 modules
- 12Right, fair and lawfulEU AI Act, AI ethics and algorithmic biasBitesize · AI for Leaders2 modules
- 13Keeping humans in chargeAI governance and oversight in public servicesBitesize · AI for Leaders2 modules
- 14Assured and openAI assurance, transparency and public scrutinyBitesize · AI and Governance2 modules
- 15Being agile, not doing agileAgile principles, mindset and ScrumBitesize · Agile and New Ways of Working3 modules
- 16Flow and the freedom to testLean, Kanban and test-and-learnBitesize · Agile and New Ways of Working2 modules
- 17Starting with peopleDesign thinking principles and processBitesize · Creativity and Innovation1 module
- 18Agile at scale, honestlyScaling agile, governance and fundingBitesize · Agile and New Ways of Working2 modules
- 19Bringing people with youAI literacy, skills and leading adoptionBitesize · AI for Leaders2 modules