Courses
How to practise as a data professional, in any industry.
Practical implementation guidance for each role that keeps data trustworthy: what the role is responsible for, what it does week to week, and how to do it well.
Coming soon
Six roles, one course for each.
Every organisation that holds data needs these roles filled, whether or not the job titles exist. Each course is built around the work itself, so it applies in banking, mining, health, government or a university alike.
Coming soon
Data custodian
For the people who run the systems and storage where data lives.
- Keeping data available, backed up and recoverable
- Applying access rules the data owner has set
- Recording and reporting incidents and changes
Coming soon
Data steward
For the subject expert who looks after a set of data day to day.
- Writing clear definitions and business rules
- Measuring and improving data quality
- Resolving data issues with owners and users
Coming soon
Data governance specialist
For the person who designs and runs the governance programme.
- Setting up roles, councils and decision rights
- Writing policies and standards people follow
- Measuring progress and reporting to leadership
Coming soon
AI algorithm owner
For the person accountable for an AI model and the decisions it makes.
- Knowing where a model’s data comes from
- Testing for bias, drift and unintended use
- Documenting, monitoring and explaining decisions
Coming soon
Process owner
For the manager of a business process that creates and uses data.
- Building data controls into the way work is done
- Spotting where a process damages data
- Agreeing data needs with the people downstream
Coming soon
Technology owner
For the person accountable for a platform, application or supplier.
- Choosing and contracting technology with data in mind
- Managing supplier and platform risk
- Planning change without losing data or control
Courses will be released one at a time. Each will be included in membership, and sold on its own or in a set of five: see services and prices.
Common data terms, in plain language.
24 terms you will meet in data governance and data management, explained without jargon.
- AI model
- A system trained on data to make predictions, recommendations or content. Its behaviour depends on the data it was trained on and the data it is given.
- Algorithm owner
- The person accountable for an AI model or automated decision: what it is used for, how well it works and what happens when it goes wrong.
- Business glossary
- An agreed list of business terms and their meanings, so that a word like “customer” or “active account” means the same thing everywhere.
- Critical data element
- A data item that matters so much to decisions, reporting or compliance that it is governed more closely than the rest.
- Data catalogue
- An inventory of an organisation’s data that records what exists, where it is, what it means and who is responsible for it.
- Data classification
- Sorting data by how sensitive it is, such as public, internal, confidential or restricted, so it can be protected to the right level.
- Data custodian
- The person or team that operates the systems holding data and applies the controls the data owner decides on.
- Data governance
- The decisions, roles, rules and checks that make sure data is managed properly and used in the right way.
- Data lineage
- The record of where data came from, how it moved and how it changed on its way to a report or decision.
- Data management
- The day-to-day work of collecting, storing, protecting, integrating and maintaining data. Governance sets the rules; management carries them out.
- Data maturity
- How well an organisation’s data practices are established, from handled when problems arise to embedded and routinely improved.
- Data owner
- The senior person accountable for a set of data: who may use it, for what, and to what standard.
- Data policy
- A short, approved statement of what the organisation requires when it comes to data, such as how it is protected, kept or shared.
- Data quality
- How fit data is for its purpose, usually judged on whether it is accurate, complete, timely, consistent and valid.
- Data residency
- Where data is physically stored and processed. It matters because the laws of that country apply to it.
- Data retention
- The rules for how long data is kept and how it is disposed of when it is no longer needed.
- Data sovereignty
- The principle that data is subject to the laws and authorities of the country where it is located.
- Data standard
- A specific, measurable rule that puts a policy into practice, such as the required format of a date or an identity number.
- Data steward
- The subject expert who looks after a set of data day to day: its definitions, its quality and the issues raised about it.
- Master data
- The core records an organisation shares across systems, such as customers, products, suppliers and employees.
- Metadata
- Data about data: what a field means, where it came from, who owns it and when it was last updated.
- Personal information
- Information about an identifiable, living person, and under South Africa’s POPIA also about an existing company or other juristic person.
- Reference data
- Fixed lists used to classify other data, such as country codes, currencies or product categories.
- Responsible AI
- Designing and using AI so that it is lawful, fair, explainable, secure and accountable to a named person.