4x1 Data Management/Governance/Security/Ethics Masterclass
What you'll learn
- You'll learn about the frequent data disciplines (Data Management, Data Governance, Data Stewardship, Data Science), their differences and nuances.
- You'll learn about the most frequent types of Data Quality (DQ) operations, including profiling, parsing, cleansing, standardisation, record merging, and others
- You'll learn about the levels of data sophistication in an organisation, and the usual DM/DG progression from projects to programs to centralised processes.
- You'll learn about the 4 major types of data (master data, reference data, transactional data and metadata), as well as what each means, and how they intersect.
- You don't need prior knowledge (knowledge of data management/data governance naturally helps, but is NOT necessary)
MANAGE YOUR KNOWLEDGE, MANAGE YOUR DATA
There are many activities related to data in organisations.
Data Management (DM), Data Governance (DG), Data Stewardship, Data Science, and many others.
All of these are crucial activities for organisations, especially those trying to protect their data from cyberattacks, complying with regulation, or just trying to improve the quality of their analytics and reports.
Frequently, you can find information on one of these activities, but not all.
And on top of that, many courses use different definitions, so you may become confused.
In short, most courses on data don't fit the minimum requirements.
And this has consequences not just for your career, but yourself personally as well.
What happens when you don't have enough information (or in the adequate format)?
You'll become confused by the myriad data activities, the tools used, which roles and responsibilities each person has, and how they intersect;
You won't be able to properly identify what belongs to Data Management or what belongs to Data Governance - and what should not be done at all;
You'll become frustrated and irritated that you don't know why an operation works, or why it doesn't;
You won't be able to identify what a specific data tool should be used for, and when your current tools don't fit the job;
You won't know how to optimize your DM and DG operations in an organisation, resulting people not taking data seriously, or making obvious mistakes;
You won't be able to know what security controls to apply to what specific data classes, or how to protect data subjects;
So if you want to know everything about Data Management, Data Governance, Data Security and a lot more, what is my proposed solution?
This new course masterclass, of course!
A HIGH-QUALITY COURSE FOR HIGH-QUALITY DATA
Unlike other data management or data governance courses you'll find out there, this course is comprehensive and updated.
In other words, not only did I make sure that you'll find more topics (and more in-depth) than other courses you may find, but I also made sure to keep the information relevant to the types of data quality issues you'll find nowadays.
Data operations may seem complex by nature, but they rely on simple principles.
In this course, you'll learn about the essentials of how data are managed with activities such as profiling and remediation, as well as how data are governed with processes and policies.
Not only that, we'll dive deep into the activities, stakeholders, projects and resources that each discipline entails.
In this 4-hour+ masterclass, you'll find the following modules:
You'll learn about the essential Data Literacy and Considerations (what are the key principles, what are the different data disciplines, usual processes of each, the information lifecycle, and sophistication levels in an organisation);
You'll get to know about Data and Data Quality in specific (including the types of data that exist, the types of data quality issues and their financial impact, the Data Management activity process flow, as well as the data dimensions and tools used);
You'll learn about Data Governance (including how to define principles and policies, what are the specific activities such as data classification, data lineage tracking and more, as well as how to implement DG from scratch in an organisation);
You'll learn about Data Security, Privacy and Ethics (including what security and privacy controls to use to protect data both in physical and logical formats, how to ensure that data subjects are treated fairly by algorithms and across geographies, how to implement and measure data ethics, and more);
By the end of this course, you will know exactly how data are managed and how they are governed in an organisation, to a deep level, including the necessary tools, people, and activities.
The best of this masterclass? Inside you'll find these 4 modules.
In short, even if you only fit one of the three profiles (only Data Management, only Data Governance, only Data Security/Ethics, or only "general" Data Quality knowledge), you will still have a course dedicated to it!
And naturally, if you are interested in multiple of these topics... this is the ultimate package for you.
THE PERFECT COURSE... FOR WHOM?
This course is targeted at different types of people.
Naturally, if you're a current or future data professional, you will find this course useful, as well as if you are any other professional or executive involved in a data project in your organisation.
But even if you're any other type of professional that aims to know more about how data work, you'll find the course useful.
More specifically, you're the ideal student for this course if:
You're someone who wants to know more about data management itself (how to profile datasets, how to parse/cleanse/standardise them, how to link and merge records, or how to enhance data);
You're someone who is interested in data governance (how to define rules and controls for data, how to institute policies, how to define required metadata, how to fill said metadata for different data sources, and many other activities);
You're some who is interested in data security and privacy, or in data ethics (that is, how to ensure that data subjects are protected in terms of data safety, but also in terms of actually being treated fairly by processes and algorithms);
You're someone who wants to know more about data quality in general (what are the usual types of problems, how do they create financial impact in organisations, what are the usual activities to improve DQ, and so on);
LET ME TELL YOU... EVERYTHING
Some people - including me - love to know what they're getting in a package.
And by this, I mean, EVERYTHING that is in the package.
So, here is a list of everything that this masterclass covers:
Data Literacy and Fundamentals
You'll learn about the 4 key principles for any successful data initiative - considering data at assets, monetising them, seeing DG as business and not IT, and gauging your organisation's sophistication level;
You'll learn about the key data disciplines - that is, what is Data Management (DM), what is Data Governance (DG), what is Data Stewardship, and other activities such as Data Science, and terms such as Data Quality (DQ), as well as the specific roles and operations related to each of these in specific;
You'll learn about the main activities in DM and DG. In the case of DM, activities such as profiling data, remediating them, and setting future data validity requirements, and in the case of DG, uncovering business rules, setting policies and expectations for data, and controls to measure DQ, among others;
You'll get to know the different stages of the information lifecycle. Data being created, accessed, changed, deleted, and possibly other intermediate steps, as well as the usual preoccupations and controls at each stage;
You'll learn about the usual progression from projects to processes - how both DM and DG usually start as specific projects with local scope, and usually grow within an organisation, culminating in replicable and centralised processes to manage and govern data;
You'll get to know the possible sophistication levels of an organisation in terms of managing data. Being reactive, with no allocated tools or people, versus having centralised and standardised roles, tools and processes for data operations, and gauging your organisation;
Data and Data Quality Management:
You'll get to know the 4 main types of data. Master data, reference data, transactional data and metadata, as well as the nuances of each and how they intersect;
You'll learn about the types of DQ issues and their financial impact, usually in one of 3 main ways: direct costs, operational inefficiencies, and/or compliance or regulatory sanctions;
You'll learn about the usual DQ improvement process, starting with profiling, usually followed by triage, remediation of the data, and possible setup of automated controls to prevent future errors;
You'll learn about the three main types of DQ actions. Remediating data on the spot, analyzing the root cause of data problems, and/or instituting rules with automated controls to measure/prevent future data problems;
You'll get to know the different data dimensions used when analyzing DQ problems. Completeness, accuracy, timeliness, lineage, and other relevant ones;
You'll know more about the effect of Big Data and/or AI in data management, specifically the consequences both have in terms of the remediation possibilities and the data pipelines;
You'll know more about the tools used for DQ management, including profiling, parsing and standardisation, linking and merging, and data enhancement tools;
You'll get to know data profiling tools and their specific uses, including validating values in datasets, detecting outliers, validating data formats and rules, and/or uncovering implicit business rules;
You'll learn more about parsing and standardisation tools, which usually take data in different formats, parse them into a unified format, and then standardise data in that format, including the possible removal/editing of wrong values ("cleansing");
You'll get to know linking and merging tools, used to prevent duplicates, which usually use a comparison algorithm to establish a match between records, as being the same, which can then be merged;
You'll learn about data enhancement and annotation tools, which allow you to add more data to the current data, when these can't be edited - or don't need to be edited;
You'll learn more about building a business case for DM/DG, including the usual operations and steps, the usual costs and savings mentioned, and how to present it;
You'll learn about the common functions and capabilities enabled by DG, from privacy and security controls to lineage tracking, metadata management, data classification, monitoring and more;
You'll learn about the usual roles and responsibilities in DG, from data owners to data stewards, Accountable Executives, who is the Data Council, the differences between the CDO (Chief Data Officer) and CIO (Chief Information Officer), and others;
You'll learn about data classification, including the 3 major systems used (by priority and criticality, by sensitivity and privacy requirements, and by processing stage), as well as the consequences of each;
You'll learn about what is data stewardship, and how it bridges Data Governance and reality, including multiple tasks related to metadata, master data, reference data, tracking DQ issues, and other activities, as well as the differences between business, technical and operational data stewards;
You'll learn about a day in the life in Data Governance, including the tasks that each role performs, how they coordinate, and how decisions are made;
You'll learn about assessing your organisation in preparation to implement DG, including data management maturity assessments and change capacity assessments, and how to define the scope of a DG program selecting processes, people and tools;
You'll learn about how to engage and obtain buy-in from different stakeholders, how to deal with resistance, how to prioritise the available projects, and how to ensure commitment to DG;
You'll learn about how to architect and define the initial DG program, including the tools used, the data lifecycle stages, the roles and responsibilities involved, and how to bring it all together in a single operating model;
You'll learn about how to deploy DG initially, with a roadmap, milestones, an operating model, and metrics to track;
You'll learn about the possible initiatives complementary to DG, including data-centric projects such as MDM (Master Data Management) or EIM (Enterprise Information Management), analytics and AI projects, and big ERP implementations, as well as how to let DG "piggyback" on these;
You'll learn about how to maintain a DG program, including maintaining key processes such as training and communication, while enforcing behavioral change through change management and controls;
You'll learn about how to scale a DG program, including dealing with resistance by new stakeholders from new projects and defining the federation of DG as it grows within the organisation;
You'll learn about how culture affects DG, including how aspects such as data literacy, commitment and others define the resources and the people accountable for DG;
Data Security, Privacy and Ethics
You'll learn about cryptographic protection (how to protect data with encryption), and measures to take into account;
You'll learn about data retention and disposal - that is, how to minimise retention time for data which may be leaked, as well as dispose of them securely;
You'll learn about locked rooms, locked devices and/or locked ports, placing physical barriers in front of attackers trying to steal data;
You'll learn about physical media protection, that is, how to protect media such as HDDs, USB drives, or paper while at rest, in transit and during their destruction;
You'll learn how service provider assessment and monitoring is done, to prevent third parties from introducing vulnerabilities in your organisation;
You'll learn about how geographical regulation affects data privacy - that is, how different countries may have different demands in terms of data subject privacy;
You'll learn about data governance structures, and how they can ensure that data ethics are taken care of in a centralised, standardised manner;
You'll learn about defining security controls by data classification, which allows us to protect more sensitive data with stricter controls and vice-versa;
You'll learn about media downgrading and/or redacting, which removes sensitive information from a medium to allow it to be shared more freely;
You'll learn about data de-identification and anonymisation, consisting of removing the personal elements of data to be able to use them without exposing PII (personally identifiable information);
You'll learn about the "compliance approach" to data ethics, consisting of treating ethics like a type of compliance the organisation must ensure, and how it enforces ethics;
You'll learn about actually implementing ethics in an organisation, with principles, governance structures and feedback loops, allowing transparency and iteration;
You'll learn about ethical data dimensions, which are just like the "vanilla" dimensions in DM, but specifically to measure ethics, such as Transparency, Fairness, Utility and others;
You'll learn about data processing purposes and authority, which consists of defining specific purposes for which data may be used (or not);
MY INVITATION TO YOU
Remember that you always have a 30-day money-back guarantee, so there is no risk for you.
Also, I suggest you make use of the free preview videos to make sure the course really is a fit. I don't want you to waste your money.
If you think this course is a fit and can take your data quality knowledge to the next level... it would be a pleasure to have you as a student.
See you on the other side!
Who this course is for:
- You're a current (or future) data management or data governance professional
- You're someone involved (willingly or not) in a data-related project, and need to understand it
- You're involved in decision-making related to managing or governing data in an organisation
- You're anyone else curious about data management, data governance, or any other activity related to data
I have what could be considered an unconventional background as a coach. I don’t come from psychology or medicine. In fact, I come from tech. I created two tech startups that reached million-dollar valuations, backed by the MIT-Portugal IEI startup accelerator, afterwards becoming its Intelligence Lead.
After years of coaching and mentoring startup founders on talent management, emotional management, influence and persuasion, among other topics, I started being requested by executives and investors, like venture capitalists, with more complex, large-scale problems.
After years of doing executive work, I started specializing in coaching asset management professionals. With the signing of my first fund manager/CIO clients, I started adapting my performance and influence techniques for purposes such as talent management for PMs and analysts, fundraising from allocators, effective leading a team, and properly assessing talent for compensation/promotion/allocation increases.
I currently provide performance coaching and influence/persuasion coaching for executives and asset management professionals, mostly but not limited to purposes like managing people, leading and closing sales/capital commitments.