4×1 Data Management/Governance/Security/Ethics Masterclass

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;
• Data Governance:
  • 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!