Introduction to Data Quality and Data Management

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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;

So if you want to know everything about Data Management and Data Governance, 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);

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 3 modules.

In short, even if you only fit one of the three profiles (only Data Management, only Data Governance, 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 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:

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;
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;