Getting Started With Data Quality
Your complete guide to DQ
There are three types of people who care about data: people who are affected by it (that’s all of us), people who are working to make it better (that’s data managers and people who work in data) and the visionaries who are changing the data industry for the better through technological and cultural innovation.
Data quality is a complex proposition whether you have a PhD in Computer Science or are just beginning your career as a data analyst, and we understand that, so we want you to read this helpful primer on getting to grips with it.
Training, whether on or off the job, should include at least some if not all of these topics and we should remember these lessons as we work with data in our daily life.
- The importance of data quality — what does bad data mean to you, to your business, and to the society we live in?
- Data profiling — you don’t know what you don’t know, but a data profiler does, and you should be using one to profile all of your data, not just a sample.
- Data preparation — did you know data analysts spend up to 80% of their time manually fixing data and making it fit for purpose before they can work on it?
- Data transformation and assurance — if you’re going to do a data migration (moving it from source to target system) you really need to be thinking about this.
- Data quality impact on AI and Machine Learning — Data quality is the brains behind the brawn of your AI and ML efforts.
- Data obfuscation — This means masking test or production data and is pretty useful when it comes to data privacy laws too.
- The cost of poor data quality — you can quickly figure out how much poor quality data is costing your business, the trick is to figure out how much value you could create by improving it.
- Relationship between data quality & data visualization — Kate Strachnyi introduces a method of using data visualization to uncover poor data quality using simple tools and processes that you can learn too.
- Selling data quality to your business — Scott Taylor — the Data Whisperer teaches you how to build the business case for data quality by using the 5 pillars of master data and aligning with the business strategy to demonstrate value.
If you’d like to learn more about the training we offer at the Idata Quality Academy, please visit the webpage. We’re excited to have launched our first training “Getting started with DQ”, and promise there’s lots more exciting stuff on the way.
We’re very grateful to work with people like Kate Strachnyi, George Firican, Scott Taylor the Data Whisperer, Susan Walsh the Data Classifcation Guru and more.
