Data protection

Lesson 14/16 | Study Time: 0 Min

Course: INTRODUCTION TO DATA SCIENCE AND ANALYTICS

·We need to be aware when working with data, especially personally identifiable information, requires some significant legal and regulatory compliance.

·A potential failure could mean serious consequences such as legal lawsuits, fines, and losing consumer trust.

·Regulations are being introduced to tighten up how companies collect, store and use data.

·When working in practice, specialist legal advice is highly recommended.

·Businesses are successful with insights obtained from third-party data.

·When building a business with key business processes relying on data, it is crucial that you are the owner of that data.

·Make sure you own the data for

ü Important business operations

ü Critical decision making

ü Monetization

Data ownership fundamental aspects

1.Own any data that is necessary to your business

2.Have the necessary rights and permissions to collect and use the data.

Ensuring the correct rights are in place

Best practice for verifying data rights

It is important to have metadata which can include

·When and where was the data collected?

·What permissions were given?

General Data Protection Regulation (GDPR)

·It is a new legislation by the EU to protect personal data.

·It defines standardized data protection laws.

·For companies failing to comply, a fine of up to 20 million euros is applied.

Remember:

·Private data has to be protected.

·Private data must be used for the purpose for which it was handed over.

·Communicate to users what data you are going to collect from them and how it will be used.

How to develop consumer trust? Example: The Royal Bank of Scotland

·They developed a new customer data strategy called
“Personology”.

·Its aim is to give some real value back to customers and show them what they do with this data.

·They use big data tools to comb through all customers’ transactions to gain valuable insights for their customers.

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Class Sessions

1- Introduction to Data Analytics 2- What is Data Science 3- Overview 4- Data preprocessing 5- Data Quality Assessment and Use Case 6- Where does Data come from? 7- Who handles th Data 8- BUSINESS INTELLIGENCE 9- Traditional Methods 10- Traditional Forecasting uses 11- Content 12- Machine Learning Uses 13- Introduction to Large Language Model and Generative AI 14- Data protection 15- Data Management practises 16- Tools and Frameworks