Opinion
A one-on-one discussion on planning today for a stronger tomorrow
Data ethics is the application of ethical values to the collection, storage, processing and sharing of data for business purposes. It involves applying ethical principles to protect an organisation’s stakeholders against harm resulting from poorly managed or mishandled data.
Data is a term that has been used to describe any quantity or abstract description of information. The difference between data and information is subtle, but important: Data refers to the actual numerical representation of an entity or object, whereas information is just about what something means.
For example, if you asked me how old I was yesterday (in human years), I would say “twenty-four” with some confidence — but if you asked me what color my eyes were, I’d be more likely to answer “green” because there’s no way for either person to know whether they’re wrong without looking into it further.
This makes sense: How do we know how old someone really is unless they provide us with proof? If someone tells us they’re twenty-four but doesn’t have any documentation on file showing them as such (e.g., birth certificate), then maybe all hope isn’t lost yet! In this case however…
Data is the fuel that powers the digital economy. It’s what makes things happen, it’s what makes our devices run, and it’s key to finding new ways of doing things — from self-driving cars to virtual reality apps.
Data is also becoming more valuable as time goes on, because it can be processed by machine learning algorithms that learn from past behavior patterns in order to make predictions about future events.
Data is like oil: once you have a good supply of it (like oil), you’re able to get more done with less effort than before because your machines can do more for you with fewer resources.
In fact, data was recently described as “the new gold” by one expert who likened its value today compared with other precious metals such as platinum or silver due to how quickly technology has advanced over recent years
You may be thinking, “Data ethics? Isn’t that just for academics?” Let me tell you, it’s not! Data ethics is an important part of modern life. Data can be used in critical situations, like policing and healthcare.
One of the most common examples of data being used improperly is when a person’s personal information is misused by companies or government agencies.
For example: if someone with access to your social security number steals your identity and buys something online with it (like a credit card), then there’s no way for you to know who did this — and even if there were signs pointing out the problem before now (like seeing suspicious activity on bank statements), they may have already been erased by now because banks usually won’t tell anyone about fraud cases until after they close them off from being investigated further (which could take months).
We have been putting blind faith in our algorithms for years, but it’s time to stop.
Machine learning algorithms are designed to learn from their mistakes, which means they can get better at making decisions based on what has already happened.
This is a good thing! If you think about it, we’re all just data points our machines use as inputs for the models that guide their behavior and decision-making process.
But this also means that when something goes wrong with your model or algorithm — whether it’s because it was trained on incomplete data or because there were human biases involved — it can fail catastrophically if not corrected quickly enough or properly updated.
The same goes for other humans who interact with these systems: they may not always understand why certain things happen with their own words or actions and then wonder why those same things keep happening again and again until someone explains them how they should behave in such situations (and sometimes even after being told).
Data ethics is about understanding the world that we’re creating. In a nutshell, data ethics means using data responsibly and ethically in order to improve our lives for everyone.
If you’re not sure what this means, think about it like this: If someone was going to write a book about how to be more ethical in your life, wouldn’t they include all kinds of philosophical ideas like “do unto others as you would have them do unto you”? Or maybe even more specific examples from your own experiences (e.g., “if someone asks me if they can borrow my cell phone before I’ve had breakfast at home — I’ll say no”).
This is exactly how much work goes into thinking through these issues when we’re talking about data privacy and ethics!
You might be thinking, “I’m not a technologist! How do I know that technology is getting more powerful?” Well, let’s look at the numbers.
The power of technology has been growing exponentially for decades now. For example, consider how much faster our computers are today than they were just 20 years ago:
- 1/1000th of a second to perform an operation — compared to 10 millionths of a second back in 1993 when my first computer was built.
- The number of transistors on integrated circuits has increased from 2 kilo-transistors per square inch (KTI) or 200 million KTI into single digit billions today with Moore’s Law still holding true despite its limitations being reached by early 2016 after over 50 years since its inception in 1965.
You can’t always trust your data.
You might think that using a device that collects and shares information is ethical, but it’s not enough to say that you didn’t know what your tech was doing.
You need to be aware of the consequences of using data and make sure you understand how it’s collected, processed, shared or sold by third parties. If you’re unsure about these things then don’t use them!
The term ‘data ethics’ was coined by the philosopher John Ralston Saul in his book on the topic, A Long Way From Home: A Global History of Data (2006). In this book he said that there is a need for systems that can help us understand how we use information and make decisions based on it because these systems will affect our lives in ways we cannot imagine today.
Data ethics is the application of ethical values to the collection, storage, processing, and sharing of data for business purposes. It involves applying ethical principles to protect an organisation’s stakeholders against harm resulting from poorly managed or mishandled data.
Data ethics can be used in two ways: as a framework for thinking about how you collect customer information; or as a toolkit that helps you apply your own personal values and beliefs when working with people who have similar interests as yours.
Data Ethics is the application of ethical values to the collection, storage, processing and sharing of data for business purposes. It’s about understanding the world that we’re creating and how it can be used.
This means that you need to understand your tech better than ever before — because even if you didn’t know what your tech was doing when you first set out on this journey with it (and I’m not referring specifically to Big Data), now there are more eyes looking at all those big piles of numbers than ever before.
Data professionals should be responsible for data ethics. They are the ones who are creating and manipulating the datasets.
The data scientist can use that fact to their advantage by making sure that every single element of their dataset is considered and accounted for in terms of its ethical impact.
If you’re working with an external supplier to gather your data — whether it’s an online survey or some other kind of research — they need to know how much control over their own information they have and what kind of protections should be put in place on the way into their systems.
Data owners must also make sure that anyone who uses their product has fully understood what permissions go along with those permissions and how long those responsibilities last (if anything).
The same goes with governance board members: if someone signs up for access but then leaves before actually using any products/services provided by your company, they shouldn’t expect free rein over everyone else’s accounts forever after! That could lead down very dangerous road indeed.
Data ethics is a big deal. And, like so many things in life, it’s not just about the technicalities: it’s about who you are and why you do what you do.
Everyone who works with data should understand their role and their ethical obligations.
To remain competitive in today’s digital economy, companies need to ensure that their employees are properly trained on how to manage big data ethically.
Training is important because it ensures that all your employees understand the importance of ethical use of data and what they can do if they encounter a situation where this might be compromised. It also helps them know how to identify suspicious activity or activities which might result in violations of GDPR laws, as well as other regulations such as PCI DSS (Payment Card Industry Data Security Standard).
Data ethics is a broad term that covers a lot of ground. But it can be broken down into three main areas of focus: privacy, security and fairness.
Privacy
Refers to the right of an individual or group to protect their personal information from being used without their consent by others in ways that are not consistent with their wishes or interests.
Ideally this means that people should have control over what data about them is collected and how it’s used — whether for marketing purposes or other reasons (such as law enforcement).
Security
Refers to the protection of data against unauthorized access by people who may want to harm its owner or access sensitive information contained within it without permission from someone who owns such information (i.e., hackers).
This includes things like encryption technology which helps prevent unauthorised access when stored on servers but also makes sure that no one else can see what you’re storing there either — even if they know where your computer is located! It’s like having super-strong locks on your doors so only those who have permission will be able to get inside.
Fairness
Refers to the protection of data representation by preventing bias, indifference or any form of algorithmic discrimination. Especially when machine learning algorithms are involved, it is critical that we drill down deep into any potential data biases that we might be indirectly feeding to our algorithms.
Opinion
A one-on-one discussion on planning today for a stronger tomorrow
Data ethics is the application of ethical values to the collection, storage, processing and sharing of data for business purposes. It involves applying ethical principles to protect an organisation’s stakeholders against harm resulting from poorly managed or mishandled data.
Data is a term that has been used to describe any quantity or abstract description of information. The difference between data and information is subtle, but important: Data refers to the actual numerical representation of an entity or object, whereas information is just about what something means.
For example, if you asked me how old I was yesterday (in human years), I would say “twenty-four” with some confidence — but if you asked me what color my eyes were, I’d be more likely to answer “green” because there’s no way for either person to know whether they’re wrong without looking into it further.
This makes sense: How do we know how old someone really is unless they provide us with proof? If someone tells us they’re twenty-four but doesn’t have any documentation on file showing them as such (e.g., birth certificate), then maybe all hope isn’t lost yet! In this case however…
Data is the fuel that powers the digital economy. It’s what makes things happen, it’s what makes our devices run, and it’s key to finding new ways of doing things — from self-driving cars to virtual reality apps.
Data is also becoming more valuable as time goes on, because it can be processed by machine learning algorithms that learn from past behavior patterns in order to make predictions about future events.
Data is like oil: once you have a good supply of it (like oil), you’re able to get more done with less effort than before because your machines can do more for you with fewer resources.
In fact, data was recently described as “the new gold” by one expert who likened its value today compared with other precious metals such as platinum or silver due to how quickly technology has advanced over recent years
You may be thinking, “Data ethics? Isn’t that just for academics?” Let me tell you, it’s not! Data ethics is an important part of modern life. Data can be used in critical situations, like policing and healthcare.
One of the most common examples of data being used improperly is when a person’s personal information is misused by companies or government agencies.
For example: if someone with access to your social security number steals your identity and buys something online with it (like a credit card), then there’s no way for you to know who did this — and even if there were signs pointing out the problem before now (like seeing suspicious activity on bank statements), they may have already been erased by now because banks usually won’t tell anyone about fraud cases until after they close them off from being investigated further (which could take months).
We have been putting blind faith in our algorithms for years, but it’s time to stop.
Machine learning algorithms are designed to learn from their mistakes, which means they can get better at making decisions based on what has already happened.
This is a good thing! If you think about it, we’re all just data points our machines use as inputs for the models that guide their behavior and decision-making process.
But this also means that when something goes wrong with your model or algorithm — whether it’s because it was trained on incomplete data or because there were human biases involved — it can fail catastrophically if not corrected quickly enough or properly updated.
The same goes for other humans who interact with these systems: they may not always understand why certain things happen with their own words or actions and then wonder why those same things keep happening again and again until someone explains them how they should behave in such situations (and sometimes even after being told).
Data ethics is about understanding the world that we’re creating. In a nutshell, data ethics means using data responsibly and ethically in order to improve our lives for everyone.
If you’re not sure what this means, think about it like this: If someone was going to write a book about how to be more ethical in your life, wouldn’t they include all kinds of philosophical ideas like “do unto others as you would have them do unto you”? Or maybe even more specific examples from your own experiences (e.g., “if someone asks me if they can borrow my cell phone before I’ve had breakfast at home — I’ll say no”).
This is exactly how much work goes into thinking through these issues when we’re talking about data privacy and ethics!
You might be thinking, “I’m not a technologist! How do I know that technology is getting more powerful?” Well, let’s look at the numbers.
The power of technology has been growing exponentially for decades now. For example, consider how much faster our computers are today than they were just 20 years ago:
- 1/1000th of a second to perform an operation — compared to 10 millionths of a second back in 1993 when my first computer was built.
- The number of transistors on integrated circuits has increased from 2 kilo-transistors per square inch (KTI) or 200 million KTI into single digit billions today with Moore’s Law still holding true despite its limitations being reached by early 2016 after over 50 years since its inception in 1965.
You can’t always trust your data.
You might think that using a device that collects and shares information is ethical, but it’s not enough to say that you didn’t know what your tech was doing.
You need to be aware of the consequences of using data and make sure you understand how it’s collected, processed, shared or sold by third parties. If you’re unsure about these things then don’t use them!
The term ‘data ethics’ was coined by the philosopher John Ralston Saul in his book on the topic, A Long Way From Home: A Global History of Data (2006). In this book he said that there is a need for systems that can help us understand how we use information and make decisions based on it because these systems will affect our lives in ways we cannot imagine today.
Data ethics is the application of ethical values to the collection, storage, processing, and sharing of data for business purposes. It involves applying ethical principles to protect an organisation’s stakeholders against harm resulting from poorly managed or mishandled data.
Data ethics can be used in two ways: as a framework for thinking about how you collect customer information; or as a toolkit that helps you apply your own personal values and beliefs when working with people who have similar interests as yours.
Data Ethics is the application of ethical values to the collection, storage, processing and sharing of data for business purposes. It’s about understanding the world that we’re creating and how it can be used.
This means that you need to understand your tech better than ever before — because even if you didn’t know what your tech was doing when you first set out on this journey with it (and I’m not referring specifically to Big Data), now there are more eyes looking at all those big piles of numbers than ever before.
Data professionals should be responsible for data ethics. They are the ones who are creating and manipulating the datasets.
The data scientist can use that fact to their advantage by making sure that every single element of their dataset is considered and accounted for in terms of its ethical impact.
If you’re working with an external supplier to gather your data — whether it’s an online survey or some other kind of research — they need to know how much control over their own information they have and what kind of protections should be put in place on the way into their systems.
Data owners must also make sure that anyone who uses their product has fully understood what permissions go along with those permissions and how long those responsibilities last (if anything).
The same goes with governance board members: if someone signs up for access but then leaves before actually using any products/services provided by your company, they shouldn’t expect free rein over everyone else’s accounts forever after! That could lead down very dangerous road indeed.
Data ethics is a big deal. And, like so many things in life, it’s not just about the technicalities: it’s about who you are and why you do what you do.
Everyone who works with data should understand their role and their ethical obligations.
To remain competitive in today’s digital economy, companies need to ensure that their employees are properly trained on how to manage big data ethically.
Training is important because it ensures that all your employees understand the importance of ethical use of data and what they can do if they encounter a situation where this might be compromised. It also helps them know how to identify suspicious activity or activities which might result in violations of GDPR laws, as well as other regulations such as PCI DSS (Payment Card Industry Data Security Standard).
Data ethics is a broad term that covers a lot of ground. But it can be broken down into three main areas of focus: privacy, security and fairness.
Privacy
Refers to the right of an individual or group to protect their personal information from being used without their consent by others in ways that are not consistent with their wishes or interests.
Ideally this means that people should have control over what data about them is collected and how it’s used — whether for marketing purposes or other reasons (such as law enforcement).
Security
Refers to the protection of data against unauthorized access by people who may want to harm its owner or access sensitive information contained within it without permission from someone who owns such information (i.e., hackers).
This includes things like encryption technology which helps prevent unauthorised access when stored on servers but also makes sure that no one else can see what you’re storing there either — even if they know where your computer is located! It’s like having super-strong locks on your doors so only those who have permission will be able to get inside.
Fairness
Refers to the protection of data representation by preventing bias, indifference or any form of algorithmic discrimination. Especially when machine learning algorithms are involved, it is critical that we drill down deep into any potential data biases that we might be indirectly feeding to our algorithms.