Are Ethics Violations Caused by Data Analytics? (Content Princess)

Business ethics is one of those terms I hear most often in jest, another in the long list of oxymorons. Of course, you can do business and be ethical, although it is likely more difficult to do it this way.

The law makes companies operate by some ethical codes, but other issues are either not covered at all or barely touched upon, leaving companies to police themselves.

This means that policies are based on the personal moral or ethical code of the company leaders. I know what you’re thinking, and you’re right. This could get disastrous, especially if the leaders of a company have questionable personal morals or ethics.

Why the Gray Areas?

Since the coming of the digital age, politicians and regulatory bodies have been scrambling, trying to come up with clear-cut policies on things like data usage, privacy, and other related aspects. Success has been slim in this area.

Technology changes so rapidly, regulatory agencies have a tough time keeping up, and it is a hassle all around for businesses and regulators. The rising popularity and usage of data analytics raises even more ethical questions.

What is Data Analytics?

If you are not aware of what data analytics is, here is a simple definition, broken down into steps.

  1. Raw data is collected.
  2. The data is analysed.
  3. Companies look at the analysis results.
  4. The results are used to help companies make important decisions, grow, expand, change, etc.

That is the gist of it. The amount of analysis done and the conclusions are usually based on the needs of the company.

Why is Data Analytics?

Data analytics is employed by tonnes of companies these days. It sounds boring, but there is a wealth of information to be had from the results. Here is why data analytics is done by so many businesses today.

  • Results can show patterns, such as in spending, buying, highest-selling products, lowest-selling products, etc.
  • Results can help a company determine what time is the right time to expand, downsize, introduce a new product, discontinue an old product, and other important decisions that could affect the company’s future.
  • Data analytics could potentially help improve customer service by analysing customer data and finding out what they want.

The Legal

The large amounts of data that companies analyse can easily be put to ill use if not checked. This is why definitive regulations are necessary. You may not be able to imagine improper usage of data analytics, but the potential is great.

Authorities do not have much in the way of regulation as far as analytics is concerned, so it is largely up to the company itself.

What to Do

If you are in the business owner’s position or other decision-making position, here is some advice on what you can do regarding your analytics while avoiding ethics violations.

  • Get details on what is explicitly illegal.
  • Use personal morals to help create policies.
  • Ensure policies are defendable at any time.
  • Implement those policies.

Aftermath of Improper Data Analytics Usage

If you end up improperly using data analytics, there are a number of things that could happen.

  • You could end up being fined because of violations.
  • You could lose customers.
  • You could lose employees.
  • You could lose your business.

Since there are not many clearly defined rules in this area, be careful, and if it feels wrong, don’t do it. That is the best advice I can give you on the subject.



Recruitment of the Right Data Scientist for Your Company! (Content Princess)

What is a Data Scientist?

Data Science is a field that specialises in data interpretation and the formulation of future predictions by using numbers. It is a method that is beneficial to a company, which seeks to accurately predict finance and profits. This technique is commonly used for minimising risk assessment for decision making.

Your data doesn’t require any form of structure, as there are a lot of tools and methods to gather the information you need.

Helpful Hint: If an Excel sheet puts you to sleep, then you may need a Data Scientist!

What methods does a Data Scientist use, to accurately interpret my data?

  • Statistics
  • Model predictions
  • Market Optimisation
  • Data Preparation
  • Machine learning
  • Public policy
  • Analytical Marketing
  • Detection of Fraud
  • Risk Management

Data Science is heavily influenced by mathematics, IT, Information Science, Chemo metrics, Probability models, data engineering, learning pattern recognition and computer programming – to name a few.

What are the benefits of using Data Science?

Data Science is a huge benefit to a majority of businesses, mainly for its use of providing answers to a formed corporate decision. They can sift through the external and internal data, to analyse the strength and future forecast of profits or loss.

Business sectors that most benefit from using a Data Scientist:

  • Firms that use brands
  • Ecommerce
  • Retailers
  • On-demand services
  • Content

However, an increasing number of markets that have branched out online for business have found many great advantages from using Data Science and have enjoyed success.

How to employ the right Data Scientist?

Data Science has become increasingly popularwith a huge market over recent years, so it can be a very daunting task when it comes to hiring the correct Data Scientist. Here are some brief tips on obtaining the full benefits and complete value, with a reliable service.

Determine what data needs interpretation, and for what purpose. To forecast future profits, knowing what you want is important, and DataScience is no exception!

  1. Google ‘Data Scientist Services’ and read through the multiple services available.
  2. Check forums, so you can check customer reviews on services – if there’s a highly rated popular service then research them further.
  3. Always make sure you compare fees to get the best deal!


It is important to note whether the data will be processed by a machine or by humans, as it won’t work for both – the processes are different.

Machine Data Science:

  • A computer is used as the decision maker, through the recommendation of products, targeting ads, etc.
  • Complex digital models can use insights from huge amounts of data, which can display stock trades and adverts.
  • Statistical, mathematical and computer fluency will provide future predictions.

Human Data Science:

  • The calculations will be done by a human, who will understand retention and growth.
  • Data must be in a readable format for human understanding.
  • Although a computer can handle much larger data in quicker time, the benefit of having a person as a decision maker is that it will be more thought out and not automated (ideal for decisions requiring higher attention to detail).

To sum it up

Data Science has grown enormously over the last decade, especially for a business that wants to obtain an edge over its competitors.

Many companies store huge amounts of data, and it is usually not used properly or misunderstood; hiring a Data Scientist can help you to use your information more shrewdly for future decisions and investments.