Data science can be applied in many areas especially with all industry systems now generating more and more data. This information can be used to provide indispensable value.
But how do you know what data to use? And how exactly do you get this data? These are questions our Data Scientists answer to existing and potential clients all the time.
Applying professional and effective data science techniques can help improve business capabilities in so many ways. These techniques can help improve decision-making by using machine learning techniques as a tool for processing information in real time.
They can also help discover new insights by analyzing patterns in data that can lead to exciting new business opportunities.
This article aims to provide a brief overview of data science, why it's so valuable to businesses, the challenges and advantages it brings, as well as the strategies for effectively monetizing data.
So, What is Data Science?
Believe it or not, data science has been referred to as “the sexiest job of the 21st century”, but what exactly is it?
Data science is made up of a bunch of different skills, and contrary to popular opinion, you don’t just need programming and statistics skills to understand how the associated algorithms work.
Data scientists also need key business skills to better understand the respective businesses and the necessary data to be processed. They also need to possess analytical skills to allow them to interpret results and be able to communicate these results effectively to others.
Data is everywhere, but is it valuable?
There are huge amounts of information being generated from data every day, however, data by itself is not valuable, as we need to choose the correct data to resolve specific problems.
Therefore, it is important to define the problem that we are trying to solve, and then go and find the correct data with this information.
As mentioned, data scientists need to understand the business context they are working with to effectively frame a problem, and then see if they have the correct data to resolve it. If they do not already have this necessary data, they then must find ways to get it, and in most cases quickly.
Data science challenges
We often hear business experts complaining that they have a lot of data but they do not get any value from it. Not only does this happen because of the amount of data they possess, but also because it is presented in a format that is unreadable and/or difficult to understand.
In most cases, large amounts of data are presented in many different formats and it can be hard to find the value you need to correctly merge information from different sources, as well as combining and transforming values.
If you ask a data scientist what the most difficult part of their job is, they won't tell you that it’s programming the algorithms. Instead, they will tell you that most of the time the data they work with is a mess, and the most difficult part is the time-consuming process of preparing and cleaning data to apply it to algorithms.
It is estimated that 80% of a data scientist's job consists of these processes.
Advantages of data science
Applying data science in your business operations can bring several advantages to an organization.
Some of these include:
- Cost and resources optimization: finding patterns in data can help to optimize your resources and reduce cost by finding better routes or ways to deliver your products.
- Time reduction: processing huge amounts of information in record time can provide new insights faster.
- New products & Innovation: keeping up with market trends by detecting what users/customers want, need and like.
- Support decisions: data can help with decision making processes. Humans and algorithms are not perfect, but, with help of algorithms, humans can improve their decision making, and if used correctly, data science can help reduce algorithmic bias and also allow for more inclusivity across industries.
- Real time value: processing information in real time can help you to not miss opportunities as well as prevent certain unwanted situations.
- Precision: with data science techniques you can improve the precision of the tasks that you develop in your business.
- Differentiate from competitors: keeping up with the market demand would position you as a leader and give your clients what they want.
How can we monetize data?
There are a number of different strategies available to extract value from data. These strategies are not new, but with emerging technologies, and the power of data science, we can better see their potential.
- Keeping data private: leveraging data for internal operations and improving internal operations can positively impact processes and employees, and can also reflect a better and faster service for clients.
- Selling data as a product: data might be valuable for different people or companies, so, you can create a product from the value generated from certain data. Plus, users might be willing to pay more for access to that data. A clear example of this is Linkedin, who charge extra to access more information with their ‘Premium Access’ feature.
- Trading data (data exchange): another strategy might be trading data with partners for mutual benefits. Fusion of two data sources comes with potential benefits for both companies who could benefit from something that by themselves they wouldn't be able to.
- Making data open and free: another strategy commonly used in social networks is making data available to many users for free, and this might generate value in another way. For example, you can monetize using ads in an app or website, and/or charge after a certain time for a minimal fee.
What to take away
With the correct use of data science, organizations have the power to add great value to their business operations. The power of data science is now invaluable for companies scattered across all industries. Data science can help with decision-making, hiring, improving workflows, and providing valuable statistics and insights into how a business is performing.
As AI continues to develop, it is now more important than ever that business owners are incorporating effective data science procedures and techniques into their day-to-day operations to not only ensure they keep up with their competition but to provide long-term stability for their businesses.
