Every company is a treasure, never truer than when it comes to their data. As modern technology has enabled the creation and storage of ever-increasing amounts of information, data volumes have exploded. In just these last two years, more than 90% of data has been created. A treasure, right? It depends on whether they are used and managed.
The vast amount of data that is constantly being collected and stored using technology can bring transformative benefits to organizations and societies around the world, but only if we can interpret it. This is where Data Science comes in.
What is Data Science?
Data Science refers to the extraction of actionable information from raw data. This discipline combines multiple fields including statistics, scientific methods, and data analysis to extract valuable insights. Data scientists combine a wide variety of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources.
Data Science reveals trends and generates intelligence that companies can use to make better decisions, anticipate change, and of course, generate more innovative products and services. It enables machine learning (ML) models to learn from vast amounts of data that is supplied to them, rather than relying on business analysts to see what they can discover from the information they can absorb.
A Data Scientist not only performs exploratory analysis to discover information but also uses advanced ML algorithms to identify a particular event in the future. Hence, data science is primarily used for making decisions and predictions using predictive causal analytics, prescriptive analytics, and machine learning.
- Predictive causal analysis: Model used to predict the chances of a particular event in the future. This model is used, for example, by banks, where they can predict according to the customer’s history if payments will arrive on time or not, therefore, if the customer is subject to credit.
- Prescriptive analysis: The model that has the intelligence to make its own decisions and with the ability to modify it according to dynamic parameters. It not only predicts, but it also suggests a variety of prescribed actions and associated outcomes. This type of analysis is a fundamental part of autonomous cars’ development. Data collected on regular vehicles can be used to train autonomous vehicles. This information will allow the vehicle to make decisions such as when to turn, which way to go when to slow down, accelerate,etc..
>>>The future of road transportation: Driverless Trucks<<<
- Machine Learning for predictions making: If a company has historical data, machine learning can be trained to make forecasts. For example, a company that has historical data on fraudulent purchases can use it as a database that the machine learning algorithm uses to prevent fraud. This model is also known as supervised learning because there is already have basic data to train machines and these can be used for different purposes.
- Machine Learning for pattern discovery: If you do not have parameters or base data with which to make predictions, you use the discovery of the patterns hidden within the data set to make meaningful predictions. This is an unsupervised model, where the most common algorithm used for pattern discovery is Clustering.
How can Data Science be applied in different sectors? Here are some examples:
Health Sector. Great advances thanks to Data Science.
The healthcare sector especially receives great benefits from Data Science applications.
Medical image analysis:
Procedures such as tumour detection, artery stenosis, or organ delineation employ machine learning methods, support vector machines (SVM), content-based medical image indexing, and wave analysis for the classification of solid textures.
Google has developed the LYNA tool, which identifies breast cancer tumours that metastasize to nearby lymph nodes. In the Lymph Node Assistant trial, it accurately identified metastatic cancer 99% of the time using its machine learning algorithm.
https://ai.googleblog.com/2018/10/applying-deep-learning-to-metastatic.html
Drug development:
The drug discovery process is extremely complicated and involves many disciplines. The best ideas are often limited by billions of tests, a huge financial and time investment. On average, it takes twelve years to make an official presentation.
Data science applications and machine learning algorithms simplify and shorten this process. The idea behind computational drug discovery is to create computer model simulations as a biologically relevant network that simplifies the prediction of future outcomes with high precision.
Virtual assistance for patients and customer care:
Optimizing the clinical process is based on the concept that in many cases patients do not need to visit the doctor in person. A mobile application can offer a more effective solution without leaving home.
AI-powered mobile applications can provide basic healthcare, typically as chatbots. Simply describe symptoms or ask questions to receive key information about the medical condition, linking symptoms to causes within a wide web of available information. They can also remind the patient to take the medicine and, if necessary, make an appointment with a doctor.
The impact on logistics, transportation, and last-mile delivery.
The most significant advance or evolution that data science has given us in the field of transportation is the introduction of autonomous cars. It is committed to providing safer driving environments, optimizing vehicle performance, adding autonomy to the driver, and much more. Through ML and the introduction to autonomy, vehicle manufacturers can create smart cars and better logistics routes.
UPS: Optimizing packet routing
UPS uses data science to optimize package transportation. Network Planning Tools (NPT), is a platform that incorporates machine learning and artificial intelligence to solve logistics challenges, such as changing routes to avoid bad weather or bottlenecks in the service. NPT allows engineers to simulate a variety of solutions and choose the best ones. The AI also has the power to suggest routes on its own.
https://www.technologyreview.es/s/10759/asi-ayuda-una-ia-ups-en-el-reparto-de-regalos-navidenos
UBER EATS: Home delivery
The data scientists at Uber Eats have a simple goal: deliver hot food quickly. However, making that happen requires machine learning, advanced statistical modelling, and on-staff meteorologists. To optimize the entire delivery process, the team must predict how each variable is possible, from storms to Christmas rush, traffic, and cooking time.
The business value of data science depends on the needs of each organization. Data Science can help create the tools for all kinds of objectives from performing tasks in a more automated way, preventing failures, to supporting us to create strategies, products, and services that constantly add value to the consumer and adaptable to their needs throughout the weather.
This technology is already in motion, and our data can be the company’s greatest asset. So, it is important to consider it within the Digital Transformation process of our business.
