In the era of big data and machine learning and with the battle for data already underway, many sectors are interested in the services of data scientists and are looking to recruit them. What are the underlying challenges? Which profiles of data scientists should be recruited for a project? In this article, we’ll explore these questions.
Understanding Why You Need a Data Scientist: The Explosion of the Amount of Available Data and Innovations in Data Processing
The pace of data creation has been exponential for years: in the last five years, we have created as much data as the entire history of humanity before these 5 years. This data is in different shapes and forms — images, videos, the articles we write, our consumption or travel habits etc. We generate massive amounts of data and use it on a daily basis, and therefore, accessing and analyzing this data can be useful for every sector in the economy.
New technologies have appeared such as Hadoop or Spark that allow the storage and processing of huge volumes of data. These technologies have the effect of increasing the speed and capacity of data processing. Machine learning algorithms, on the other hand, makes it possible to automate the interpretation of very large databases, putting an end to our inability to process large amounts of the data we produce.
Data Science has thus emerged and with it the responsibility and the speciality of a data scientist to exploit this data. Data science offers the opportunity to stimulate business growth through the use and exploitation of data. Data science projects create opportunities for value creation and can generate significant returns on investment. Spectacular success stories such as Google, Facebook, Amazon are mainly due to the extensive use and exploitation of data.
What are the Applications of Data Science in Different Sectors Today?
Today we see more and more projects involving big data and data science emerging. These projects are linked to data enhancement or the advancements of analytical techniques: whether in sales, marketing, the fight against fraud, or cybersecurity.
- In marketing, they can be used to optimize emailing or advertising campaigns or to segment a business’s customer base according to different types of indicators.
- In finance, data scientists create evaluation and prediction models to improve return on investment.
- In communication technology, they have enabled the creation of chatbots using Machine Learning models based on Natural Language Processing (or NLP).
- In the health sector, data science enables the detection of cancers before they occur thanks to predictive models.
- For e-commerce sites, data collection and analysis has become essential: visit statistics, path statistics, behavioural statistics… The segmentation of the customer base allows us to contextualize and personalize messages in order to increase the customer conversion rate.
- In startups, the first data scientists recruited generally define the data acquisition process and have the task of automating data collection as much as possible. The data scientist “cleans” the data, sorts it before processing and analyzing it. The data scientist also helps choose the tools the company will rely on as it grows. In short, data scientists are often responsible for creating the entire data infrastructure of startups.
How Do Data Scientists Create Value?
In general, the data science approach can be applied to any business model or company, as most companies produce and collect data through their activities and the interactions between employees and customers.
Based on knowledge in mathematical sciences, statistics and computer science, the role of the data analyst will be to evaluate all the data held by companies (customer database, sales figures, transaction history, visits of Internet users, purchasing behaviour, etc.) with the purpose of extracting knowledge useful for the improvement of products and services offered, and this is in addition to better targeting customers, proposing more suitable products, creating new personalized offers from the data collected, and identifying opportunities to save money.
The ways a data scientist creates value is almost limitless, and if you can’t hire one for any reason, it is always a good idea to go with a data science agency instead. They’ll be able to help you get started and lay the groundworks necessary for you to succeed.