Data science is used nowadays in almost all companies, which increases the demand for data science specialist responsible for separating and extracting data from the information store, which makes the company make a better business decision.
There is no doubt that current companies are being squeezed with a lot of information, as this is an important part of every business, because it enables business leaders to adjust business options and rely on facts to upgrade their efficiency and benefits.
This era is considered the era of artificial intelligence and big data, where there is a big data explosion that led to the introduction of new technologies and smarter products, as about 2.5 exabytes of data are made every day, and the demand for data has increased dramatically in the past decade, where Many organizations have installed their work on data.
Creating a tech enterprise, assembling a decent product, and obtaining traction is made simpler thanks to better connectivity, lower storage costs, cloud computing, and access to distribution platforms to reach your target audience, as this helps to significantly reduce the time it takes for a product to reach 100 million monthly active users.
The combination of more products created with purchased online devices combined with increased time spent online has greatly increased the volume of user interaction data.
There was a very great enthusiasm for mining this information and infering key insights to help build great products, as the organization’s ability to compete is currently estimated by how effective analytics is applied to massive and disorganized data sets, across various sources to drive product innovation, and along these lines, There is a great need for data science professionals, and a data science team can make or break a product.
Data science supports organizations to provide relevant products, where the company can find where to sell its products at the best price and at what time the sale will be more, this enables the company to provide the right product at the right time. However, data science will also boost organizations’ work in creating new products to meet the basic requirements of their customers.
Data science helps reduce risks and business fraud. Data scientists are appropriate to understand the information that is evaluated in general. As for predictive fraud models, data science builds an analytical framework, path, and approach to big data, and uses it to issue alerts that ensure timely responses if some are identified. Unusual information.
Organizations use data to analyze their marketing strategies and create better ads. Generally, organizations spend a large amount on marketing their products, but by examining and analyzing customer feedback, organizations can provide better promotional offers, as organizations do this by analyzing customer behavior with caution online, similarly. , Monitoring customer patterns encourages the organization to show signs of improvement and better market insights, so organizations need data science professionals to help them make the right decisions about marketing campaigns and ads.
In addition, data science assists in hiring skilled experts in the company, where employees need to follow some CVs throughout the day, but big data has made this activity easier, as helping a lot of data specific to capabilities facilitates the work of data science specialists across all Platforms, for example: social media, company databases and various job portals.
Data science allows the recruitment team to accelerate the recruitment process and make accurate choices gradually, by withdrawing the vast amount of available data, processing the internal CV, and conducting data-driven proficiency tests.
Customer data is vital to improving their lives, for example: health care companies use the information available to them to assist their customers in their regular daily lives, and data science professionals in these types of businesses have reason to examine individual information and health history, and create products that address the problems they face Customer.
Creating a roadmap and strategy that is not quantitative, and then requires data-based approaches, for example: Through the use of information a roadmap can be created to increase the use (daily active) by focusing on SMS notification.
The results of forecasts generally depend on the data, for example: Understanding the necessity of presenting a story to the customer requires understanding various variables, including the possibility of the customer clicking or reading this story, and companies usually create models that are constantly being repeated to measure this specific result .
Among the examples mentioned previously for companies that focus on data, there is no doubt that every company uses the information in an unexpected way, and the use of the information changes according to the basic requirements of the company, and in this way the motivation behind the specialists in data science depends on the interests of the company.