The biggest challenge of Data Science
What is Data Science?
Data Science is a process of using advanced analytics methods to examine large data sets and extract information from them. Information is beneficial to business operations and strategies. Additionally, these techniques can generate insights and signals automatically and even apply them directly to other systems. This way, we don’t need human intervention and constant machine learning, and business optimisation can happen automatically and constantly.
Data scientists use machine learning, predictive modeling, data mining, data preparation and visualization techniques to turn raw data into insightful business knowledge.
How can Data Science be used?
Many data science applications, including pattern recognition, predictive modeling, conversational AI or machine learning in its broader spectrum of uses, are used in various industries, each choosing the applications that suit its specific needs.
In the financial services sector, the most useful application of data science would be mining and analyzing data to detect fraudulent transactions and manage financial risks by evaluating potential customer portfolios.
In entertainment and streaming services, data science allows companies to track what kind of content is most popular among subscribers, allowing them to determine what to produce. Furthermore, data-driven algorithms create personalized recommendations based on a user's viewing history.
In the case of transportation: delivery companies, freight carriers and logistics services providers use data science to optimize delivery routes and schedules and the best modes of transport for shipments.
Retailers can analyze customer behavior and buying patterns to drive personalized product recommendations, targeted advertising, marketing and promotions. Customers who work during the day are more likely to want their purchase delivered in the afternoon when they’re back home from work, knowing that the company may tailor its delivery system to accommodate the customer’s schedule.
What is the biggest challenge of Data Science?
Companies need experts to create a base platform to experiment on data, especially in strictly regulated environments such as the financial and medical sectors. These experts must secure the data, prepare it, validate it, and ensure that data access is strictly controlled.
Without automation of provisioning of AI training environments, testing an idea requires even 6 months of work and a huge budget to meet the compliance regulations. For this reason, good ideas often don't manage to even get to the testing phase.
Now, how about you - do you also have to wait a long time to access company data? If so, how long?
