According to Anaconda,  a company that develops data products, on average, almost half of the time (45%) is spent preparing data, that is, loading and cleaning it. Another third is spent on data visualization and model selection. Only 12% and 11% of the work time is left for training and deployment, respectively. 

So, where is data science applied?

Dynamic pricing 

Online commerce and booking systems contain data on sales of various goods and services to different categories of buyers. Data Science helps find the best prices for products and services that will help you increase your revenue. 

Example: dynamic hotel pricing model. 

Demand forecasting 

Companies have large amounts of data on the sales of their goods and services over the past years. Analyzing this data using Machine Learning will help you find patterns, predict future demand, and rebuild business processes for the required number of goods and services. 

Example: This model helps natural gas producers predict supply. 

Recommendations

Internet services have data on the views of each user of their content: videos, films, music, articles or pages of goods and services. Machine learning can analyze preferences in order to suggest the most appropriate content for them. 

Example:  RealState Recommender model offers top five deals to real estate website visitors based on their search results. It uses algorithms for clustering queries - that is, it combines data into homogeneous groups. 

Chat bots 

Chatbots and machine learning to help answer customer questions faster and more accurately. This helps to solve most of their problems and reduce the load on the call center. 

Example: The LegalTech machine learning model helps law firms estimate the duration of cases and the cost of services and receive confirmation of the stages of work from the client via a Telegram bot.  

Support 

Outstanding customer service is the key to maintaining productive, long-term customer relationships. As part of customer service, support is an important but common concept in banking. In fact, all banks are service businesses, so most of their activities include service elements, which are detailed and timely responses to customer questions and complaints, as well as interaction with them. 

Data science makes this process more automated, accurate, productive, and less time-consuming for employees. 

Statistics 

According to a study by Accenture, 92% of high-performing organizations said their investment in deep analytics paid off, while among low-performing companies, only 24% had paid off. 

How can Data scientists help businesses? 

Data Scientists create forecasting and dynamic analysis models, implement automation and learning algorithms. For example, data experts work with the data analysis libraries Python, MySQL and have the skills to visualize the collected data.  Businesses can challenge big data analytics in different ways. 

Stages of working with advanced analytics 

Surprisingly, most of the work of a data scientist is not building models, but preparing data: collecting, organizing, cleaning, and transforming it. The first step in working with big data is to clearly define the end goal. It is not formulated abstractly and must be measurable. For example, an increase in profits by 40% or more, an increase in conversion by 35%, a decrease in equipment downtime by 30%. 

Building a data culture 

A data scientist's job requires a lot of data. A ton of data! High-quality data processing directly depends on the culture of work, including collection and storage. Every specialist in the company should be aware that any incoming and outgoing information has value. Unfortunately, this is not obvious to many, so it is important to train every  employee how to carefully collect and store materials.  

Data collection takes time

 The more services and clients there are, the longer it will take to form the base. Again, there should be a lot of data! The more there is, the better the models learn. Accordingly, Data Science will bring more accurate forecasts and fewer errors. 

Building models 

The models, the forecast of which coincided as closely as possible with the real result, are the most suitable. Do not be surprised if there are several of them. Here, as in mathematics, different solutions can lead to the correct result. 

Checking the results 

We are approaching the final stage - we are checking the results on combat data. We estimate the difference between forecast and reality. 

Summary 

This list of data science cases will expand every day with a dynamic research field and the ability to apply machine learning models to real data for more accurate results. To gain a competitive advantage, companies should recognize the critical importance of data science, integrate it into the decision-making model, and develop strategies based on insights from their customers. Start with small steps to incorporate Big Data analytics into your working models and stay ahead of the competition. Our professional team of data scientists is always ready to help with consultation on how we can help your business.