Data science (Data Analyzing) - outsourcing company Boosty Labs
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Data Science (Data Analyzing) Development Company

Analyzing and processing data means applying predictive analytics to get the most out of the information your organization has. This is not a complete product, but a collection of interdisciplinary tools and methods that combine statistics, informatics and modern technologies that help turn data into strategically important information. Boosty Labs is the largest blockchain, data science consulting and data science development company in Europe. Its world-class fintech and cloud engineering team with a solid background of practice that combines consulting, strategy, design and engineering at scale, can help with data science services.
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Trends that are making data science/data analysis a promising field

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Demand Forecasting

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

Recommendation system

Internet services have data about views by each user of their content: videos, movies, music, articles or pages of goods and services. Machine learning can analyze preferences to offer them the most relevant content.

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Dynamic pricing

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

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Search for anomalies

Data Science helps companies find errors in business process data and reporting. This helps to notice inaccuracies and anomalous data changes in time, understand their cause and change the work of the company.

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Chatbots

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

Benefits of using data science/data analysis in various industries

Energetics

A utility company can optimize the smart grid to minimize energy consumption according to real-time usage data and cost structure.

Retail

A retailer can use analytics and data manipulation on point of sale information to predict future purchases and better match product mix.

Automotive industry

Automakers are actively using data analysis and processing to collect real-time vehicle traffic information and develop autonomous systems through machine learning.
 

Various industries

Industrial plants use analysis and data processing to minimize waste and increase equipment uptime.It is data analysis and processing, as well as artificial intelligence, that have become the foundation for advances in text analysis, image recognition and natural language processing that are driving innovation in a wide variety of industries.

Most companies today are overloaded with data and are probably not fully exploiting their potential. This is where data analysis can help to transform information into meaningful strategic insights and real competitive advantage.

By using data analysis, your organization can make decisions and act with confidence because you rely on facts and scientific method rather than guesswork and intuition.

Analyzing and processing data can dramatically increase productivity in almost any area of ​​your business through the following capabilities:

  • optimization of the supply chain;
  • reduced staff turnover;
  • understanding and meeting customer needs;
  • accurate forecasting of business performance;
  • control and improvement of the appearance and characteristics of products.

The analysis and processing of data is becoming more and more automated, and this process will continue. For example, today a technician can set up an automatic grid search of all possible combinations of thousands of data parameters to find the best solution to a specific problem in real time.

In the past, statisticians had to manually design and tune predictive models, drawing on their experience but being creative at the same time. But today, as the volume of data and the complexity of business problems has increased, the task has become so mathematically complex that it requires resorting to artificial intelligence, machine learning and automation to solve it. As big data gets bigger, this trend will only intensify.

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