We explore data analytics outsourcing – outline the service, look at the advantages that it can bring to businesses, as well as explore the aspects that usually raise concerns.
Our BI implementation team dwells on the evolvement of operational BI in 2019, considers the advantages that it brings in real-life projects, as well as the hurdles that can spoil its implementation.
Alex Bekker, Head of Data Analytics Department at ScienceSoft, talks about true and superficial best practices in business intelligence and recommends the information sources that describe the practices helpful for real-life projects.
We share information useful for companies contemplating BI implementation: required investments and expected payback period, main advantages and typical problems, contractual and collaboration tips.
As a manufacturer, you’re interested to see what big data can do for you? Then check out these 12 real-life use cases for big data in manufacturing and see a nice and easy guide on how to start your big data action.
Learn about different types of data analytics and find out which one suits your business needs best: descriptive, diagnostic, predictive or prescriptive.
Explore five different types of artificial intelligence (AI) – analytic, interactive, text, visual and functional – and get inspired by real-life business examples of AI in action.
Data science with its deep learning algorithms allows for data-driven assessment of supplier risks. Is this approach good enough to undermine the position of a traditional one? Let’s explore how it works and what its advantages and limitations are.
We conducted secondary research, which serves as a comprehensive overview of how companies use big data. Here, you’ll find the facts arranged by organization size, industry and technology. You’ll also discover real-life examples and the value that big data can bring.
Dissatisfied with your demand forecasts? Instead of giving up on them completely, try reconsidering the methods you use. Here, we describe the approaches that will definitely work: traditional and contemporary data science.