Big data / Data Analytics

Since ages, businesses are using basic analytics, essentially numbers in a spreadsheet that are manually examined, to uncover insights and trends.

Big data analytics examines large amounts of data, coming from different streams into the business, to uncover hidden patterns, correlations and other insights.

The main benefits are speed and efficiency: combined with the ability to analyse new and different sources of data, businesses are able to analyse information immediately, make better decisions based on what they’ve learned in less time and even make predictions for the future. You can create new products and services better corresponding to the customers needs.

Business impact

Every organisation should use data analytics to identify new opportunities, leading to smarter business moves, more efficient operations, higher profits and happier customers.

Data mining technology helps to examine large amounts of data and discover patterns in the data – this information can be used for further analysis to help answer complex business questions. You can pinpoint in your data what’s relevant, use that information to assess likely outcomes, and accelerate decision making.

Predictive analytics uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It’s all about providing a best assessment on what will happen in the future. Production industries can detect future repair needs of machines, enabling intervention before production chain stops working.

With text mining technology, you can analyse text data from the web, comment fields, books and other text-based sources to uncover insights you hadn’t noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, social media, surveys, competitive intelligence and more – to help you analyse large amounts of information and discover new topics and relationships.

By analysing large amounts of information from different sources, health care providers can provide lifesaving diagnoses and adapt treatment options almost immediately. Data analytics are used to detect the mood of psychiatric patients and prevent human catastrophes.

Armed with endless amounts of data from customer loyalty programs, buying habits and other sources, online shops and retailers get in-depth understanding of their customers and they can also predict what product you will want to buy at what time, thus recommend new products – and use it to boost profitability.

Other predictive analytics include fraud detection, risk, operations and marketing.

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