Clinical research is a slow and expensive process, with trials failing for a variety of reasons. Advanced analytics, artificial intelligence (AI) and the Internet of Medical Things (IoMT) unlocks the potential of improving speed and efficiency at every stage of clinical research by delivering more intelligent, automated solutions.
Big Data Analytics
The word "big data" applies to high-volume, fast-pace and large range digital information stores. Big data research is the technology method which exposes trends, patterns, comparisons or other valuable observations in these large data stores.
Data Analytics is not a new field, since it has been a part of the industry as business intelligence and data mining technology for decades. The technology has significantly improved over the years, enabling it to manage much greater data sizes, to run queries quicker and to execute increasingly advanced algorithms.
Big data analysis allows analysts, researchers and business users faster and better decision-making using data that was previously unusable or inaccessible. Companies and Businesses can use advanced big data analytics techniques such as text analytics, machine learning, data mining, statistics and predictive analytics to gain new visions and insights from the data sources which were previously untapped.
Benefits of data analytics
Application can be built to aggregate semi- and unstructured or structured data from touch points your customers have with the company to get a 360-degree view of customer’s behaviour and drive or motivations for better and improved tailored marketing. These data sources can be from social media, IoT devices or sensors, mobile devices, sentiments data from sentiment analysis and calling log data.
Big Data Analytics to Detect and mitigate fraud
For monitoring the transactions in real time, application can proactively recognize the abnormal patterns and behaviours identifying and indicating the fraudulent activity. Companies can predict and mitigate frauds using the big data analytics along with the predictive/prescriptive analytics and with the comparison of historical and transactional data.
Big Data Analytics to Drive supply chain efficiencies
Big data analytics can be used to determine how products are shipped and reaching their destination. In identifying the inefficiencies and how cost and time can be saved or minimized. IoT device, Sensors, data logs and transactional data can help tracking critical information from the warehouse to the destinations.
Application areas of Big Data Analytics Tools & Solutions:
Financial institutions gather and access analytical insight from large volumes of unstructured data in order to make sound financial decisions. Big data analytics allows them to access the information they need when they need it, by eliminating overlapping, redundant tools and systems.
For manufacturers, solving problems is nothing new. They wrestle with difficult problems on a daily basis - from complex supply chains, to motion applications, to labor constraints and equipment breakdowns. That's why big data analytics is essential in the manufacturing industry, as it has allowed competitive organizations to discover new cost saving opportunities and revenue opportunities.
Big data is a given in the health care industry. Patient records, health plans, insurance information and other types of information can be difficult to manage – but are full of key insights once analytics are applied. That’s why big data analytics technology is so important to heath care. By analyzing large amounts of information – both structured and unstructured – quickly, health care providers can provide lifesaving diagnoses or treatment options almost immediately.
Certain government agencies face a big challenge: tighten the budget without compromising quality or productivity. This is particularly troublesome with law enforcement agencies, which are struggling to keep crime rates down with relatively scarce resources. And that’s why many agencies use big data analytics; the technology streamlines operations while giving the agency a more holistic view of criminal activity.
Customer service has evolved in the past several years, as savvier shoppers expect retailers to understand exactly what they need, when they need it. Big data analytics technology helps retailers meet those demands. Armed with endless amounts of data from customer loyalty programs, buying habits and other sources, retailers not only have an in-depth understanding of their customers, they can also predict trends, recommend new products – and boost profitability.