Apart from big data, businesses are making optimum use of open data - data that is inexpensive, easily accessible, and a profitable resource goldmine. Businesses are now starting to feel the benefits of open data in synchronization with their private data and are collaborating on a whole new level to acquire state of the art business models, improved revenue streams, innovative products and services, and a competitive advantage over their business rivals.
It is crucial that data scientists and analysts take into account the existing biases and formulate remedial solutions for these. As hidden biases in big data are an impediment to accurate decision-making and can affect outcomes, it is paramount that business leaders and lead management members remain alert.
If there are pros to big data, there are cons too. The negative impact of big data is subtly hidden in the trail of digital traces we unknowingly leave. Anything that is unmonitored leaves an opportunity for exploitation. The same is the case with big data. While big data is not bad in itself, it can have undesirable effects if the people involved in its use have malicious intentions. It is time that individuals and organizations become aware of the value personal data and information holds and adopt a more transparent approach.
The hype around artificial emotional intelligence is real. By integrating emotional intelligence with the existing artificial intelligence, AI is taking a crucial turn on its journey to becoming a transformational technology. Companies that will able to effectively incorporate contextual understanding and empathy into their technologies will become the front-runners in this race to technological excellence.
While big data is empowering business decisions with information abundance, cyber espionage is on the rise too! Big data has helped organizations and businesses in a plethora of ways earlier. However, what big data can do for supporting cyber security and curbing cyber espionage is promising and exciting.
Hadoop is a technology that can help your organization in storing ample amount of data in your database, in any given form. Hadoop is technically a database management system that focuses on helping its users easily retrieve data and maintain it.
Solving business problems with blockchain ensures efficient functioning of an enterprise. When an organization suffers a loss or a specific problem that manages to disrupt its rhythmic flow , authorities frantically search for options that can help them solve the problem. Blockchain is a technology that focuses on increasing transparency and introducing decentralization that will allow the technology to enable everyone on the network to view information stored on ledgers. Blockchain has many applications that contribute to its popularity amongst people. Here are four common business problems that can be solved using blockchain.
The focus of AI implementation at present must be to minimize human involvement in the routine and non-creative tasks, so that human effort can be directed towards innovation and planning, where AI can be used for guidance. Due to its deep learning and independent decision-making capabilities, applications of AI in different business areas are seeing a steady rise in ubiquity in some industries. The concept of artificial intelligence or machines that aim to emulate human thinking is undergoing vigorous research. Here are a few application areas that you can consider for AI implementation.
IoT is rapidly emerging as the next giant technological leap towards global integration and interconnectivity. Combining the expansive geographic reach of Internet with the pervasiveness of everyday objects makes the Internet of Things a truly global network, where everything can communicate with everything else. The applications of this technology, or rather a phenomenon, though not fully realized, are already emerging everywhere around us.
The insurance industry collects and generates a large volume of data on a daily basis, including a customer’s health records, sensor data from vehicles, confidential legal papers, to name a few. The data, if analyzed thoroughly, gives actionable insights that the insurance industry can use to improve its services. Deep learning comes with neural networks that are capable of analyzing swarms of data and learning from it. Deep learning in insurance not only enhances customer experience but also helps the industry detect fraudulent activities.