Despite the rising popularity of data-driven technologies, studies show that nowadays less than 10% of data is used effectively by organizations. Over 40% of companies are struggling to find talent to implement Big Data solutions. However, by 2020 company’s success will largely depend on their ability to create digitally enhanced customer experience solutions. Cognitive applications are revolutionizing the faculty of enterprises to grow and compete. Solutions like chatbots for customer advice and assistance in retail, knowledge discovery tools for medical diagnostics and treatment, predictive analytics solutions for industrial equipment or recommendation systems for fraud detection and prevention in financial services are becoming increasingly more productive. Nevertheless, cognitive applications have challenges that semantic technologies are helping to overcome. Working with semantic technologies is a qualitatively driven way to enhance smart applications.
The limits of cognitive applications
Cognitive applications are being applied to a wide variety of uses and across various industries. Based on statistical and rule-based methods, they are excellent to process a large volume of information. But many companies are battling with the imprecise results this technology delivers. Complex algorithms to simulate how the human brain works lead data scientists to a bottleneck for taking cognitive computing to the next level.
How semantic technologies come into play
Semantic technologies enable companies to uncover untapped business potential and to point the way to efficiency gains. Semantic technologies have knowledge graphs at the core of their solution approach. Knowledge graphs organize information using interrelated concepts like a human brain would do. In other words, resemble the human brain’s architecture allowing domain experts and business users to perform data modeling.
How does it work
Cognitive systems can be built on statistical models or knowledge bases – or on a hybrid of these. Semantic Web Company recommends the last option. The use of knowledge graphs together with statistical models provide the end user with the option to independently modify the functioning of smart applications. Therefore, it comes more natural and user-friendly for human users and at the same time allows dynamic data management with precise results that naturally grow and learn over the time.
If you want to find out more download a free IDC White Paper about how to build cognitive computing applications with semantic technologies at www.poolparty.ai