Content Analytics, Discovery, and Cognitive Software
Content Analytics comprises of range of search and reporting technologies which can provide identical levels of business intelligence and strategic value for unstructured data. Content analytics for unstructured information includes social media monitoring, reputation monitoring, and sentiment analysis. Content analytics software uses natural language queries, trends analysis, predictive analytics and contextual discovery to reveal trends and patterns for the company's unstructured data.
Discovery tools are the search tools that analyse contents for its likely relevancy to a process, by linking names, time periods or terms used. Discovery tools can also extend to legal hold and partitioning of content for further scrutiny. Various types of discovery tools include search engines, auto-categorization, and information visualization tools.
Cognitive computing software makes context computable by identifying and extracting context features such as hour, location, task, history or profile in structured way for an individual or an application engaged in a specific process at definite time and place. Cognitive computing technologies and platforms includes expert assistance software.
Content Analytics, Discovery and Cognitive Systems, collectively covers the market and technologies that access, analyse, organize, and provides advisory services related to a range of unstructured data. Cognitive systems leverages large amounts of structured and unstructured data and content analytics tools, along with several infrastructure technologies to answer questions, provide recommendations and directions.
F.A.Q. about Content Analytics, Discovery, and Cognitive Software
What are Content Analytics?
Content analytics is one way that brands can measure the success of their digital content; it involves analyzing data from both inbound and outbound collateral to figure out what’s working and what’s not. After all, no one wants to spend hundreds or even thousands of dollars on content that isn’t resonating.
It should come as no surprise, then, that 75 percent of enterprises think content analytics can provide real business insight: What are your customers most interested in reading about? What format generates the most engagement? You can use answers to these questions to help you decide what content you should be producing, how to present it (quiz, paid ad, slideshow, blog post, etc.), and where/when to post it for the best results. If utilized correctly, content analytics can offer brands a way to build customer loyalty and boost revenue.
Advanced content analytics strategies should help you to look beyond the numbers and to focus on taking action. Content analytics is not a mirror reflecting how your company’s content is performing; rather, it is but one tool that can help steer you towards actionable solutions and more effective content choices.
Metrics provided by content analytics, and the actions you take based off that data, can help you optimize conversion rates. In addition to tweaking your content strategy, you can also build a conversion optimization strategy by testing calls-to-action (CTAs), headlines, subscription forms, etc.
What are the features of a cognitive computing solution?
With the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.
The purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems:
Adaptive.This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.
Interactive.Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.
Iterative and stateful.The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.
Contextual.They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).