How businesses can leverage cognitive computing along with data science | 7wData

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It is efficient for organizations to connect business Knowledge with data-guided solutions. data science enables businesses to derive data-driven decisions. Data, and more importantly analytics, are changing the way we see our machines, our processes and our operations. The fusion of data science with other technologies would enhance the decision-making approach, making it more eloquent and accurate. Let’s study in brief, how this fusion helps different industry verticals.

Applying analytics on datasets helps businesses identify patterns in the data, model behaviors, predict failures, and forecast demand based on a variety of variables that exist in the manufacturing setting.

Cognitive computing extends this analytics approach to expanses that were unreachable by more conventional tools like business intelligence and statistics.

Cognitive computing offers the benefits of traditional analytics techniques along with reasoning and predictive analysis. Key reasons to adopt cognitive computing for any business: To deliver consumers’ demand, production was managed on spreadsheets for decades. But with the increase in digitized data, manufacturing companies needed a more mature approach towards digitization.

As the usage of sensors and measurement systems streams more volume of data, the need to utilize advanced digitized technologies for better decision-making became prominent. This gave a rise to cognitive systems that could access and process data for insight generation. Data science along with cognitive computing enables a supervisor to assess process or machine performance and receive immediate answers, preventing unplanned downtime.

Using deep learning and discovery enables companies to uncover critical patterns that improve predictive maintenance. Applying data science on the data collected from the machines, the supervisor can forecast machine failure way before it occurs and take respective preventive measures. It also allows machine technicians to access historical data in detail including performance, quality and repairs, manuals and bulletins in context, and more. Technicians can become smarter and faster at their jobs with each repair. The purpose of data science in this case is to enable data-driven business decision-making. However, this does not necessarily mean that the end solutions must compute decisions.

The purpose of the derived output is to support decision-making. Natural language processing (NLP) refers to the ability of a computer to understand human speech as it is spoken or written. NLP helps organizations to translate data into Natural language, not standard computer-generated text that is overly technical and difficult to read, but natural human language that can be read by a literate person. Organizations dealing with lots of customer data face the following data-challenges: NLP helps organizations with labeling and understanding data in a natural and simple form. It can be used to produce a readable summary from a large chunk of text.

Through the tremendous depth of data presented by NLP, businesses will be able to learn about customer habits and tendencies across their entire consumer base. It can help companies measure what their customers are searching online to improve their business model. This data can be applied across numerous facets of the business, from marketing campaigns to sales and promotions and beyond.

Leveraging the patient’s historical data, NLP can support clinical decision-making by integrating and synthesizing symptoms, physical findings, and both positive and negative elements within the dataset. From a precise collective of relevant documents, meaningful and actionable summaries, and a dashboard of pertinent findings, NLP has the capacity to speed up discovery and support decisions. NLP with applied data science enables healthcare companies with automatic summarization to produce a precise summary of a patient’s history.