Post by ummefatihaayat12 on Feb 28, 2024 0:41:09 GMT -5
Gartner predicted that by 2014, 30% of analytics applications would use proactive , predictive , and forecasting capabilities . Promises fulfilled that urge, in 2015, to take another step and innovate. When looking for innovation in predictive analytics , trying to extract even more value from your application, you should keep in mind that there are two different patterns that can be applied to this process: Disruptive innovation: understood as a predictive search linked to a very different value proposition, capable of discovering opportunities in the form of new markets. Sustainable innovation: which would be represented by dashboards or visualization tools that contribute to improving the performance of existing products and services. In the execution of either of both patterns within the framework of a predictive analytics project, it is necessary to follow four stages: learning, exploration, commitment and execution; which consist, broadly speaking, of the following.
Learning – In this phase, the main focus is the development of awareness and knowledge. 2. Exploration : stage where the focus is on the definition and design of the organization's work plan for the development of big data. The result of this process would be the creation of a roadmap. 3. Commitment : At this stage, organizations India Part Time Job Seekers Phone Number List begin to see the business value of big data, while often choosing to carry out an assessment of their technologies and skills. 4. Execution : Big data and analytics capabilities are applied more widely within the organization through comprehensive implementation. Sergey Nivens Predictive analytics, innovation and decisions Depending on the use case and the predictive analytics solution chosen, it will be necessary to make choices and decisions along several dimensions: Type of analysis required: in real time or not. Data processing methodology: which may include one or more, for example, text analysis, pattern identification, social media analysis, etc.
Frequency of data use: continuous, in real time or on request, among others. Data types: metadata, historical or transactional data, and master data. Content format: structured, unstructured, semi-structured or several of them. Sources of origin: internal databases, biometric data, social media, generated by users, generated by teams or transactional data, to name some of the most relevant. Data recipients: individual users, business processes, data repositories, or enterprise applications. Based on the choices made, the digital technologies chosen and the predictive analysis model implemented in the organization, a business is in a position to innovate, based on the newly acquired knowledge; doing it with different objectives such as: Improve the marketing of your products or services. Optimize supply chain management . Design a diversification or expansion strategy to take advantage of the opportunity to penetrate new markets or reach different customer segments. Related posts: Strategic objectives that can be achieved with predictive analytics Reasons predictive analysis: barriers and motivations The best consultants specialized in predictive analytics.