The digital transformation of businesses is based on the collection and utilization of data generated in an efficient, useful and profitable way. The framework of transformation, Data Analytics and Artificial Intelligence Transformation Framework, combined with the use of advanced analysis methodologies in cloud computing technologies, are key tools of success.<\/p>\n\n\n\n
The Data Analytics and Artificial Intelligence Transformation Framework consists of four main milestones.<\/p>\n\n\n\n
\u2022 in the initial stage we have the unknown (unaware) where the organization \/ business begins to understand the purpose of using analytics.<\/p>\n\n\n\n
\u2022 then goes to the reactive stage that creates ad-hoc analyzes without a structured operational process model,<\/p>\n\n\n\n
\u2022 in the next phase it passes to the stage of prevention (pro-active state) where a functional analytics framework has now been created,<\/p>\n\n\n\n
\u2022 and in the fourth and final stage all the pillars of the frame work at an optimized state.<\/p>\n\n\n\n
During the digital transformation of the company, answers to specific business questions are given. Specifically,
\u2022 cover the business purpose of using data analytics
\u2022 the interoperability of data collection sources
\u2022 the technological infrastructure and the required number of analysis algorithms,
\u2022 the organizational structure,
\u2022 the framework of governance,
\u2022 the processes,
\u2022 the available scientific staff,
\u2022 as well as the level of maturity of the data analytics driven culture of the organization \/ business.<\/p>\n\n\n\n
In terms of analytics methodologies, significant strategic benefits are highlighted by the use of cloud computing technologies in a wide range of data analytics applications in the context of cloud analytics adoption. Indicative benefits include:
\u2022 the scalability of any type and size of data,
\u2022 Reliability of cloud computing services,
\u2022 the automation of analyzes and progressive models,
\u2022 modularity based on a micro service framework
\u2022 and the possibility of complete configuration of the implemented data analytics architecture.<\/p>\n\n\n\n
Areas of application are
\u2022 the extraction of important conclusions (key-insights) from multidimensional analyzes of large volume data parameters,
\u2022 rendering multiple experiments using intelligent applications of automated selection of high precision prediction algorithms and quality control,
\u2022 reusable open source for analysis and forecasting models using special DevOps tools.<\/p>\n\n\n\n
Key technical features need to cover interoperability, processing, storage, consumption cost monitoring, protection and security of different levels of operating processes, data flow, covering real-time streaming, batch and storage.<\/p>\n\n\n\n
The combination of all the above achieves a significant reduction of the required development time and application of advanced analyzes and forecasting models, the continuous monitoring of their reliability and their rapid replacement whenever necessary.<\/p>\n","protected":false},"excerpt":{"rendered":"
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