The market for connected devices , such as sports clothing, smart watches or glasses or remote sensing devices that track the health of equipment or people, will skyrocket sales in the coming years . The Internet of Things is beginning to be a need that consumers have or believe they have, and that drives them to know data about their reality and the everyday elements that make it up. In 2020 , Gartner points out that the number of items based on the Internet of Things will reach twenty-six billion units , an astronomical and surprising figure, especially if one takes into account that PCs, tablets and smartphones are excluded from the calculation . With so many sensors collecting data on equipment status , environmental conditions , and human activities , businesses are enjoying an era of unprecedented information wealth.
The data is there, users volunteer to collect it, but the question is : what to do with all this information? The Internet of Things opens a golden age for businesses, which with the right tools can unleash their full potential. But it also poses the challenge of figuring out the most effective way to process and use that data. The answer to this question cannot be obtained immediately, but rather derives Phone Number List from a learning process that teaches that: Collecting large volumes of data is not enough. The relevance of "raw" information is simply marginal. The integration of sources is a requirement for the usability of knowledge in decision making. internet of things Photo credits: Internet of things and change of mentality The Internet of Things implies a radical change in thinking . Traditionally , companies have used business intelligence tools to know themselves better, understand their processes and optimize them, discover their strengths and weaknesses. Then came advanced analytics, which gave them the ability to predict the future based on the analysis of trends and consumer habits, their universe expanded.
Today, other information is added, such as public data about the environment or local events , data produced by sensors that other companies have in the field , data about people that they themselves share on the network. This information can add much more value , but comes with great difficulties, such as integration . Combining this data is often difficult as it is usually presented in different forms . It is necessary to go beyond the experimentation part , the deployment of sensors and data collection ; It is necessary to increase effectiveness from implementation and, for this, the infrastructure is only one part, since it is also necessary: Visualization, through, for example, dashboards that allow us to understand the meaning of the data in order to make intelligent decisions based on it. Expertise, which guides organizations in their first steps towards the new reality that they can access through the internet of things. Related posts: The phases of a predictive analytics project Support and training in predictive analytics Predictive analytics vs prescriptive analytics.