IoT data continues to drive increasing changes on the path of digital transformation. Organizations are creating new data-driven business models to meet the ever-changing needs of consumers and the business. However, this transformation threatens to flood the data through the technical architectures of these organizations, causing a sharp increase in operating expenses, companies become more vulnerable to security attacks and critical points of failure in security systems.
Conducting an analysis of IoT data will help control data flooding and drive business value.
However, IoT analytics continues to face encouraging expectations. The degree of digitization of the industry in 2020 is expected to be 19% in Spain compared to 8% in 2017.
As the volume of data collected from sensors, devices, and other endpoints increases. The potential business value you can derive from this data continues to grow exponentially.
Analyzing the data is the key to obtain important and useful information from the entire torrent of data that reaches us, and thus be able to apply it to business needs. However, an IoT data architecture that is tailored to your purpose is required to properly collect the important insights.
The key to moving towards digital transformation is to get an analysis, that is, any mechanism that generates information from data. Such mechanisms range from simple to complex, mechanisms could include leveraging signal processing techniques or the use of advanced machine learning (ML). Any of these mechanisms for extracting information is considered ‘analytic’, and this analytics can be leveraged at any point in the IoT solution architecture.
The commercial value of IoT is based on data, specifically, on extracting and exploiting the business value of the enormous volumes of information generated by a large number of sensors and devices currently implemented. Data analytics drives business value and operational efficiency by enabling new ways to leverage large amounts of IoT data and by reducing the overhead of moving large amounts of data across a network.
IoT Needs New Infrastructure
IoT is creating an unprecedented amount of data in the company in terms of volume and speed, to extract value from this data, the data analysis architecture of the company must be renewed.
To act on IoT data in a timely manner, real-time streaming or analysis is required. The need to incorporate new analysis methods, such as streaming analysis and new infrastructure, is essential for companies.
The analysis that is carried out with IoT has some unique requirements compared to the analysis for other types of data. This includes the format of the data, the richness of the data, the sensitivity to time, where the data is stored and how long it is stored.
The Need For An IoT Data Analysis Platform
The key need for data analytics is to bridge the gap between data generation in the physical world and the need for action, whether in the physical or digital world.
Since not all data is neatly stored in a database, every device that produces data has to be catalogued, this is where IoT solutions come in.
We are faced with a number of security and privacy problems so we have to be able to have our IoT infrastructure ready to protect the system and protect data, so companies need faster, more flexible network management, achieve greater performance and safety.
Industries that are encompassing IoT analysis include energy exploitation (eg oil and gas), however other key industries such as manufacturing and transportation are becoming increasingly active in assessing IoT analysis
But what can be gained in an organization? The objective is to gain in efficiency, better adapt production and supply to demand, integrate the entire value chain of the company, decentralize decision-making, accurately predict results … This methodology allows 2.9% growth in the annual turnover of the companies that bet on an analysis of the data with IoT, as well as a 4.1% decrease in the costs linked to production.
Clients use the platform ( IDbox RT ) to obtain and store data embedded in devices to make decisions almost in real-time. The sensors send data over a network to the connected gateways. Data is captured and stored in order to be able to make decisions in real-time and to be more efficient so that customers can create a rule to guarantee that an email is sent to an administrator when a machine goes down or has a fault or when the temperature of a machine increases.