Data Analysis As A Starting Point For An IoT Business Solution

Data Analysis : Organizations are creating new data-driven business models to meet the ever-changing needs of consumers and business. However, this transformation threatens to flood data through the technical architectures of these organizations, causing a sharp increase in operating expenses , companies becoming more vulnerable to security attacks.

Conducting analytics on IoT data will help control the data flood and drive business value. The potential business value you can get from this data continues to grow exponentially.

Analyzing data is the key to extracting important and useful information from all the torrent of data that comes to us, and thus being able to apply it to business needs. However, a fit-for-purpose IoT data architecture is required to properly collect important insights.

Such mechanisms range from the simple to the complex, the mechanisms could include leveraging signal processing techniques or the use of advanced machine learning (ML). Any of these mechanisms for extracting information is considered “analytics”, and this analytics can be leveraged at any point in the IoT solution architecture.

The business value of IoT is based on data, specifically extracting and exploiting business value from the huge volumes of information generated by the myriad of sensors and devices deployed today. Data analytics drives business value and operational efficiency by enabling new ways to harness vast amounts of IoT data and by reducing the overhead of moving large amounts of data through a network.

IoT Needs New Infrastructure

IoT is creating an unprecedented amount of data in the enterprise in terms of volume and velocity, to extract value from this data, the enterprise data analytics architecture must be revamped.

Acting on IoT data in a timely manner requires real-time streaming or analytics. The need to incorporate new analytics methods, such as streaming analytics, and new infrastructure is critical in business.

Analytics carried out with IoT has some unique requirements compared to analytics for other types of data.

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The Need For An IoT Data Analytics Platform

The key need for data analytics is to bridge the gap between the generation of data in the physical world and the need for action, whether in the physical or digital world.

Since not all data is stored neatly in a database, every device that produces data has to be cataloged, this is where IoT solutions come into play.

We find ourselves with a lot of security and privacy problems so we have to be able to have our IoT infrastructure ready to protect the system and protect the data, so companies need faster, more flexible network management, achieve greater performance and safe.

Industries that are embracing IoT analysis include energy exploitation (e.g. oil and gas), however other key industries such as manufacturing and transportation are becoming increasingly active in evaluating IoT. 

But what can be gained in an organization? The goal is to gain 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 for 2.9% growth in the annual turnover of companies that opt ​​for data analysis with IoT, as well as a 4.1% decrease in production-related costs.

Customers use the platform to retrieve and store embedded data on devices to make near real-time decisions. Sensors send data over a network to gateways that are connected. Data is captured and stored for real-time decision making and greater efficiency, so customers can create a rule to ensure an email is sent to an administrator when a machine goes down or fails or when the temperature of a machine increases.