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IoT Data Analytics: How Data Analytics Affect IoT

IoT Data Analytics e1656004316676

Managing one’s business involves an intricate process, requiring every entrepreneur to think out of the box. Whether you own a small, average, or huge company, it is a must to know the different tools, programs, and platforms that can help in making your company productive and efficient. When it comes to reducing maintenance costs while improving customer experiences, IoT data analytics can be of great help. Are you familiar with this one? If not, simply use this article as your guide.

What is IoT Big Data Analytics and Its Importance for Organizations?

IoT Analytics refers to a platform that can access the information gathered from IoT equipment using deep learning technologies. This is perfectly suited to evaluate IoT information because the equipment usually produces various data. When it comes to the size and diversity of sources, IoT data is identical to big data. The heterogeneous information sources create data integration an unbelievably complex procedure. As a matter of fact, data integration is considered one of the major challenges to solve. This is the reason why this IoT Analytics or analytics method was introduced. 

Adoption of IoT

IoT adoption is becoming in-demand and often used by business tycoons. However, since the number of connected devices tends to increase, organizations find it hard to manage the gigantic amounts of generated information. Since the data gathered from IoT are less structured, the information sets are quite intricate.  IoT Analytics tools help organizations to leverage complex IoT datasets, increase earnings, improve products, and optimize products based on the needs of their customers. 

Say, for instance, the IoT Analytics platform provides insights into the optimal battery capacity, average usage patterns, and popularity of features. This level of analysis and connectivity helps companies fix issues and able them to release software updates. When fixes are quick and timely released, user intention and customer engagement upsurge significantly. In addition, instead of gathering and trying to utilize all the information, IoT systems and data analytics tools able to know how to accumulate vital data points, provide insights connected to the offered products and services and perform quick analysis. 

IoT Analytics can also save your precious time by reducing tasks relative to the data source integration. As a result, there will be an analytic data pipeline that gives full access to data. If at all possible, any role in the company can utilize the workflow to inquire and acquire insights.

A union between IoT and Data Analytics and Its Impact on Business

IoT and data are like twins that are fundamentally linked together. When companies get hold of vital information for the purpose of analyzing, they utilize the data collected by IoT devices. This is where the union of IoT and data analytics takes place.

This helps companies check immense volumes of data with distinct properties to produce meaningful conclusions. These conclusions can be in the form of patterns, trends, and statistics. These actionable insights using the predictive maintenance model, allow the companies to engage with various data to make informed and effective decisions. For some business owners, IoT data with analytics doesn’t only drive new decision-making using predictive analytics but also improves their monthly profits after using the prediction model.

Here are some of the business models and new revenue stream use cases you need to know:

  • The Outcome Business Model – Asset tracking and IoT allow you to pay for what your company produces instead of owning huge assets that boost substantial insurance charges. This is proven valuable for manufacturers that need to finance in niche areas like equipment and engines. Distinct IoT sensors can instantly know how useful they are and where the clients can be owed for certain usage. Through IoT Predictive analytics and maintenance systems, asset tracking made it so fast and easy. 
  • Asset Sharing – This business model is quite identical to the outcome model in that a client rents your assets. Nonetheless, whatever unused assets are then fed back into this system. 
  • Automatic Fulfillment – Since assets can be checked throughout the supply chain, this allows businesses to access their inventory and check their goods. IoT systems able to track, especially when the inventory is delayed or running low. 

In addition, organizations that monitor their energy and device use can sell their insights they collect to other businesses that may need similar applications. 

What are the Top advantages of IoT Data Analytics

Expanding or setting up enterprise IT systems to manage a large computing capacity of IoT data analytics needs a thorough planning. The good this is that its rewards outweigh its underlying challenges.

To name a few, here are some of the endless opportunities where IoT applications can improve business operations:

  1. Improved Human Efficiency – Efficiency is the best way to improve human productivity, resulting in huge revenues. To achieve this, the power of IoT must be properly used.  This can help in mobilizing company resources and optimizing the office floor plan for a more effective and streamlined approach and better workflow.
  2. Equipment Health Monitoring – IoT Analytics service devices are the most common useful stuff in the healthcare industries. Everything can be monitored in real-time, from blood pressure to chipped heart monitors. 
  3. Adaption to Automation – Using IoT analytic metrics can assist you in spawning the next generation of products. By incorporating smart devices into your items, it will be easy for you to understand its usage patterns and recognize errors in the existing design, giving you a chance to make some modifications.
  4. Customer Experience – IoT data and analytics processes can boost business insights and its ability to produce the best experiences for every customer. This real-time analysis is able to reveal the customer needs and other related information.
  5. Improved Decision-Making – An immense volume of new data from smart sensors and equipment adds to the huge pool of large data in the globe. Now, companies utilize that data to inform operational and strategic decision-making using prescriptive analytics. Operational decision-making is where analytics and data are already available to every member in the organization, usually through a self-service tool, while strategic decision-making is where the team leader recognizes crucial questions it needs solutions or answers.

IOT and Data Sensors

The combination of data analytics and IoT sensors also helps companies to determine when devices need maintenance by measuring heat, vibration, and other significant figures. Smart devices can also send information to operators about wear, potential breakdowns, and even delivery schedules. This does not only facilitates ordinary device maintenance but also subsidizes analytical maintenance. 

Truly, the opportunities with artificial intelligence like IoT are enormous for business users. It can also be game-changing for every business owner, especially those that are boarding on a digital adaptation.

How to Develop Infrastructure and Incorporate IoT Data Analytics Process?

The growth of IoT applications entails several activities which are often mixed towards deploying and developing non-trivial IoT data applications.

These activities include the following:

  • IoT Data Collection – This includes interfacing to IoT sources and enhancement of data with proper contextual metadata.
  • IoT Data Validation – This covers the validation of the source and format of data origin. The process also includes the validation of the data’s consistency, accuracy, and integrity.
  • IoT Data Analysis – This covers the application of machine learning schemes and data mining that lead to transforming IoT data streaming Analytics to actionable insights.

Other analytics strategy activities include IoT data structuring and storage, data semantic unification, and analytics algorithms. 

Microsoft Power BI Capabilities for IoT Data Analytics

Microsoft Power BI is a group that consists of a business intelligence tool collection like data connectors, apps, and software services for descriptive analytics. It is a cloud-based platform utilized to consolidate data from distinct sources into a sole data set. These data are often utilized for evaluation, data visualization, and data analytics by creating sharable apps, dashboards, and reports.

One of the Microsoft Power BI analytics capabilities you shouldn’t miss to know is the Get Data Source. This allows Power BI users to choose from various data sources. These sources are anywhere in the field from structured to non-structured, cloud-based to on-premise. Another is the dataset filtration. You can utilize the datasets to make visualizations of distinct kinds. It can be made of data from a single source like workbooks or from any other data source. 

Other capabilities of Microsoft Power BI for IoT advanced data analysis includes customizable dashboards, pre-built models, flexible tiles, information reports, navigation pane, embedded reports, and a lot more.

In short, IoT data analytics is the analysis of the generated large data volumes by connected devices using intelligent applications. Companies can derive several benefits from it like optimize operations, engage more customers, control processes automatically, and empower employees. The combination of data analytics and IoT has already proven to be advantageous not just in smart cities but also in healthcare, retail, manufacturing, and telematics.

Additional Feature of Power BI

Apart from providing cloud-based IoT data analytics, Power BI also features diverse stunning visualizations. Visualizations play a crucial role in Power BI as it provides multiple detailed and creative visualizations. You can make dashboards and business intelligence reports using both complex and simple visualizations. Data governance, on the other hand, is also one of the features of Power BI. It is defined as the exercise of control, authority, and shared decision-making over data asset management.

In addition, the latest Power BI security capabilities try to ensure the security of the data organization in Microsoft Azure. This data is protected when being utilized by employees in the organization. 

Another best feature of Microsoft Power BI is its external data sources. As one of the best cloud-based business analytical tools, Power BI helps in evaluating and sharing the data and gets associated with all the users’ data sources.

Power BI and Real-Time Analytics Capabilities

Organizations using Power BI as analytics software, commonly depend on constant data refreshes for real-time data. These are enough for some daily dashboards and reports with capabilities of real-time monitoring. However, as streaming data become in-demand, uplifting is not enough. In order to meet its evolving demand, Power BI provides two options for dashboards and real-time reports.

Analytical models or systems working in real-time or near-time can deliver knowledge based on what is happening at present, providing the organizations an edge over those decisions based on outdated, traditional analytics or past data.

Though it is crucial to foster a data-driven culture, you will not win the competition if you are not making decisions based on real-time data. Say, for instance, you need to consider customer service as they always expect that the agents have available real-time data. Organizations in distribution also need to know real-time updates about inventory and shipments. The main purpose of this is to have a corrective and reliable action. This is also one of the most common analytics functions that Power BI provide for IoT analytics solution.

Take note that no company is exempted from the advantages of real-time decision-making. Even though your data is not time-sensitive, real-time decisions allow you to make proactive and quicker decisions than other companies. 

EPC Group Approach for IoT Data Analytics

EPC Group is one of the renowned companies in the field of support and IT consultation. This company has been working with clients for more than 25 years. They continue to build cloud and digital platforms, workplaces, and tools to fully meet the needs of their clients. 

EPC Group has an all-inclusive perception when it comes to delivering quality solutions. They create a governed tactic around optimizing and curating data using sentiment analysis, Prescriptive Analysis, predictive analysis methods for smart decision making. They also create analytics that fits the needs of their clientele.

With several years in the industry, they dubbed as the United States’ leading collaboration, business intelligence, and information management company. Having a focus on software integration, the company has been groundbreaking in the way companies collaborate, analyze, and share data. 

What makes EPCGroup renowned is its various services and expertise in the field. They are professional in Azure Consulting, Power BI, and even SQL platforms for business intelligence, data warehousing, and data science projects. They have also authored four Microsoft reference guides on Office 365, SharePoint, and Power BI. Among others, they have proven proof-of-concept reports from the company leading Power BI Consulting agency. 


Innovative data analytics are not considered as a fancy add-on but a vital part of IoT integrated solutions for data-driven insights. These provide the organizations with the knowledge needed to produce personal and smarter business accurate decisions. They can even point out critical problems in a more effective and efficient way. IoT is powered by data capability, enabling profitable decision-making.

Errin OConnor

Errin OConnor

With over 25 years of experience in Information Technology and Management Consulting, Errin O’Connor has led hundreds of large-scale enterprise implementations from Business Intelligence, Power BI, Office 365, SharePoint, Exchange, IT Security, Azure and Hybrid Cloud efforts for over 165 Fortune 500 companies.

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