Industrial IoT Analytics– The Shift to Smart Manufacturing

Industrial IoT Analytics– The Shift to Smart Manufacturing

For Industry 4.0, data analytics is an integral part of its operational strategy; enriching everything from vehicles and manufacturing to warehouses and marketing. There is a definite ROI in aggregating previously inaccessible data from the network and the edge, and analyzing it to increase efficiency, monitor performance, save costs and stay competitive.

The power of Industrial IoT data

The Industrial IoT is driven by a universe of sensors that enables accelerated deep learning of existing operations. These data tools allow for rapid contextualization, automatic pattern, and trend detection. Applying this to manufacturing operations allows for true quantitative capture of formerly “expert” qualitative operations. There are several powerful use cases in a digital factory that is enabled by analytics and Industrial IoT technologies, including the ability to:

  • Manage machines, processes, and people with speed and agility
  • Monitor factory assets in real-time by analyzing historical operational data to predict failure and fix it before it occurs
  • Apply Video Analytics to improve processes in real-time
  • Quickly simulate and compare the results of retooling an entire product line

Of course, success with these types of advanced analytics and Industrial IoT initiatives does not happen overnight. Employing analytics, sensors, and other related technologies can have a snowball effect on uncovering new efficiencies or business opportunities. For example, data analysis or analytics tools provide a way to more accurately identify potential issues in your processes that might be ideally suited for Industrial IoT initiatives.

Applying Industrial IoT data analytics

SMEs contribute to the health of economies and business productivity around the world. They are critical suppliers, partners and customers in nearly all industries –particularly in manufacturing where they become important intermediate suppliers, selling their goods into global value chains through larger local or multinational companies. That is why it is important that SMEs keep up with their larger business partners and customers in the Industry 4.0 revolution.

Improving productivity is the most obvious and tangible benefit of adopting Industrial IoT technology and the related data, but the benefits for SMEs go well beyond that. Industrial IoT can create value along multiple dimensions such as driving growth through improved products, improved customer service and engineering, better operations and planning, and more efficient factory management and enhanced support functions. A huge variety of devices connected to the Internet and share data through sensors every day, which when effectively collected, analyzed, and stored helps achieve a variety of benefits for SMEs.

Let us consider some of these benefits.

Improved equipment maintenance
Industrial IoT data analytics helps SMEs determine when factory equipment requires maintenance by measuring vibration, heat, and other important figures. Smart equipment can also send messages to operators about potential breakdowns, wear, and delivery schedules. Workers can see exactly how their machines are performing in real-time, and stay updated about potential issues. This not only facilitates regular equipment maintenance but also contributes to predictive maintenance. Sensor data allows maintenance to be scheduled at the optimal time, thus reducing breakdowns and maintenance costs.

Operations optimization and automation
With Industrial IoT sensors and analytics working in tandem, SMEs can automatically control processes that previously could only be tracked manually. For example, they get a comprehensive view of what is going on at every point in the production and maintain a continuous flow of final products, identify bottlenecks in real-time, and avoid defects. Humidity, for example, can have a negative impact on the quality of a paint shop and this can lead to rejects. Therefore, Harley-Davidson has implemented sensors in its paint shop to detect the humidity level. The ventilation fan speed can be automatically adjusted in order to assure a consistent coat.

Customer experience enhancement
IoT-enabled field service can dramatically improve customer experience. Giving technicians access to CRM data from their tablet shows them a detailed customer history. And they do not need to call the office to answer the customer’s questions. SMEs can also build upon predictive maintenance with business data like CRM and EAM. When the machine learning algorithm predicts an asset failure, they can connect to the EAM system and check the warranty. By automatically checking the warranty, they can prevent compromising warranties and reduce maintenance costs.

Data from wearables
We are not talking about data from smartwatches and fitness, but a new breed of industrial wearables. These new wearables promise to make difficult and often dangerous jobs safer and easier. For example, data from wearable gas detection sensors can track employee exposure levels and can then be displayed alongside their work schedule. This helps dispatchers adjust the schedule based on the worker’s exposure. Another use case is for logistics service providers. Sensors can detect driver fatigue and trigger an alarm to stop the driver. This helps improve schedules, routes and safety practices.

Location data
Location data could come from mobile devices, location beacons, GIS systems or even drones. GPS data from a vehicle can be combined with traffic reports to optimize delivery routes in real-time. SMEs could also place track-and-trace sensors on expensive mobile assets that often get stolen or misplaced. A vital IoT data application is using real-time location data to avoid vehicular accidents. Streaming real-time data from location beacons can help prevent fatal accidents. When a vehicle passes a beacon, the IoT application can automatically check whether the vehicle has the correct clearance certificate.

Inventory Management
IoT technology can eliminate the need to physically scan individual parts to get an accurate count or location. RFID chips – easily affordable – can be placed on products and remotely connected for real-time visibility into product locations and quantities. For SMEs manufacturing perishables like food, RFID tech can raise an alert when a product is approaching its expiration date. Optimizing the supply chain is a huge benefit of Industrial IoT data.

The technology of the future

Industry 4.0 is no longer a vision. Best-in-class firms are using analytics and Industrial IoT (IIoT) to make better decisions regarding assets, products, processes, and operations, and it is driving significant returns. IIoT analytics help SMEs get a better understanding of the manufacturing and supply chain processes, improve demand forecasting, achieve faster time to market, and enhance the customer experience. However, considering the scale and the complexity of the IIoT initiatives, successful adoption requires thoughtful orchestration, analytics and management of the tons of data the IoT generates.

Seeking assistance with data management is imperative

Managing data from IoT devices is an important aspect of a real-time analytics journey. This large chunk of data needs to be managed appropriately to reduce complex challenges. To be sure that an SME can handle IoT data demands, they need to build several capabilities such as versatile connectivity and ability to handle data variety, edge processing and enrichments, big data processing and machine learning, real-time monitoring and alerting, etc.

This can be overwhelming for an SME, especially when it is not even in the business of handling data.

Building IIoT data analytics expertise in-house can be challenging; especially for SMEs without the same financial, human resource or technology ecosystem options as larger firms. An Inmarsat study revealed that 72% of businesses have a shortage of people at the management level with experience in IoT, while another 80% reported a lack of skills among employees in IoT deployment. SMEs are wary of unknowingly tying themselves into a platform that may not last the course. That is why engaging an experienced data analytics player becomes critical.

How BFSI Firms can Leverage Data to Navigate through the Pandemic

How BFSI Firms can Leverage Data to Navigate through the Pandemic

Soon after the WHO declared a COVID-19 pandemic, there was utter chaos all across the financial world. Banks, NBFCs, fintech firms…all were hit hard by drastically pivoting market conditions and deteriorating credit quality among others. Lockdown situations in most countries and industries resulted in severe dips in cash flow with deteriorating corporate revenues and depletion of credit facilities. Governments across the world announced financial measures to ease payment pressures on individuals and businesses, such as extended moratoriums on loan payments, adding to liquidity woes.

Investors began pulling out their money, stock markets crashed along with oil prices, and central banks had to inject liquidity to keep the economy moving. Both the supply and demand sides dulled, thus impacting the economy. Uncertainty clouded investment decisions taken by investors and shareholders operating on financial markets, including securities markets.

And the problem will not go away soon. Moreover, the current downward trend could worsen which could impact the industry for years. The question everyone is asking is – what can help the BFSI industry sustain through to the other side of the pandemic?

Among other strategies, one instrument that BFSI firms can use to stir through this crisis and build further resilience is data engineering and advanced data analytics. BFSI analytics can help focus on spending patterns and customer behavior, primary transaction channels, fraud management, risk assessment, amongst others and help banks take steps from the what, to the why and finally, the how.

Risk Modeling
Poor credit quality will result in an increased number of default cases, more requests for forbearance and rising credit risk provisions. Banks need to cope with recalibrations of rating models and an analysis of credit portfolios in light of the pandemic. This will require collecting and sifting through massive amounts of customer and credit data. Analytics will process all that data at scale and perform quantitative risk analysis for better risk modeling, evaluating market risk, value at risk, accelerated credit review, and so on.

Liability Analysis and Delinquency Detection
Loan delinquency has become a bigger problem for banks during the COVID risk and will be devastating if it goes unchecked. Data analytics in BFSI plays a vital role in giving financial firms early warning predictions using liability analysis to identify potential exposures prior to a default. AI-based analytics uses drill-down reporting making it easier to detect criminal activities like fraud and money laundering by identifying transaction anomalies. Analytics helps issuers proactively use account pattern-recognition technologies and take proactive maintenance strategies by segmenting delinquent borrowers and identifying self-cure customers.

Growing Fraud in a Pandemic
The pandemic has provided the perfect storm for fraudsters to flourish, thanks to a more digital environment. Analytics sift through structured data (transactions) and unstructured data (emails, reviews, forum entries) and help BFSI companies identify potential fraud by analyzing the most recurring operational patterns regarding trades, purchases, and payments. Financial firms can use prescriptive analytics to evaluate their internal fraud control measures by looking at statistical parameters, data anomalies. AI’s high computation power will alert banks to potential fraud in payment, customer identification and so on, while ML algorithms will reduce false positives.

Credit Scoring
Various companies, especially MSMEs, are strapped for funds. They were just about making a comeback from the 2008 financial crisis when the COVID pandemic pushed them off-track once more. When evaluating them for financial support, banks typically use only credit scoring, which is not holistic and looks only at credit and financial details. This is not enough protection against loan defaulters. To determine a more valid credit score, BFSI analytics examines all available information –both structured and unstructured – using an algorithm to calculate the size of the risk the bank would take if they chose to underwrite that customer. AI-powered credit scoring models will reduce credit risk and enable decision-making and actions that are transparent and based on data.

Risk Hedging
Being able to sort out customers before they default on their installments helps banks avert disaster when the debt becomes overwhelming. Data analytics in BFSI allows banks to quickly adjust their hedging strategies across forex, commodities, equities, or fixed income as the pandemic situation evolves. They can use analytics to build portfolios and hedge risks by either setting a higher interest rate or offering a new payment schedule.

Liquidity and Treasury Risk
Liquidity stress models that were revised after the 2008 crisis are not fine-tuned to manage the liquidity crisis today, so BFSI companies need to pressure test and revise certain models. BFSI Analytics helps banks build credit line models with an additional layer of judiciousness and loan models with more flexibility to meet requirements during a pandemic. Financial firms can also use analytics to increase the flexibility of liquidity models for ad-hoc recalibration.

In Summary
To navigate the crisis brought on by the pandemic, Banking, Financial Services and Insurance sector companies worldwide must ensure that their business models, strategies and methodologies are fit for purpose and fortified with a solid recovery plan and governance models. They need to re-adjust their risk appetite statement and recovery thresholds by building a layer of BFSI analytics that can help them with:
• Managing liquidity, navigating new policies and preventing losses
• Model implementation and quick revision of risk models
• Flexible data visualization and risk analysis
• Monitoring trends and identifying emerging risks
• Insights into strategic actions
• Augmented underwriting powered by AI

In the midst of all this chaos, financial institutions have to be able to analyze new scenarios faster and learn from frequent updates to forecasts, business, funding, and capital plans. Data analytics will help companies in the BFSI sector to remain resilient and competitive in these challenging times.

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The Analytics of Things – Why Data Analysis is Good for IoT

The Analytics of Things – Why Data Analysis is Good for IoT

Internet of Things (IoT) strategies are being adopted by industries world over to improve their processes and deliver a unique customer experience. From pens, lanyards, smart tags in stores and switches to air conditioners, refrigerators and traffic lights, IoT is being deployed almost everywhere.

But implementing IoT at scale requires managing millions of connected devices to gather and transmit data that is at once generated and essential for IoT devices. Naturally then powerful data analytics solutions become necessary to help businesses understand what this data is telling them. It can be a daunting task for any organization to build a functional, scalable and secure IoT network and datacenter without an experienced technology partner with the relevant expertise.

To understand the scale of data generated by these devices, we need to only count the number of IoT endpoints or a number of devices with each one capturing different parameters. This number runs into billions worldwide. Imagine the final volume of the data that is generated by these devices including sensors, actuators, tags etc. In fact, according to the Grand View Research, the global IoT analytics market size is expected to reach USD 57.3 billion by 2025 at a CAGR of 29.7%.
These staggering numbers could mean a data nightmare and raise several questions like:

  • Should all the generated data be stored and how?
  • How can we derive meaning from the data generated?
  • Is it possible to handle the high velocity of the data stream?
  • How do we secure IoT data and prevent misuse?
  • How do we enable reliable data communication between sensors, from sensors to applications, or even from sensors to a cloud?

IoT Analytics is an emerging area of data science and data analytics that can provide answers to these questions. The full capability of IoT for business use lies in connecting IoT to the data generated within the IoT ecosystem. There are various types of analytics that can be applied to IoT:

  • Streaming Analytics: Analyzes huge in-motion data sets, i.e. real-time data streams, to detect urgent situations and immediate actions and help areas like financial transactions, air fleet tracking, traffic analysis etc.
  • Spatial Analytics: Analyzes geographic patterns to determine the spatial relationship between the physical objects and helps in fields like smart parking.
  • Time Series Analytics: Basis analysis on time-based data which is analyzed to reveal trends and patterns and can be applied to weather forecasting applications and health monitoring systems.
  • Prescriptive Analytics: Combines descriptive and predictive analysis to help organizations evaluate the best action that can be taken to troubleshoot or improve a particular situation.

But do note that IoT Analytics is still an evolving field and is still dealing with complexities related to streaming and real-time data. IoT data comes in huge volumes, is highly unstructured, and differs in terms of format (text, image or videos). Moreover, while the operational technology relates to the data collected from temperature sensors, pressure sensors, tablets, smart manufacturing devices/tools, etc., the information technology it needs to be set in context to the data collected from enterprise systems, legacy systems, ERP, CRM, and finance systems.

Moreover, the total amount of data being collected may be so large that it may not be possible to move it to a central location. All IoT devices need to be connected and inter-operate. However, this requirement also raises data security issues, based on the age-old principle that a chain is only as strong as its weakest link. In IoT’s context, if the security on a specific vendor’s outdoor sensor is weak, and the sensor is connected to other devices, the likelihood of ‘indirect’ critical impact is high. Attackers can compromise the sensor and modify its data or exploit the connection to other devices to cause damage. Similarly, devices can also malfunction and transmit false readings to the system and throw IoT analytics off course.

Despite these challenges, IoT-generated data must be able to give organizations a clear picture of their desired outcomes in order to ensure that the analytics technology used to gain insight from that data is aligned with business needs. The good news is that analytics technologies have rapidly become advanced, and it is now easier for even newly generated data to be available for analysis almost instantly. IoT can be processed within a matter of seconds and can be retained efficiently and cost-effectively for a longer duration.

Wearable Medical Devices – And All That Data They Bring!

Wearable Medical Devices – And All That Data They Bring!

When I got my first fitness tracker, a smart wearable device that was easy to strap on my wrist during my workout and walks, I felt like the ultimate tech geek. It could track my heart rate, count my steps, measure calories burnt and event monitor my sleep patterns. And back then this was a revolutionary step towards fitness and good health.

Today wearable devices are gathering and curating a phenomenal amount of health-related data providing real-time information about the user’s health. In addition, the hardware market is rapidly evolving with new state-of-the-art medical devices, apps, and other programs, offering us the ability to monitor everything from glucose levels to UVA exposure to medication schedules. We are even seeing a definite move towards the use of wearable devices in clinical trials to collect results from patients to improve understanding of how patients respond to treatments.

The wearable medical technology market continues to expand as consumers are increasingly persistent about using technology to track their health. According to research a whopping 80% of consumers want to wear fitness technology to monitor their health and are willing to pay for subscriptions. Smartwatches, like Apple Watch, and fitness trackers, like FitBit, hare the most popular among consumers and will continue to dominate the wearables market. According to an IDC research, by 2023, watches will account for nearly 50% percent of the entire wearables market, while the overall global wearables market will grow to USD 54 billion. The more accurate information generated by wearables offer insurers more opportunities to personalize health care plans, that save employers and consumers a lot of money. 

Why wearables appeal to businesses

Businesses can decrease health care costs through personalizing insurance plans by providing employees with wearables. For example, in 2019 Omron launched HeartGuide, the first wearable blood pressure monitor which looked like any other smart watch but functioned as an oscillometric blood pressure monitor. It stores a user’s blood pressure and daily activity, and then transfers the data to a mobile app from where the readings could be reviewed. This data would allow the health insurance firm to recommend treatment and a suitable insurance plan. John Hancock, a leading US-based life insurer, has already announced that it will no longer sell traditional life insurance policies, replacing them with interactive policies that track fitness and health data through wearables, offering discounts to policy holders who regularly meet exercise targets. They also offer gift cards for workouts and healthy food purchases, while charging higher premiums to customers with high-risk habits. 

Better medical treatment through wearables

By tracking data from wearables, healthcare providers are able to personalize treatment plans and deliver better healthcare. Rather than relying on possibly incomplete or delayed reports from patients, doctors can view data from wearables data in real-time and make a more informed analysis. Devices can also alert users when it’s time to take medication and even alert doctors when a prescribed schedule is not followed.

Obviously, there are a lot of wearable medical devices that have the potential to provide useful patient data, especially for patients who have chronic medical conditions. But how is all that data to be managed so that healthcare professionals extract only the nuggets of value in all that data? Such wide-ranging benefits of monitoring health using data from wearable devices can only have a positive impact when a comprehensive data analytics solution is used to accumulate patient data and process it for individual patient care needs.

If doctors can get the right kind of data, the information collected from these wearable devices holds the potential for more accurate diagnoses, personalized treatments, and better outcomes. However, in this context more data does not directly mean more value. So how can healthcare firms manage this data?

Mining and analyzing data 

Technology partners can help healthcare firms present this data to both patients and physicians in a way that’s both meaningful and actionable. A PDF file here, a report of steps taken daily here, a blood glucose app output there. Doctors are already frustrated with their EHR systems, so adding more data to the pile without proper support will eventually cause the entire process to collapse. The smart way to manage this data deluge is with technological assistance such as algorithms and machine learning (ML) programs that will take all this data, make sense of it, and then present valuable insights to the doctor. 

Analytics can help interpret the data churned out by wearables and thus improve overall patient outcomes and community health. These devices are usually monitoring constantly, so any analytics solution should be robust enough able to handle the huge amount of data. Demographic, usage, and consumer expectation data is going to be pouring in from these devices, which will then have to be analyzed to churn out insights. The key requirements from any data analytics solution will be speed and the ability to store a continuously growing amount of data while analyzing it using pattern recognition and machine-learning-based predictions.

Wearable data is already ballooning to huge proportions and the challenge will be not just to analyze it, but also to offer personalized insights. For example, by understanding the data captured by wearable technologies, brands can create personalized marketing offers for each consumer. Using advanced analytics, wearable device companies can realize the potential that each device offers to the user and to healthcare professionals. Remember that any collected data is only as good as the action that it propels.

Having said that, do keep in mind that wearable devices collect a lot of data that’s stored either on your smartphone or stored downstream on a cloud. And this data is worth ten times that of a credit card on the black market. So while a lot of us could be using this tool to track and enhance our health, imagine if that data was stored carelessly and could be hacked by a malicious third party. Privacy policies of wearables for healthcare are vague and ever-changing, ranging from “We respect your privacy” to “We may share your information with third parties…”. Basically, in most countries, expect that wearable device manufacturers can legally share your sensitive data without your permission.

Tech companies and healthcare providers will need to explain clearly where and how this data is being used if consumers are to sign up. Improving healthcare using wearable devices should be contingent on patient data being anonymized. Patients must be educated on how their personal data will be used. As we wait for wearable technologies to mature and collect better data, it would also be a good time to figure out where and how information is stored, whether and how it might be used and disclosed, who has access to it, and what safety measures are in place to protect it.

Analytics Are Making Us Smarter About Outbreaks

Analytics Are Making Us Smarter About Outbreaks

In 2014 Liberia’s hospitals were overflowing with people infected by the deadly Ebola virus, and there were sick people lying on the ground outside hospitals, writhing in pain. Their only hope of getting treatment was if someone else died first, freeing up a bed. By 2016, the Ebola outbreak ended with more than 11,000 deaths (reported ) across West Africa. Experts were unanimous in their analysis that even in the worst months of the outbreak, whole countries were unprepared for such a catastrophe.

The World Health Organization (WHO) eventually declared a Public Health Emergency of International Concern but it came too late, underlining how we suffer from a lack of timely data, unrelated datasets that are difficult to collate, and a shortage of people with computational skills to help prepare for and respond to global epidemics. What if there were an early warning system for such outbreaks that could have given WHO a heads-up, allowing them to organize an effective response and contain the disease’s spread? Thankfully with the AI and data science revolution we see today such a system may not be too far in the future. 

Why managing pandemics needs data analytics

Detecting an infectious disease is usually an after-the-fact-activity, and stopping it from causing an epidemic requires real-time information and analytics, because controlling a pandemic is not about where the disease is occurring today. It’s about where the disease is occurring and who is most vulnerable to it. That combination of information can help health experts and organizations like WHO look for long-term catalysts, such as how climate affects the spread of a pathogen like the coronavirus.

To help governments across the globe track, respond to and prevent the spread of the coronavirus, health experts are turning to advanced analytics and AI to prevent further infection. Several researchers are even looking at the Internet of Things (IoT) to collect sensory data in real-time and track people, health systems, and environments, even in remote regions of the world.

IoT and Big Data are helping with disease control
It is now possible, thank to IoT and big data analytics in healthcare, to collect data from places where previously it was either done manually or not done at all. For example, smart thermometers feed data in real-time to global medical systems, bench-top analyzers are scanning patient samples and sharing data almost instantly with disease monitoring tools installed remotely. Disease monitoring tools are merging IoT data with population data, GIS data, land-use information, social media streams, and other sources to detect emerging public health threats.

By collecting and analyzing data from remote locations, clinical researchers are in a better position to make an evidence-based analysis of a possible outbreak and suggest preventive measures using data from the IoT devices. As a result, identifying and preventing the spread of infectious diseases proactively is now a reality.

Using AI to track pathogens

There are several ways that government health agencies can use AI technology to limit the spread of diseases like the coronavirus. Researchers are turning to AI to help predict locations where new diseases could emerge by integrating global data about known viruses, animal populations, human demographics etc. to predict epidemics. AI can also help reduce the time required to detect an outbreak thus enabling faster action to stop the spread and effectively treat the infected.

According to the founder of Alibaba, the company’s new AI system can detect coronavirus in CT scans of patients’ chests with 96% accuracy against viral pneumonia cases. They have developed a new algorithm that has shortened the process of recognizing the pathogen/infection to a mere 20 seconds, which is a big improvement from the 15 minutes that traditional methods take to analyze a CT scan. Baidu’s new AI tool called LinearFold promises to reduce coronavirus prediction time from 55 minutes to 27 seconds, which is crucial for understanding the virus and initiating drug discovery. 

AI can also analyze and aggregate travel, population and disease data to help predict not just how, but also where, a disease might spread. When it comes to treatment, radiologists are using AI technology (machine learning and deep learning) to extract insights from large data sets and make better treatment decisions based on medical imaging. Taking coronavirus as an example, data from chest X-rays of infected people can help build AI models so doctors can make quicker diagnoses. AI can also help shorten the time it takes to create vaccines for newly discovered pathogens by examining data from similar viral diseases and then using it to predict outcomes. 

And it doesn’t stop there. After an outbreak has ended or has at least been contained, governments and global health organizations can use machine learning to simulate different outcomes to test and validate policies, public health initiatives and response plans based on “what if” analyses.

The importance of data analytics is incontrovertible 

While analytics and ML aren’t sitting in local doctors’ offices taking samples to be tested, they are being applied to help the overall effort and make doctors and healthcare organizations more efficient and better equipped to fight epidemics. When used effectively, these tools have the potential to save lives. As an example, the Johns Hopkins University’s Center for Systems Science and Engineering has developed a real-time visualization of the coronavirus epidemic which includes a map, total numbers of cases, deaths, and people recovered. The data, sourced from WHO, CDC (in the US) and others, is also broken down by country and the numbers of cases are represented on the map using dots. Predictive analytics can also be applied to data from public locations to predict disease spread and risks and plan for the impact of an outbreak on healthcare organizations.

Machine learning can churn out high-resolution world maps highlighting where epidemics are likely to infect people, by using remotely-sensed and other geographic data about environmental, human and animal factors. Experts are taking complex infectious disease datasets and feeding them into large-scale computational disease spread models. This allows them to generate hundreds of terabytes of computer-generated synthetic outbreak simulations that give an idea about expected numbers of cases, hospitalizations, deaths, and even financial losses. 

Currently, we are in a critical juncture as experts and governments shift their focus towards containing coronavirus. The role of surveillance, drug discovery and diagnoses has become crucial, and with analytics and AI, there will be a tremendous saving of time and hopefully, lives.

IoT Analytics: Telling It Inside Out / Building The Complete Picture

IoT Analytics: Telling It Inside Out / Building The Complete Picture

At an estimated USD 3.9 Tn Industry 4.0 is being seen as the domain with the most to gain from the Internet of Things (IoT). Manufacturing firms world over are using IoT to improve operational efficiencies, to automate and to innovate to discover additional sources of revenue with new business models. Enterprises are realizing that data analytics and connected devices will be required to enjoy higher efficiencies and process improvements.

With the advent of IoT, Industry 4.0 has taken this digitally driven transformation to another level via interconnectivity and access to real-time data. A variety of sensors, dramatic increases in storage capacity and processing power, real-time analytics of unprecedented sophistication, and the ability to translate that data into meaningful action – are all helping organizations predictively maintain equipment and operations in order to optimize performance. The data analytics being derived from IoT sensors is helping companies forecast potential issues, minimize downtime and also eliminate any guesswork from preventive maintenance. IoT analytics is being used today to structure, process and analyze data and churn out invaluable insights and support better decisions.

One key advantage that the trifecta of IoT, big data and advanced analytics brings is that it enables systemic interoperability and collaboration between diverse teams and operations delivering cost and efficiency benefits. IoT analytics are playing a crucial role in modern industrial systems, adding an information layer to the conventional methods for data collection, storage and analysis.

IoT analytics for preventive maintenance

Until recently, factory managers and machine operators carried out scheduled maintenance manually and regularly repaired machine parts to prevent downtime. This process was time consuming and counter-productive, and despite the time invested most of the predictive maintenance steps taken were ineffective. Implementing IoT to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allowed enterprises to lower service costs, maximize uptime, and improve production throughput.

They are now building blueprints of a connected system that includes equipment and sensors, business systems, communication protocols, gateways, cloud, predictive analytics, and visualization. This allows IoT sensor data to be captured and used for predictive analytics that can be applied to the machine data and predict conditions of upcoming failure. A dashboard for predictive analytics processes operational data allows engineers to address actionable insights and take corrective action. Rule-based predictive maintenance allows enterprises to bypass the need for large historical data sets – at least initially – or advanced machine learning algorithms, instead giving them faster results and a step into advanced analytics.

Advanced analytics with predictive alerts and automated root cause analysis can be applied at a later phase, and that’s when historical data can be used to accurately predict issues. IoT analytics help organizations enhance overall efficiency, improve safety procedures and apply quick fixes to maintenance problems. Machine learning and AI technologies are giving more impetus by helping enterprises connect disparate data sources and gain insights for forecasting future performance.

Making grids smarter with IoT analytics

The transformation of electrical grids into smart grids is perhaps one of the major technological challenges, and achievements, of the past decade and also one of the key growth areas for IoT analytics. Smart-home technologies and the corresponding analytics are an integral part of many use cases in this field. Smart grid solutions based on IoT technology are playing a huge role in energy conservation by connecting disparate platforms in home automation, building and infrastructure automation, as well as in transmission and distribution systems.

Smart grids collect much more data than the manual energy meter reading system, which warrants the need for data analysis and highly realistic consumption forecasts that take a multitude of variables into account. Smart grid analytics are expanding because there are exponentially more data available thanks to IoT sensors to develop analytical models that could even predict future failures. What makes the IoT smart grid better is two-way communication between connected devices and hardware that can sense and respond to user demands, and also gather performance data and feed it back to the supplier offering deep analytics and insights. Data analytics combined with grid visualization can lead to better situational awareness, preventive maintenance and fault detection, as well advanced metering infrastructure and the security of the power system.

IoT sensors connect to the gateway, which in turn connects to the cloud and enables access to sensor data remotely via mobile devices. Sensors also collect energy consumption data on real-time from devices, and this data is analyzed by the gateway, which then escalates the necessary output or command message (like a utility command, an alert, HVAC control etc) to the control system. IoT devices also help analyze energy utilization of each device, which aids the user in managing device up/down time. Enterprises consuming energy can access historical data from the cloud, derive insights and accordingly optimize their consumption of energy.

The IoT has allowed companies to move towards a new way of doing business by applying it to various processes in order to enhance productivity and efficiency. But the real value of IoT lies in using the data from the cloud and the edge to get better analytics and derive insights from raw data. Analytics can play a much broader role and influence business practices, predictions, ROI, decision-making and more. Combining the IoT with advanced analytics takes businesses to the next level by offering transparency into business operations, insight into market trends and highlighting opportunities for improving the business.

We, at Tibil, understand the immense potential data holds for business and help enterprises harness its power fully, helping them achieve larger business goals through reliable and intelligent management of data. Our IoT Data Analytics services help you leverage your IoT device data to create immersive and insightful reports that can be combined with contextual data. We drive big data analytics, predictive analytics and customer analytics using new generation data analytics technologies, ML/AI and industry-grade statistical models to deliver advanced, real-time analytics. Reach out to us at [email protected] for more information.