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How manufacturing data analytics is driving production automation
Manufacturing is arguably the most important industry in an economy, as it makes everything we use and love. It’s safe to say that the manufacturing industry has had a hand in making the world as we know it today and will continue to do so in the years + decades to come.
From the advent of coal and steam power to the adoption of electricity, the industry has reinvented itself several times over the last few hundred years. In recent years, thanks to advancements in engineering technology, production optimisation, and better quality control processes, the industry has grown leaps and bounds.
Now, as the world heads into the fourth industrial revolution, however, there is a growing need for the manufacturing industry to adopt novel and independent manufacturing technologies that can not only help handle contemporary challenges but also minimise the use of human resources in the production process.
There is consensus among manufacturers across the globe that production automation is the way to move forward in industry 4.0, and manufacturing data analytics is the key to implementing efficient automation processes that deliver better efficiency and productivity compared to human resources.
Data analytics is not a new concept for the manufacturing industry, but the dynamics of modern manufacturing demands greater integration of data analytics to deliver automated production processes that increase plant predictability, agility, and quality control.
In this post, we take a deeper look at how manufacturing data analytics drives modern automation efforts in the manufacturing industry.
How is data analytics used in the manufacturing industry?
Manufacturing data analytics platforms collect data and analyse it to reveal useful insights, which can be leveraged for various decision-making purposes. The additional insight makes these solutions incredibly useful across the production chain.
From equipment maintenance to studying market trends, manufacturing analytics can be leveraged for different use cases to make decisions that generate a higher ROI and lower operational costs.
The use of edge analytics to build smarter production equipment
Unlike in traditional manufacturing, modern production hardware is always connected to the web using a wide range of IoT devices in the form of sensors and cameras. These IoT devices collect and transmit data to centralised data processing systems to produce insights that help optimise the next production cycle.
That said, the increasing pressure to produce quality throughput on manufacturing plants means that they can’t wait until the next production cycle to optimise processes and minimise errors.
With edge analytics, instead of streaming collected data to centralised data analytics systems, manufacturers can build smart manufacturing equipment that process data in-device, and make real-time optimisations in the production process—ensuring tighter tolerances and reduced resource wastage.
Material handling in manufacturing plants, for example, relied heavily on human resources until recently. Today, with the use of manufacturing data analytics, manufacturers have automated material handling by using Automated Material Handling Systems (AHMS) that detect material requirements and deliver them to the particular workstation—making the interaction between different stages of the production process seamless.
The integration of robotics in the manufacturing process
Traditionally, manufacturers relied on human intensity to conduct tasks that require extreme precision or a high level of complexity—think businesses operating within the fashion and automotive industries.
That said, now, due to advancements in manufacturing data analytics, artificial intelligence, machine learning, and robotic technology, manufacturers can leverage advanced robotics systems that handle complex tasks that require a high level of precision.
Adidas, for example, has opened a robotic manufacturing plant, dubbed the Speedfactory, based in Germany, which utilises robots in every step of the production process—reducing the six-week shipping time from Asia, where Adidas produces the majority of its 400 million shoes per year.
Another fashion giant, Zara, operates 14 robotic apparel factories in Spain, which deliver new fashion products to the showroom floor within ten days of finalising the design.
When it comes to robotics in the production process, we can’t miss the automotive industry, as it was one of the early adopters of production automation through robotics. Almost all of the major auto brands utilise robotics in their production process to some extent. Tesla is even building multiple Gigafactories around the world that use fully automated production processes.
How can manufacturers benefit from data analytics?
When considering the big picture, manufacturers benefit from data analytics through a significant reduction in uncertainty across decision-making.
Today, senior executives often make choices with incomplete sets of information that lead to unforeseen consequences. What’s more, visionary and innovative choices may be perceived as risky because there is a lack of evidence to back up the practicality of the ideas behind them.
Manufacturing data analytics can eliminate the risk from the decision-making process by providing a complete set of information to back up more creative, intuitive choices.
Manufacturing data analytics is building the factory of the future
As the business world evolves and consumer preferences change, the manufacturing industry needs to reinvent itself one more time to keep up with the growing demand.
Manufacturing automation and data analytics hold the key to building future factories that are smarter and more efficient.
What are the benefits of data analytics in manufacturing and how does it improve product quality?
In the truest sense of the word, manufacturing is the engine of the world economy. In fact, the manufacturing industry is one of the most valuable industries to any economy.
For example, the GDP of the two largest manufacturing hubs in the world, China and the USA, had contributions of $4 trillion and $2.3 trillion respectively from the manufacturing industry.
Without manufacturing, we would not be able to access the products and services we love and use. From the cars we drive to the phones we communicate with, we depend on the manufacturing industry to deliver our beloved products to us.
Ever since the industrial revolution transformed how we approach manufacturing, the business world has been looking for new ways to streamline the process even more; leading companies are outsourcing their manufacturing processes to more efficient offshore plants, and manufacturers are automating their production processes to reduce human errors and improve quality.
With the help of these advancements, leading manufacturers across the globe can produce thousands of products with tighter tolerances and minimal human intervention.
The most critical advancement in the manufacturing industry in recent years, however, has been the integration of data analytics into the manufacturing process, which has given manufacturers enhanced production capabilities.
In this post, let’s explore the benefits of data analytics in manufacturing and how it’s improving product quality across all industries.
The benefits of data analytics in manufacturing
- Data analytics powers better product design
Traditionally, introducing a product design to the market involved a lot of trial and error. Most often than not, the first iterations of the product design had an underwhelming welcome among consumers due to the less than ideal ergonomics and design.
Today, however, with the power of data analytics, manufacturers can test their designs for efficiency and ergonomics without ever making a physical prototype.
Modern data analytics tools utilise machine learning and artificial intelligence to create computerised product designs and help manufacturers put them through their paces.
In automobile manufacturing, for example, even wind tunnel testing has been moved to systems powered by data analytics.
- Automated manufacturing powered by analytics algorithms
We need to talk about the rise of automated manufacturing when considering the benefits of data analytics in manufacturing.
Today, in many industries, the manufacturing process involves minimal human interaction—everything from delivering raw materials to quality control of the finished products is executed through advanced algorithms powered by data analytics.
The tight integration of data analytics also allows manufacturers to eliminate issues due to human error. A recent study found that 23% of unplanned idle time in the manufacturing process is due to human error.
With automated manufacturing systems, manufacturers can minimise unplanned downtime by optimising the analytics algorithms to detect potential anomalies in the production process or equipment and conduct proactive maintenance.
- Efficient product management
Deciding how much to produce is perhaps one of the most critical decisions across the entire production process. Overestimating the demand can lead to overproduction, costing the manufacturer millions of dollars in the process.
Underestimating market demand, on the other hand, can tarnish the reputation of the company due to the untimely delivery of products and services.
Therefore, manufacturers need to estimate their demand accurately to maximise their profit.
Data modelling and predictive analytics use historical demand data and simulate future market conditions to produce accurate demand forecasts for the future, helping manufacturers meet the market demand without wasting their resources.
The benefits of data analytics in manufacturing are powering the next industrial revolution
Today, many industries across the globe are going through the fourth industrial revolution—driven by the power of data analytics and digital infrastructure—and the manufacturing industry is no exception.
The benefits of data analytics in manufacturing enable modern manufacturers to enhance capabilities to deliver more quality and efficiency in the production process.
How big data analytics platforms in manufacturing optimises production
The manufacturing industry has always thrived, thanks to the right technology and tools.
During the 1800s, the industry thrived due to the development of machinery. Now, as the fourth industrial age takes over, we are seeing the advent of new technology that can boost productivity and remove traditional obstacles preventing the industry from reaching its full potential.
The main challenge, today, is the absence of a platform that can analyse data and break it down into useful insights that optimise the manufacturing process. Fortunately, big data analytics platforms in manufacturing can optimise your production and cut down on costs.
While I have explored how data analytics can improve manufacturing in the past, in this post, I focus on how we can boost productivity and operating costs.
Optimising manufacturing processes with data analytics
Here are some of the ways big data analytics in manufacturing can add value to your operations.
Manufacturers can ensure quality across batches
While custom manufacturing is a consumer-friendly practice, there are certain industries where consistency in quality is key; for example, pharmaceuticals. In this industry, the product is often made in a series of batch processes, which can lead to inconsistencies in product quality.
By using data analytics, organisations can conduct multivariate data analysis. The intent, here, is to better define quality control parameters and use continuous process verification to ensure consistency in product quality.
Furthermore, real-time manufacturing analytics can provide manufacturers with the insight they need to reduce batch variability. They can use the information to adjust process parameters during manufacturing to reduce deviations that might compromise product quality.
Manufacturers can predict trends, causes, and effects
Advanced data analytics uses high-level methods to conduct sophisticated multivariate data analysis (MVDA).
This new level of analysis allows manufacturers to analyse data using several variables at the same time. What this means is that manufacturers can perform “what-if” calculations, allowing them to conjure multiple scenarios. The ability to project the future, based on different variables, can help your company future-proof business strategies and determine where your processes could go wrong.
This makes it easier to make long-term structural adjustments that optimise production.
Analytics can remove bottlenecks in operational productivity
It’s somewhat common to encounter bottlenecks when it comes to production. These bottlenecks could be a result of poor equipment efficiency because when equipment breaks down, it can halt the entire production line, driving up operating costs.
By using analytics in manufacturing, you can collect data from different sources, like ERP, the environment, and maintenance. This helps you determine when your equipment requires maintenance and prevent breakdowns during production.
Manufacturers can change input processes to discover new inputs
Advanced analytics in manufacturing can help organisations in the industry make unexpected discoveries through experimentation. For example, by using big data analytics in manufacturing, manufacturers can experiment with different inputs like carbon dioxide, temperature, and coolant pressure.
This calculated experimentation allows you to make unexpected discoveries in your industry.
Analytics makes product customisation easier to pull off
Big data analytics platforms in manufacturing can make product customisation easier to execute. Customisation in manufacturing is becoming a pivotal part of the industry. But, while it sounds awesome in theory, it is very difficult to pull off in practice. This is because making the shift from mass production to custom production is not possible without the right equipment.
Data analytics platforms make this a more practical feat. Big data analytics tools give manufacturers the insight they need to analyse the different factors that determine custom manufacturing, like customer preferences and production processes, making it much easier to customise manufacturing.
Removing complexities in the supply chain
In a global, connected environment, supply chains can be long and complex.
Analytics in manufacturing can help streamline the supply chain, reducing operating costs and optimising production. With this level of insight, it allows manufacturers to examine the ins and outs of the supply chain and pinpoint existing weaknesses.
Optimising production with big data analytics platforms in manufacturing
Big data analytics platforms in manufacturing help organisations maintain the right balance of production processes and variable inputs to optimise production and reduce operating expenses.
Manufacturing analytics can help optimise business processes either by reducing the variable inputs needed or by discovering new methods to improve production.
Big data analytics in manufacturing powering the industry
With the high rate of adoption of sensors and connected devices, there has been a massive increase in the data points generated in the manufacturing industry. These data points can be of various types. Data types range from a metric detailing the time taken for a material to pass through one process cycle to a more complex one, like calculating the material stress capability in the automotive industry.
With this surge in data available, there is no wonder why big data analytics in manufacturing is a hot topic.
Manufacturing remains a critically important part of the world’s economic engine, but the role it plays in advanced and developing economies has shifted dramatically. The manufacturing industry market was valued at $904.65 million in 2019 and is expected to reach $4.55 billion in 2025.
Big data is essential in achieving productivity, improving efficiency gains and uncovering new insights to drive innovation. With big data analytics in manufacturing, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production.
Leaders in manufacturing enterprises understand the importance of process – KRC research study found that 67 per cent of manufacturing executives planned to invest in data analytics, even in the face of pressure, to reduce costs in this volatile climate.
To understand big data analytics in manufacturing and its impact, let us dive into how it’s intervention helps streamline operations.
Optimising asset performance and operations
Since manufacturing profits rely heavily on maximising the value of assets, asset performance gains can lead to big productivity improvements. By the same token, a reduction in asset breakdown can reduce inefficiencies and prevent losses. For these reasons, manufacturers focus on maintenance and continuously optimise asset performance.
Machine logs contain data on asset performance. This data is potentially of great value to manufacturers, but many are overwhelmed by the sheer volume of incoming information. Data analytics can help them capture, cleanse and analyse machine data to reveal insights that can help them improve performance.
In addition to enabling historical data analysis, data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdown and avoid unexpected downtime. A study found that big data analytics can reduce breakdowns by up to 26 per cent and cut unscheduled downtime by nearly a quarter.
Improving production processes and supply chains
In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. Efforts to streamline processes and optimise supply chains must be supported by the ability to examine every process component and supply chain link in granular detail. Data analytics gives manufacturers this ability.
With the right analytics platform, manufacturers can zero in on every segment of the production process and examine supply chains in minute detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.
Making product customisation feasible
Traditionally, manufacturing focused on production-at-scale and left product customisation to enterprises serving the niche market. In the past, it didn’t make sense to customise because of the time and effort involved to appeal to a smaller group of customers.
Data analytics is changing that by making it possible to accurately predict the demand for customised products. By detecting changes in customer behaviour, data analytics can give manufacturers more lead time, providing the opportunity to produce customised products almost as efficiently as goods produced at a greater scale. Innovative capabilities include tools that allow product engineers to gather, analyse and visualise customer feedback in near-real time.
According to a Deloitte review of the rise of mass personalisation, the ability to postpone production gives manufacturers new flexibility that allows them to take on made-to-order requests. The ability to postpone production can reduce inventory levels and improve plant efficiency. A streamlined manufacturing process is not only beneficial – it gives manufacturers a way to maintain efficiency while customising manufactured goods.
Big data analytics in manufacturing and the way forward
Big data analytics in manufacturing presents many promising and differentiating opportunities and challenges.
According to a McKinsey report, worldwide consumption will nearly double to $64 trillion. In such a scenario, data analytics provide manufacturers with a huge opportunity to predict, innovate and implement their approaches.
For more information on how big data analytics in manufacturing is powering the industry, visit our website!
Big data analytics has helped improve the productivity of many industries such as the manufacturing industry. Your organisation can also enjoy the benefits of leveraging your data analytics with the Selerity analytics desktop.
Modernising factories with manufacturing data analytics
Innovation in the manufacturing industry has allowed facilities to become more software-oriented than ever before. Data collected during standard operations can identify inefficiencies even when they are relatively minute, allowing for adjustments that will make processes as streamlined as possible. Over time, these manufacturing data analytics can increase production rates, limit waste, and even save energy.
As the use of sophisticated sensors increases, allowing real-time data to be available for analysis, the capacity of big data in the sector may even become more powerful.
Read on to find out about how to transform your manufacturing processes with the help of manufacturing data analytics.
Increasing cost efficiency and ultimately fattening the bottom line
Purchasing is a standard part of most companies’ supply chains, but one that can easily be ignored when you’re too busy trying to improve upon other aspects. Starting off from a faulty supplier or one that is a few cents too expensive per component may not seem like the end of the world, but if you produce thousands of products a day, a cent here or there will turn into thousands of dollars on your ledgers.
Manufacturing data analytics can help you understand the cost and efficiency of every component in your production lifecycle, all the way from your suppliers’ trucks. Advanced manufacturing data analytics can help you reach better decisions by visualising how each aspect impacts the final result. If certain components are constantly failing or are not doing exactly what they need, powerful analytics will help you spot them before they become an issue.
Predictive maintenance with manufacturing analytics
Manufacturing systems are constantly operating under heavy loads and any stoppage in work can translate to spiralling losses. Few things are costlier to a manufacturer than downtime. In some industries, it can cost thousands of dollars per minute and millions of dollars per year. Now, with all of the various sensors and connected devices included in today’s advanced equipment, it’s possible for manufacturers to use algorithms to uncover complications before they arise and fix minor issues before they become costlier problems.
Predictive maintenance has the potential to save manufacturers millions of dollars over the course of a year, prolonging the life of equipment and ensuring efficient operations. Thanks to the growth and advancement of big data platforms, it’s becoming easier and more cost-effective to gather these insights.
Effective time management translates to more productivity
One of the biggest problems manufacturers run into is wasted time. While manufacturing chains can be built with efficiency in mind, different factors may play a contributing role in reducing the overall efficiency of the line because of poor installation, misuse, or simply a lack of downtime coordination.
By using sophisticated data analytics and platforms, companies can gain real-time insight into how well their manufacturing lines are operating, both on a micro and macro scale. Understanding how downtime for a single machine can affect the chain, or how different configurations may improve overall efficiency, isn’t just a pipe dream, it should be a necessity. Generating actionable data that lets you realise real improvements in the overall process is a major advantage of applying analytics to manufacturing.
Create better demand forecasts for products
Every manufacturer knows that they are not just making their products for someone today, but also for the perceived demands that will/may emerge in the near future. Demand forecasts matter because they guide a production chain and can be the difference between strong sales or a warehouse full of unpurchased inventory. For most companies, forecasts are based on previous years’ historic values, and not on more actionable forward-looking data.
However, manufacturers can combine existing data with predictive analytics to build a more precise projection of what purchasing trends will be.
Manage your warehouses better
Another overlooked aspect of the manufacturing process is storage. Once products are ready to be shipped, they must be placed in warehouses before leaving for their destination. At this point, seconds and minutes become important, especially in a world that is increasingly embracing ‘just-enough’ and zero-inventory models.
Managing warehouses is more than simply finding space for products to wait. Establishing efficient arrangement structures, better product flow management, and the most effective replenishment procedures can improve operations, as well as your bottom line. Advanced analytics make it easier to understand how to improve your inventory and manage your warehouses better.
Key takeaways
Bringing your manufacturing processes into the 21st century can be a straightforward process. By incorporating robust analytics and visualisation tools, you can build a more granular understanding of how your production line operates, and how you can streamline it further.
For more information on how you can bring your manufacturing processes into the 21st century, visit our site.
Experience the many benefits of a SAS pro analytics environment with our Selerity analytics desktops. With this cutting-edge software, you can improve your organisation’s data analytics exponentially.
Data analytics software for manufacturing companies
The manufacturing sector is an industry that is in a constant state of improvement and innovation. With industry leaders always exploring the latest technology in pursuit of greater efficiency and cost-effectiveness, the role of data analytics software for manufacturing companies has grown significantly over the years.
From effectively tracking a production cycle to inventory management to maintaining comprehensive stock insights, data analytics software for manufacturing companies are providing unprecedented insights and driving significant results. In the following sections, this blog explores a few of the benefits of data analytics software for manufacturing companies.
Taking care of the supply side of manufacturing
It’s interesting but something that happens frequently when we discuss the manufacturing sector is the emphasis we place on the final output a given manufacturer is expected to deliver. For example, if you’re a consumer electronics producer, the focus is always on the quality and quantity you’re able to produce in order to facilitate demand. However, how frequently do we place an emphasis on the supply side aspect of this manufacturing process – irrespective of the type of goods being produced?
While it is true that manufacturers would naturally consider the various supply factors that contribute to the whole process, there are many aspects of the supply side that aren’t necessarily focused on in the same way that other aspects of the supply chain are. For example, with limited data analytics, manufacturers will not be able to fully scope the extent to which a supplier is marginally more expensive or of less quality.
However, with a powerful data analytics platform in place like SAS software, you can rest assured knowing that every inch of your manufacturing process is covered. If there’s one component that is costing you a little more than it should or there’s one part that’s not performing as well, your data analytics will tell you precisely what it is.
Extend the lifespan of your equipment and machinery
One area where many manufacturers go wrong is assuming their machines and equipment, no matter how expensive or renowned they are, will never go bad and start to dwindle in performance. Unfortunately, as is the case with most things in life, all things falter at some point.
In the manufacturing space, most equipment maintenance practices take place on a fixed routine with little room for flexibility. A large part of this is due to the fact that most manufacturers rely on preset maintenance schedules provided by the equipment producers themselves. However, the reality is that the lifespan of a given piece of equipment is dependent on basic wear and tear – especially how extensively it is used. With a powerful data analytics platform in play and with the expertise of a team of SAS consultants, manufacturers can set up their equipment insights well in advance and know precisely when a machine or piece of equipment is going to need servicing/maintenance. That’s how far data analytics software for manufacturing companies has evolved.
Navigate the unforeseen waters of external risk
Manufacturing is one of those industries that are highly dependent on external factors like weather and resource availability. A shortage or dramatic effect on one of these fronts and many manufacturers could be looking at years’ worth of losses. This is precisely where the power of data analytics software for manufacturing companies comes into play.
With predictive analytics emerging and growing at such a rate, modern analytics platforms like SAS now have the capacity to forecast such instances well in advance and allow manufacturers the unprecedented luxury of making adjustments accordingly. From tracking adverse weather patterns to monitoring market conditions and prospective resource shortages, manufacturers can leverage powerful data analytics to make decisions that not only drive value, but avert potential disasters as well.
The value of data analytics software for manufacturing companies is all about demand
While I started this article with an emphasis on the supply side of manufacturing, the reality is that ultimately what matters is demand. All manufacturing operations take place to facilitate and support the machine that is consumer demand. If insights and analytics are not up to par, you can be certain that the result of this will be an inefficient and uninformed manufacturing cycle.
Keeping in mind that manufacturing is not just about meeting today’s demands, but the demands for the days, weeks, and months to come is important for all manufacturers. Unfortunately, without the right data analytics, this is incredibly difficult to do. With platforms like SAS, demand forecasts become the norm, allowing manufacturers the insights necessary to guide a production chain and be exponentially more effective in driving and delivering sales. For most companies, having access to these insights is the difference between ensuring strong sales or having an entire warehouse full of unpurchased merchandise.
Remember, the right analytics platforms have the capacity to leverage insights from the past, merge them with existing circumstances, and drive value by predicting future results and demand.
If you would like to know more about our role in the data analytics space, our SAS services, our company and our work with SAS, in addition to how you can leverage our SAS administration, installation, and hosting services, feel free to reach out to us, or stay tuned to this feed.
How does data analytics improve risk management for businesses?
Businesses face all kinds of threats and risks which can affect their performance.
Some of these threats may be inevitable but businesses can identify these threats and fortify themselves against them through risk management.
The biggest threats an Australian business could face are from cybercrimes, a shift in the market and regulatory changes.
Risk management involves identifying and evaluating potential risks to the business and formulating strategies to utilise their resources to handle these risks. With a good risk management system in place, a business can increase its survivability.
To create a proper risk management system, businesses need data and lots of it; using this information to create a comprehensive strategy requires data analytics.
This blog post will highlight the ways data analytics can improve risk management for businesses.
It can help identify and reduce customer churn
Customer churn is a major problem for businesses; it refers to the rate at which customers eventually stop interacting with them.
Customer retention is vital for any business and every year a customer stays in a company, the more profits they’ll generate. It’s estimated that for financial businesses, even a 5% increase in customer retention can generate up to 25% in revenue.
There are many reasons why a customer would want to stop doing business with a company and businesses need to identify these potential reasons and create a risk strategy to keep customers with the company longer.
By analysing big data and predictive analytics, businesses can study historic data to look for potential causes of customer churn.
Analysing data regarding consumer behaviour, customer demographics and trends will help businesses find patterns between these sets of data, giving them insight into why customers tend to defect.
It can reduce operational risk in manufacturing
For businesses in the manufacturing industry, the flow of operations is vital. The biggest issue with managing operational risk is the huge amount of data required to identify the risks.
Fortunately, modern data analytics tools can easily find meaningful patterns in this data very quickly.
The quality of the materials used, production time, cost and the reliability of the suppliers all contribute to how efficiently the manufacturing process will go.
Data about the machines being used in the production process can be analysed to assess their reliability. Data about how many hours the machines are used every day, their schedules, and their maintenance times can provide insight into how these machines can be used efficiently and effectively, avoiding any breakdowns.
Analysing worker data will also help in developing an operational risk management strategy.
By analysing the working hours of workers, their productivity, the number of accidents that happen on the factory floor, manufacturing techniques and current workers benefits, businesses are able to reduce employee turnover through effective employee risk strategies using the data gathered.
It improves risk management for working capital
A business’s working capital is a measure of a company’s efficiency and shows how stable the business is. The company’s many assets, like its building, equipment and inventory, all contribute to its working capital.
Risk management strategies need to be devised in order to make efficient use of the company’s working capital. To do this, data analytics can be used to identify the current efficiency of the business’s assets and compare them to its current liabilities.
Through data analysis, businesses can identify weaknesses in their current assets and the kinds of risks their liabilities impose on them. With this information, businesses will be able to use their assets more efficiently.
Data analytics also allows a business to predict how their current liabilities, like taxes, creditors and overhead expenses, will change over time or in reaction to other factors like laws and regulations. This will help the business plan out risk management strategies to handle these liabilities while keeping their working capital efficient.
Strengthen your risk management with data analytics
With the help of data analytics, you can create a comprehensive risk management strategy to protect your business from many potential threats.
Using large amounts of data to help create a risk management plan may have been difficult in the past but with today’s efficient data analytics software, analysing relevant data for useful insights has never been easier.
Can data analytics improve road safety?
The invention of the motor car was a major leap forward in human history; since then, it has propelled mankind to greater heights in the last 200 years.
Today, we enjoy a plethora of transport choices that help us get from point A to point B faster than ever before.
That said, like all good things, motorised transport also comes with a certain level of danger, and that is the increased risk of accidents.
Although road accidents predate the invention of motorised vehicles, the advent of faster, more powerful modes of transport has increased its risks significantly in recent years.
The first recorded motor accident in history, as it happens, was in 1891 in Ohio, USA—five years after the invention of the car—and the first death caused by a motor accident in 1899. Since then, countless people have experienced motor accidents in various degrees of severity.
According to annual global road crash statistics, 1.35 million people lose their lives in road accidents every year—an average of 3,700 deaths every day. A further 20-50 million also suffer non-fatal injuries due to road accidents.
In the last few decades, these trends have pushed governments across the world to put their best foot forward to increase road safety, and that resulted in significant decreases in the number of accidents.
In recent years, however, road accidents have, once more, seen a gradual increase.
This raises important questions about the effectiveness of traditional road safety measures, creating a real need for more advanced technologies like data analytics to improve road safety.
Data analytics can help us identify accident hotspots
A detailed look into road accident data will reveal that most accidents occur in certain hotspots—places that have the highest concentration of road accidents.
With data analytics, road safety authorities can identify these hotspots and the reason for the high concentration of accidents in these locations. It also allows them to implement safety measures like building roundabouts, which have proven effective in reducing injury crashes by up to 75%.
Moreover, with predictive analytics and historical accident data, authorities can engage in proactive accident prevention. This can also be useful when building new roads or repairing old roads, wherein authorities can incorporate features that improve road safety.
Increased traffic is another reason for the high number of accidents in major urban cities. With data analytics, urban authorities can manage traffic during peak hours better and reduce the likelihood of fatal or injurious encounters.
Data analytics supports driver safety features in modern vehicles
It’s no secret that many road accidents today occur due to distractions while driving.
According to recent statistics, texting and talking on the phone while driving are some of the leading causes of driver distraction. Fortunately, with advanced analytics and Artificial Intelligence, modern automobiles can detect these distractions and alert drivers about them—reducing the risk of accidents in the process.
Many modern vehicles also have in-built sleep detection systems that ensure drivers are not fatigued while driving—another leading cause of road accidents.
Moreover, data analytics powers many driver-assist features like traction control, cruise control, electronic brakes, and automatic lane changing systems to reduce the risk of accidents.
Automobile manufacturers also use data analytics and data modelling to identify limitations in their safety features and build safer vehicles that minimise damage in the event of an accident.
For example, modern vehicles can detect potential accident scenarios using advanced algorithms and can send signals to the airbag inflation system before the accident occurs. This reduces the latency in airbag deployment, reducing the risk of fatal injuries.
Data analytics can help us take a more up-to-date approach to improve road safety
Although motorised road transportation is one of the greatest innovations in human history, it has resulted in a significant increase in road accidents in recent years.
The good news is that with powerful data analytics, we can take a modern approach to a modern problem and save countless lives in the process.
Understanding IoT edge analytics and its uses in the real world
Since the advent of the internet, the world has changed drastically. The World Wide Web has democratised information and made the entire world as accessible as one republic. Albeit one governed by the rules of internet companies instead of a government.
Today, most of our interactions are reliant on the internet, at least to a certain extent. Everything from communication, entertainment, and security to payments. The impact of the World Wide Web is undeniable.
While the internet and the services that are based on it have taken much of the limelight, a key piece of this environment often gets overlooked; the IoT device network.
In recent years, IoT devices have exploded in popularity. A recent study found that 55 billion IoT devices are expected to power our households and businesses. IoT devices take the form of smart light bulbs to complex sensors powering planetary research rovers.
Many believe that IoT devices are the driving force behind the internet. Without these devices, the internet as we know it may not exist. These devices power the products and services we love by collecting and transmitting information to companies who operate them.
That said, the amount of data collected by these devices has created a bottleneck in data analytics. This is because analytics platforms are not powerful enough to analyse and produce insights in real life.
This has led to the development of IoT edge analytics—an analytics framework that eliminates bottlenecks by moving data analytics closer to the IoT devices.
In this post, we explore what IoT edge analytics is and its real-world applications.
What is the role of data analytics in IoT applications?
IoT devices act as the data collection tools where they are deployed. These devices stream data to centralised data processing systems which clean, process, and analyse these data sets to produce actionable insights.
Using these insights, IoT devices can help real-time decision making in IoT applications. Edge analytics is the next step in data analytics in IoT applications.
How is big data analytics important for IoT systems?
Without big data analytics systems, it would be impossible for organisations to derive any meaningful value from their IoT systems.
IoT needs analytics platforms because they generate a large volume of data. In fact, by 2025 IoT devices will be generating 79.4 zettabytes of data in real-time. The immense volume of data generated makes it hard to analyse the data efficiently.
Big data analytics platforms can analyse data on such a large scale because they feature machine learning that can extract useful insights from the data in real-time and reframe the insights in a framework relevant to the organisation’s goals.
However, not every analytics platform is suited for IoT systems. The analytics platform must have the right infrastructure and performance capabilities to analyse IoT data in real-time. This is where IoT edge analytics systems become essential.
What is IoT edge analytics?
IoT edge analytics refers to a data analytics framework where analytics capabilities are moved to, or closer to, the devices that make up the fringes or edges of the data analytics pipelines.
Traditionally, data analytics involved collecting data from various sources and migrating it to a centralised data lake or data warehouse to initiate the data analytics process. This, however, has proved to be inefficient as the amount of data collected in the current environment far outweighs the analytics capabilities; which means businesses incur increasing costs for data transmission and storage.
Edge analytics eliminates this issue by conducting data analytics closer to the data source and only transmitting the final results to the warehouse, reducing data transmitting and data storage costs.
This also allows for real-time analytics, which has paved the way for some clever implementations in the real world.
Edge analytics in the real world
Transportation
The transportation industry is one of the pioneers in IoT edge computing adoption. Technologies like auto parking, lane correction, and automated driving found in modern cars are powered by IoT analytics. Sensors in these cars collect and send information to the computer unit, which analyses the data and sends instructions to these driver-assist systems.
NASA also used edge analytics to execute the landing of the Perseverance Rover. Without edge analytics, NASA would not have landed the rover due to data transmission latency from Mars to Earth.
Manufacturing
Quick decision making and tight tolerances are key to an efficient manufacturing function. By nature, manufacturing needs continuous monitoring to spot errors and optimise the production process.
That said, achieving this level of production efficiency is a hard task using human resources or traditional analytics methods. Edge analytics can streamline this process using IoT sensors to power error identification systems and production optimisation processes.
Machine learning
One of the more widespread uses of IoT analytics, machine learning, uses on-device processing to perform complex tasks without connecting to the cloud or the internet.
Your smartphone, for example, uses a neural processing unit to power smart assistants like Siri or Google Assistant to understand your voice commands and execute them.
IoT edge analytics is the secret weapon for the modern experience
In the current context, IoT devices help us simplify data analytics by eliminating the bottlenecks of traditional methods. It’s fair to say IoT edge analytics is the future of data analytics, and the internet experience as a whole.
How SAS data preparation can optimise your data analytics
Do you know which process manufacturing lines have in common?
Manufacturing lines require high-quality material that’s ready to be used in production. The process they have in common, then, is preparing raw material to make it suitable for value-added production.
This analogy applies to data analytics as well.
Imagine data analytics platforms as manufacturing lines. In this case, the raw material would be the high-quality data required to churn out the right products, which, for us, is actionable analytics.
Raw data can’t be ingested directly on the analytics platform, however, as it may contain errors and discrepancies.
Raw data needs to be cleaned and enriched in a process known as data preparation. According to Forbes, data scientists spend more than 76% of their time preparing and organising data using a variety of tools.
While there are many data preparation solutions out there, the SAS data preparation platform remains one of the most popular options.
In this post, we explore how SAS’s data preparation platform can optimise data analytics platforms to produce high-quality, actionable insights.
SAS data preparation increases your efficiency
Modern businesses collect vast amounts of data every single day. According to recent findings, organisations now collect more data in a single month than they did in the entire decade between 2002-2012.
This data needs to be enriched using data preparation techniques such as wrangling, cleaning, correlation and formatting. Without these processes, analytics platforms can’t produce accurate and actionable insights as the raw data can generate errors and discrepancies in the process.
That said, traditional data preparation techniques can only process less than 10% of the collected data, which means 90% of the raw data is either wasted or takes an unnecessarily long time to be processed, hampering efficiency and output.
SAS data preparation, however, can generate codes to handle cleaning, wrangling, correlation and formatting automatically, eliminating these challenges from the get-go.
Data preparation can increase the accuracy of analytics platforms
As we’ve already established, raw data can create errors and inaccuracies in the data analytics process if ingested directly.
That said, data prepared through traditional techniques can also create errors in the code, leading to inaccurate insights since traditional methods rely on manual processing, which not only requires a long time to complete but is also subject to human errors.
Modern data preparation tools rely on computer-generated codes that are free of this issue. These codes are designed to better identify anomalies, null values and duplicate entries compared to older techniques.
Data preparation supports a migration to cloud services
In recent years, more and more organisations are migrating their assets, processes and analytics infrastructure to the cloud to facilitate a collaborative work environment.
Unfortunately, integrating raw data into the cloud analytics platform is easier said than done, as organisations still need human resources to integrate this data into the cloud analytics environment.
SAS data preparation, however, is designed to be self-serviceable, meaning you don’t need a team of data scientists to carry out this process!
With this platform, organisations can clean, wrangle, structure, and format data from data lakes and enter it into cloud data analytics deployments without much human intervention.
Make the most of SAS data preparation tools to optimise your analytics platform
Data analytics platforms are great at arming organisations with the insights they need to make strategic, far-reaching decisions.
That said, analytics platforms are somewhat like manufacturing lines. They can’t produce high-quality insights without data that has been prepared to be ingested.
Fortunately, with SAS data preparation, organisations can enrich vast amounts of data without too much human intervention and feed it into analytics platforms.
Optimise your data analytics deployment with the right data preparation tools today.
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