How SAS managed services can help businesses leverage the power of data analytics

What do mega-corporations like Apple and YouTubers have in common?

Well, both parties use data analytics to make business decisions.

Whether it’s marketing or product development decisions by the $2-Trillion Apple Inc or decisions on content ideas by YouTubers with a few hundred thousand subscribers, data analytics plays a critical part in the decision-making process.

More and more companies are jumping on the analytics bandwagon after realising the benefits offered by data analytics.

Data analytics platforms facilitate this shift to data-backed decisions by offering analytics services that span a large spectrum of real-world applications. According to Statista, analytics platforms facilitate the use of data in market monitoring, manufacturing and much more.

While there are many data analytics platforms, none are more popular than SAS, the data analytics industry leader. Today, organisations both big and small use SAS’ data analytics platform to obtain actionable insights.

Businesses, however, require SAS managed services such as installation, administration and hosting services to leverage the full potential of their analytics. In this post, let’s explore how SAS managed services help organisations optimise their analytics deployment.

SAS installation services

Not all organisations are created equal. The needs and requirements related to data analytics deployment differ, accordingly, across each organisation.

Certain companies may need to deploy their analytics platform on a single server setup. Others might require more expansive deployment setups spanning multiple servers both locally and on the cloud.

A standard SAS installation may not meet your team’s specific requirements. SAS installation services provided by managed service providers, on the other hand, help you meet these varying requirements. These types of Installation services also help you get the best return on your SAS analytics investment by customising your deployment according to your specific requirements.

SAS installation services also give you the benefit of leveraging the services of SAS-qualified industry experts who work with you to meet your analytics needs.

SAS admin services

The effective deployment of a SAS business solution, alone, won’t help you leverage the full power of your SAS analytics platform.

That’s because it needs to be maintained and tested constantly to weed out any issues that may arise as part of your deployment. The resolution of these issues, however, requires extensive knowledge not only of your general SAS platform but your customised deployment as well.

While bigger organisations employ in-house SAS administrations to resolve these issues as they arise, that is not a luxury every organisation can afford. Most entities either don’t have the financial resources or the human resources for a dedicated SAS administrator.

Dedicated SAS admin services eliminate the hassle of managing and maintaining the platform and give you more time to extract actionable insights from your deployment.

SAS admin services act as your point of liaison with SAS, making the update, maintenance, troubleshooting and fixing of your SAS deployment easier and cost-effective.

SAS hosting services

One of the most critical issues facing organisations is the scalability of their analytics infrastructure. Traditional organisations rely on on-site servers to deploy SAS analytics solutions, making it harder to scale as analytics requirements become more complex and expansive.

That’s why many modern organisations are migrating their analytics deployment to cloud-based servers. Migrating your SAS deployment by yourself, though, is not necessarily straightforward as you have to figure out certain prerequisites before moving to the cloud.

With SAS hosting services, you’re free of dealing with technical requirements because SAS experts take care of everything from migrating your data to cloud servers to configuring AWS to meet your requirements.

Leverage SAS managed services to optimise your SAS deployment

SAS has become the leading name in the data analytics industry with its business solutions, which cater to various applications ranging from historical data analytics to preventive analytics.

Leverage the power of SAS with the support of managed services providers who take care of the installation, hosting and administration of your analytics deployment. Make use of these services to optimise your analytics platform today.

How SAS analytics for IoT is helping us make smarter devices

What do you think the future on earth looks like?

We all think of a future where humans and machines interact seamlessly. Our idea of the perfect future is influenced by how it looks in movies and TV shows.

Most of these fictional future worlds have one thing in common: super-smart devices.

Whether it’s the ultra-futuristic world of Blade Runner 2019 or the toned-down future of Black Mirror episodes, all of them feature smart devices that interact with the characters.

These fictional worlds, however, are not all that rooted in fiction. Some of this technology already exists in the present world, albeit with limited functionality and capabilities. These devices are called IoT devices—interconnected devices equipped with sensors and cameras that allow them to communicate with neighbouring devices over the internet.

Businesses, though, are not resting on their haunches. They are trying to create smart devices with greater functionality and capabilities, just like in the movies. Data analytics tools like SAS analytics for IoT are helping engineers create these uber-smart devices. 

SAS analytics for IoT is embedding AI in smart home devices

Early IoT devices had simple functions and limited capabilities due to the lack of data needed to improve their intelligence. As a result, they were not smart—only a little less dumb.

Early smart doorbells, for example, only relayed a video feed of the front door to occupants inside the house. While this is undoubtedly useful, it was not very smart.

Modern smart doorbells not only relay a video feed to occupants but also identify the person at the front door. In addition, they notify occupants about the arrival of guests, all through the power of analytics.

This identification function, in particular, uses cameras, AI and data analytics to identify different people. Cameras capture the image and data analytics helps identify the individual by comparing the image to information in a database.

Analytics tools like SAS analytics for IoT have been critical in improving home assistants powered by the likes of Google Nest Home and Amazon Echo.

These digital home assistants use AI-powered data analytics to study our behaviour through various sensors located. Studying our behavioural patterns helps these devices automate actions like setting the temperature through a thermostat or switching lights on and off.

Analytics helps us build smart manufacturing lines

IoT devices are not only making a splash in our homes but also in our factories and commercial spaces. Many organisations are now using IoT devices in their manufacturing lines, which previously required manual labour.

Early manufacturing equipment relied on human knowledge about each machine for maintenance. Unless there were trained machine operators on-site, these expensive machines were rendered useless when they broke down.

Modern manufacturing lines include IoT devices so that maintenance and troubleshooting become easier. Modern manufacturing equipment includes sensors and cameras that not only monitor the production process but also monitor the optimal operation of these machines.

These sensors stream large amounts of data to servers to deliver visual analytics insights about the manufacturing process and components inside the machines. With these insights from IoT devices, employees are able to detect faults in the machines and fix them, reducing downtime across the manufacturing process. 

Modern devices not only share data but also use AI to pinpoint faults, eliminating the hassle of troubleshooting for employees.

In the future, data analytics tools for IoT devices may help us build intelligent manufacturing lines that fix themselves, making production seamless.

Data analytics for IoT devices is making the world a smarter place

All of us envision a future powered by smart technology that automates our mundane tasks.

Thanks to data analytics for IoT, this reality is not too far away. The IoT analytics challenges that prevented widespread adoption is fast becoming a thing of the past as more and more smart devices are used across industries.

Soon, we might not need special effects to create the fantastical scenarios we see in movies.

How data analytics is improving the automotive industry

Ever since Karl Benz invented the first car in 1886, the automotive industry has seen a significant boom in business, to say the least. 

From the first car to the introduction of the Ford Model T, the first mass-market car, to the introduction of the Tesla Roadster, the first all-electric sports car, the growth of the automotive industry has been astronomical.

In the last 140 years, the industry has improved significantly with contributions from visionaries like Gottlieb Daimler, Ferdinand Porsche, Ferruccio Lamborghini, Enzo Ferrari, Kiichiro Toyoda, Henry Ford, and most recently, Elon Musk.

Vehicles have now become very intertwined with our lifestyle and culture.

Along the way, the industry has changed how it has operated. While it once relied heavily on human intervention and intelligence, it now relies on more technology-backed tools to make decisions on design, manufacturing, marketing and sales.

One such widely-used tool is data analytics.

Data analytics is improving automotive design

Do you remember the cars of the late 1980s and the early 1990s? 

This period is widely considered to be the dark age of automobile design, because of the soap box-like design of the vehicles from that era.

Modern automobiles, however, have ditched these boxy designs in favour of flowing lines and aggressive body panels.

These elements not only improve the look of the vehicle but also improve key performance metrics like fuel efficiency (battery efficiency in electric vehicles), drag, braking performance and speed. 

Fuel efficiency or battery efficiency, for example, relies heavily on how much drag the vehicle experiences. The higher the drag, the lower the efficiency. 

The flowing design of modern vehicles also improves their aerodynamics, reducing drag, which results in better fuel efficiency.

These design improvements are thanks to data analytics, which helps engineers and designers gain insight into how their designs are impacting the overall performance of the vehicle.

Data analytics can also help engineers and designers create 3D models of the vehicle and run computer simulations based on real-world conditions to measure these metrics.

By leveraging the power of data analytics in the design process, manufacturers can reduce research and development costs and prototyping costs to a significant extent.

Data analytics is optimising production lines

Early automotive production relied heavily on manual labour. Even today, certain artisan automotive manufactures use a very high percentage of manual labour in their production lines. Manual labour is slow and inefficient, however.

In response to this, mass markets brands like Toyota and Volkswagen have shifted to a mostly automated production line with minimal human intervention, which is fast, reliable and more efficient than manual production lines.

Volkswagen, for example, produced close to nine million vehicles in 2020 thanks to their optimised production line.

These production lines depend on data analytics to function efficiently and smoothly. Sensors and cameras placed in the production line collect a vast amount of data, which is then processed by data analytics tools and fed into the pieces of equipment that run the production lines. 

Without data analytics, the equipment that supports automated production will become obsolete.

Analytics is helping us reduce accidents and fatalities

According to recent statistics, fatalities as a result of automotive accidents have decreased from 3,798 in 1970 to 1,195 in 2019 in Australia alone.

The decrease in fatalities is largely thanks to better road safety regulations and better safety standards implemented in modern automobiles.

Automotive manufactures are now including more than 50 sensors in their vehicles to collect data from the vehicle and its surroundings, which can then be analysed and used in lifesaving technologies like collision detection, airbag deployment, and driver eye-tracking.

Without big data analytics, this kind of technology would not be as effective as it is now.

Data analytics is reshaping the automotive industry

Automobiles are a vital part of modern society, given their role in supporting large-scale, life-changing mobility. The industry has constantly made improvements to make transportation better, and data analytics has helped them make these advancements.

With data analytics, automakers are creating faster, safer, and more reliable vehicles. This will only get better in the future.

How data analytics supported the development and distribution of COVID-19 vaccines

COVID-19 is the biggest public health crisis the world has seen since the Spanish Flu, which infected one-third of the world’s population in the aftermath of World War I, and killed more than 50 million people.

Even now, more than 18 months after the first COVID-19 patient was detected, governments, hospitals and other healthcare institutes are still struggling to contain the spread of the pandemic.

Fortunately for us, there is one distinctive difference between the Spanish Flu and COVID-19—the pace at which pharmaceutical companies have been able to develop vaccines. 

Another contributing factor to the success and speed of the COVID-19 vaccine development was data analytics in healthcare. 

Thanks to these advancements, which have reduced the typical vaccine development timeline of 10 years, pharmaceutical companies have been able to explore, develop, trial and receive approval for multiple vaccines within two years since the outbreak of the virus. 

It has also helped government authorities in developing and implementing successful vaccine rollout plans to inoculate as many people as possible.

In this post, we take a deeper dive into the role data analytics played in the development, manufacturing and distribution of COVID-19 vaccines.

Data analytics improved the efficiency of preclinical and clinical experiments

The development of a vaccine for any disease requires pharmaceutical companies to conduct rigorous experiments and clinical trials involving a large number of participants, to prove the efficacy of a vaccine against a virus.

The typical vaccine development process takes a minimum of 10 years before the vaccine gets approved by regulatory authorities in charge of widespread manufacturing and distribution.

The need of the hour, however, forced the pharmaceutical industry to adopt novel techniques in both analysing the data at hand and developing vaccines and experiments to speed up the development process.

One such data analytic tool used in the development of the COVID-19 vaccine is Design Of Experiments (DOE), which improved the efficiency of preclinical and clinical experiments while reducing the number of experiments otherwise required.

DOE ultimately helped companies design and approach experiments systematically, allowing them to identify and determine the effects various factors had on the outcome of clinical experiments.

Predictive technology helped companies scale vaccine production up

The success of vaccine development also relies on how fast pharmaceutical companies can manufacture the doses required to meet demand. Scaling the manufacturing process up to meet the needs of billions of people in a very short timeframe, however, is entirely unprecedented.

Fortunately, data analytics tools like predictive analytics and multivariate analytics helped companies not only predict demand to scale production up but also to reduce the number of batches needed to prove the efficacy of the vaccine.

Data analytics supported the management of the fluctuations in vaccination supply

Vaccine distribution is perhaps the area where data analytics is most traditionally used. This time around, data science was used to predict and handle fluctuations in vaccine supply due to various political, economical and logistical factors. 

Government authorities planned their vaccine rollout strategy to prioritise the most vulnerable locations and population segments. Many countries, for example, prioritised vaccinating everyone over the age of 65 as that segment of the population was deemed the most vulnerable to the virus.

Governments also used data analytics tools to detect virus transmission patterns to predict possible outbreaks and prevent them by vaccinating the people living around that geographical area.

COVID-19 vaccinations and beyond; data analytics will continue to shape public healthcare

COVID-19 is likely to be the most impactful healthcare crisis of our lives. Fortunately, things may return to the new normal sooner than we anticipate with the introduction of life-saving vaccines.

The development of these vaccines involved the adoption of many novel techniques including new vaccine production methods and data analytics to support this.

It also helped pharmaceutical companies and governments in developing, manufacturing and distributing these vaccines to protect the health of millions of people around the world. 

As such, data analytics will continue to be a force for good as it helps us navigate the uncertainties of public health crises—today and in the future.

Discovering the connection between Industry 4.0 and big data analysis

You may have heard the buzzword “Industry 4.0” in tech circles. How it promises to change our economy and restructure the way we live, work, and play. But what is the connection between Industry 4.0 and big data analysis? Big data analytics has a huge role to play in the development of smart technology. Let us explore the connection between Industry 4.0 and big data analysis in more detail.

Big data analytics and Industry 4.0

Analysing data streaming through these devices

When we think of Industry 4.0, we think of IoT, smart sensors, cloud computing, and more sophisticated technology. Big data analysis via analytics platforms play a huge role in the effective use of these technologies.

Technology from Industry 4.0 generates a lot of data, which needs to be analysed to gain value. Analytics platforms can conduct real-time big data analysis on data streaming from Industry 4.0 devices. Without big data analytics, it would be impossible to make sense of the data streaming through these devices.

Incorporate devices better into the production process

Self-service data analytics platforms are becoming more prevalent throughout different industries, and it is having an impact on the way we interact with Industry 4.0 technology. For example, the data generated can be consolidated into bulk with data analytics platforms. Self-service analytics systems can break down data in real-time to find patterns, faults, and visualise findings.

With the ability to analyse data in real-time, organisations can overcome one of the biggest problems related to Industry 4.0 devices: Extracting value from data. Previously, it would have been difficult to utilise Industry 4.0 devices to their full potential because it would have been impossible to collect and analyse data in real-time. However, thanks to the discovery of analytics platforms, the raw data can provide valuable information.

Data derived from Industry 4.0 can generate tremendous value. For example, in the manufacturing industry, big data from machines can yield operating data, process quality, logistics information, and records of manual operations. The information can optimise production processes and make significant gains in operational efficiency.

Organisations can expand production processes

By incorporating big data analysis into the production process, organisations can make better use of Industry 4.0 devices. When that happens, it opens up organisations to new production techniques that were not there before. An excellent example is predictive maintenance. Big data analysis can analyse data in real-time to accelerate the rate of discovery.

Predictive maintenance is an effective cost-saving tool, with most organisations estimating that they can save over $100 million from pre-planned maintenance, compared to organisations that don’t. The use of advanced analytics, big data, and Industry 4.0 devices also pave the way for more advanced production processes, like automation.

Actuators and robots can play a huge role in optimising the production process. However, for them to work effectively, the machines need to be connected to software that can pick up data, interpret it, and in return, feed information back into the system.

Big data analysis allows for the exchange of data between robotics and software, which allows organisations to maximise returns on robotics and make it easier to generate an even higher ROI from the use of robotics and other software designed to automate certain production processes.

Overcoming device shortcomings

One of the biggest problems associated with IoT devices or Industry 4.0 is that they are operating within a diverse ecosystem that generates information in different formats. Given this problem, it is difficult to get devices to communicate with each other properly. However, by investing in platforms for big data analysis, these organisations would have a much easier time carrying out proper analysis, creating greater synergy within the ecosystem, and making it easier to generate sufficient ROI.

Entering a new era with big data analytics

The next decade is going to see significant changes in the way we live and work. As Industry 4.0 advances and becomes a significant part of business operations, we need to adopt the right technology that would allow us to maximise ROI on our analytics platforms. Technology that is conducive to big data analysis will allow organisations to maximise ROI on their analytics platforms.

Data science modelling techniques for organisations

Everyday, 2.5 quintillion bytes of data are generated. With so much information at our disposal, it is becoming increasingly important for organisations and enterprises to access and analyse relevant data to predict outcomes and improve services.

However, arbitrarily organising data into random structures and relationships is not enough. In order to access the data properly and extract the most out of it, it is essential to model your data correctly.

The Big Data revolution has arguably provided a more powerful information foundation than any previous digital advancement. We can now measure and manage large volumes of information with remarkable precision. This evolutionary step allows organisations to target and provide more finely-tuned solutions and use data in areas historically reserved for the “gut and intuition” decision-making process.

Data science modelling techniques play a crucial role in the growth of any organisation that understands the importance of data-driven decisions for their success. Having your data in the right format ensures that you can get answers to your business questions easily and quickly.

What is data modelling?

In simple terms, data modelling is nothing but a process through which data is stored structurally in a specific format. Data modelling is important because it enables organisations to make data-driven decisions and meet varied business goals.

Typically, a data model can be thought of as a flowchart that illustrates the relationship between data. It enables stakeholders to identify errors and make changes before any programming code has been written. Alternatively, they can be introduced as part of reverse engineering efforts to extract other data models from existing systems.

Importance of data science modelling techniques

Data modelling represents the data properly in a model. It rules out any chances of data redundancy and omission, helping analysis and processing. Furthermore, data modelling improves data quality and enables concerned stakeholders to make data-driven decisions. This clear representation makes it easier to analyse data properly. It provides a quick overview of the data, which can then be used by the developers in different applications.

Since a lot of business processes depend on successful data modelling, it is necessary to adopt the right modelling techniques to get the best results.

Types of data models

There are three types of data modelling techniques for business intelligence: Conceptual, logical, and physical.

Conceptual data modelling examines business operations to create a model with the most important parts (such as describing a store’s order system). Essentially, this data model defines what data the system will contain.

Logical data modelling examines business functions (like manufacturing and shipping) intending to create a model describing how each operation works within the whole company. It also defines how a system should be implemented: By mapping out technical rules and data structures.

Physical data modelling examines how the database will actually be implemented, intending to model how the databases, applications, and features will interact with each other. Here, the actual database is created while the schema structure is developed, refined, and tested. Data models generated should support key business operations.

Drive key business decisions using data science modelling techniques

Clearness: How easy it is to understand the data model just by looking at it.

Flexibility/scalability: The ability of the model to evolve without making a significant impact on code.

Performance: You can attribute performance benefits based on how you model the data.

Productivity: An organisation’s model needs to be easy to work with.

Traceability: The ability to manoeuvre through historical data.

The data model of every application is the heart of it

In the end, it is all about data: Data comes flooding in from everywhere, data is processed following business rules, and finally, data is presented to the user (or external applications) in a convenient way.

With new possibilities to easily access and analyse their data to improve performance, data modelling is morphing too. More than arbitrarily organising data structures and relationships, data modelling must connect with end-user requirements and questions, as well as offer guidance to help ensure the right data is being used in the right way for the right results.

Business performance, in terms of profitability, productivity, efficiency and customer satisfaction can benefit from data modelling that helps users quickly get answers to their business questions.

For more information on data science modelling techniques, visit our website!

Edge analytics refining our data approach

Edge analytics is the new hot buzzword. With industries looking for any chance to optimise their systems for incremental improvements, any analytics platform that can refine the data analysis process is a huge boon and is bound to garner a lot of attention. The edge analytics market is expected to grow from $1.94 billion in 2016 to $7.96 billion in 2021, with a growth rate of 32.6 per cent.

With edge analytics generating this much interest and traction, I think we need to delve behind the curtains of this new entree and figure out what exactly is going on in there!

What exactly is Edge analytics?

According to Gartner’s report, edge analytics is the method that will enable users to leverage data analytics to go beyond conventional business insights and increase operating efficiency. The analytics platform aims to accomplish this by zooming into the smallest detail with precision to make analysis more accurate and relevant.

First, let us break down what this new buzzword even means. Edge analytics refers to the approach of capturing, monitoring, and analysing the data from edge network devices such as sensors, routers, gateways and switches. The analytical computation is done at the edge of these devices in real-time, without waiting for the data to be sent to a centralised storage system, then the system computes the analytical applications and sends commands back.

Edge analytics is an innovative addition to data collection and analysis because it reduces decision-making latency on connected devices, improves the rate of data processing and increases deployment scalability and effectiveness.

How does it help?

According to the International Data Corporation, the growing number of IoT devices will increase the amount of data available to 79.4ZB by 2025. This results in a massive accumulation of unmanageable data, 73% of which will not be used.

Edge analytics is believed to address these problems by running the data through an analytics algorithm as it’s created, at the edge of a corporate network. This allows organisations to set parameters on what information is worth sending to a cloud or an on-premise data store — and what data offers little value.

With edge analytics, we will see better data security due to decentralisation. Having devices on the edge gives absolute control over the IP protecting data transmission. It also ensures that applications are not disrupted in case of limited network connectivity. Furthermore, your expenses are driven down with edge analytics minimising bandwidth, scaling operations and reducing latency of critical decisions.

Without the need for centralised data analytics, organisations can identify signs of failure faster and take action before any bottleneck can arise within the system.

Where does edge analytics fit in?

The edge analytics model enables users to generate valuable and actionable insights in real-time, bringing order to unstructured content and feeding relevant data to cognitive-oriented systems.

Edge analytics is in demand and its features could be leveraged by most industries to supercharge their operations. For example, remote monitoring, maintenance and smart surveillance could be utilised for a diverse spectrum of industries. Industries, such as energy and manufacturing, may require instant response when any machine fails to work or needs maintenance.

Organisations can use smart surveillance and benefit from real-time intruder detection edge services for their security. By using raw images from security cameras, edge analytics can detect and track any suspicious activity. Local and national governments have invested in edge analytics to boost public infrastructure effectiveness. For example, analysing data from sensors triggers real-time action, a function which can be used to improve security.

Sensors on trains can trigger stop signs in the event of an emergency without human intervention, send a message to the police or alert the fire department instantly. Edge analytics can go a long way in boosting security and improving the quality of public services using already existing resources, making them a worthwhile investment for local and national governments.

The future is edge analytics

Edge analytics is said to be the future of data analytics because of its ability to optimise data collection and analysis from network devices. In some cases, it is already preferred over conventional data analytics systems.

Visit our website for more information on how edge analytics is refining our approach to data analysis.

What is advanced analytics? What are its applications?

One of the most exciting aspects of data analytics is its constantly evolving nature. What started off as analytics systems that could only analyse data to describe the current situation has evolved into advanced data platforms that can process petabytes of data to predict future trends. The proliferation of advanced analytics is one of the more exciting developments in this industry.

But, what is advanced analytics? What is its business value? That is what we will be explaining in this article.

What is advanced analytics?

As the name implies, advanced analytics can do far more than what the standard analytics software can do. Advanced data analytics refers to systems that go beyond the capabilities of standard BI and analytics. These systems enable data analysts to go deeper into datasets using machine learning, pattern matching, sentiment analysis and cluster analysis, just to name a few techniques, which cannot be done on earlier versions of the software.

Advanced analytics functions are different from conventional analytics systems. It incorporates classic approaches to analysing data with newer, more advanced machine-driven techniques, like deep learning. Data mining is a key differentiator that separates standard analytics and BI from more advanced solutions like machine learning, neural networks and data visualisations. These advanced methods find the patterns and correlations in big datasets, setting the groundwork for deeper analysis.

Advanced analytics holds a huge advantage over standard data analysis software because it leads to better, more reliable answers. These data analytics platforms can mine data at a deeper level than what standard BI can manage. While some solutions, like self-service BI, hold a lot of value for certain business functions, they cannot compare to the level of analysis provided by advanced analytics. These analytics systems feature quality-tested algorithms that are always updated to analyse data to reflect current and future trends. Capabilities that allow data analytics software to create a more accurate picture than before.

In addition to a deeper, more comprehensive level of analysis, there are also other benefits. Some of these benefits include creating superior data models and simplifying data preparation.

Furthermore, the right analytics software simplifies analysis. When using advanced analytics platforms, like SAS advanced analytics, data experts don’t need several software platforms to complete different types of analysis, like categorical data analysis and psychometric analysis.

As you can imagine, this makes it much easier for any data analysts to use the system.

There is no denying that advanced analytics offers several technical advantages, but how can businesses translate these technical benefits into systems that generate value?

The applications of advanced analytics

Corporations in different industries can use advanced analytics to conduct more sophisticated levels of analysis. For example, in marketing, instead of examining what is popular with customers at the moment, businesses can take it one step further to decipher how consumer preferences evolve, which can be used to refine marketing campaigns. Another example is manufacturing, where businesses can use analytics to create self-maintenance systems that reduce wear and tear. This is possible because traditional BI systems analyse data to examine historical and current trends, but advanced analytics examine data to predict future trends.

The supply chain can be easily automated to improve operational efficiency. While some decisions will always be in the hands of humans, advanced analytics can be trusted to make decisions autonomously without any human intervention. Certain parts of the production process, like inventory checking, can be handled by systems developed using advanced analytics platforms. Other functions like monitoring, data gathering and forecasting can be executed using platforms built from advanced analytics. Due to their architecture, this system can function with greater efficiency than their human counterparts, giving organisations the chance to make significant gains in productivity and efficiency.

An exciting, new era in data analytics

As data analytics becomes more prominent, we begin to embark on an exciting era where advanced analytics powered by machine learning and AI becomes the norm. When that happens, we are going to see organisations make significant gains in productivity and efficiency. This is because analytics systems can do so much more than before, they provide deeper insights into data that allow organisations to be more proactive in their operations, making it easier to cut costs, save time and create exciting new technologies.

Resolving industry wide problems with food analytics

Food.

We never stop to think about the amount of work that goes into turning produce from the farm into a finished product found at grocery stores and restaurants, and that’s not accounting for the importation and exportation of food from other countries. There is an entire industry behind the logistics of distributing food across the nation.

I learnt about some of the problems they were facing while speaking with a client involved in the production and distribution of produce. “There is a lot we need to address,” he told me. “And the government is not making things any easier. They keep cracking down with regulations, and it forces us to reexamine our processes all over again.”

There are so many moving parts to the distribution and supply of food that it’s impossible to manage all of them without going insane. We need something that can not only help us keep an eye on the moving parts, but also help us anticipate future trends. Do you have anything that can help us?” My client’s answer was food analytics.

First, we need to define what we mean by food analytics. It is the all-encompassing term for analytics platforms designed to collect and analyse data to reveal insights into the food industry. Food analysis has become more complex in recent years due to factors like tighter regulation and higher manufacturing costs.

Tackling industry problems with food analytics

Food production, distribution and services are beset with problems with waste management being one of the worst. Recent estimates indicate that the amount of food wasted equates to 1.3 billion tons. This massive level of waste produces other problems, like greenhouse gas emissions and larger landfills. Why is there so much waste?

This is because all stakeholders across the supply chain are directly or indirectly involved in waste production.

However, it’s not just waste, it’s also a question of tightening regulation and addressing growing concerns about foodborne illnesses that are forcing food companies to re-examine processes, something that is much easier to do when food analytics is integrated into company operations.

In fact, many organisations are investing in analytics because it helps them improve operational efficiency up and down the production chain.

For example, food analytics can directly address the problem of food waste by using data to analyse the waste disposal process. This allows businesses to get a better understanding of the process, making it easier to identify inefficiencies and devise solutions to optimise it.

Similarly, producers can improve food quality by using analytics to study soil samples and weather conditions.

Finally, analytics can be used to improve logistics because food analytics can be used to transport food produce more efficiently. This is because transport companies can determine the ideal conditions for transportation, which could be useful when transporting delicate foodstuff.

They can improve co-ordination in logistics and even determine the best traffic routes based on data.

Assessing the future of foodservice and production

The future of the food industry will see food analytics heavily involved at all stages of the production process. Whether it is a restaurant, a producer or a distributor, all organisations across the industry will have to incorporate data and analytics into their processes to make significant gains in operational efficiency.

As an example, a restaurant can use predictive analytics to proactively manage demand and availability of food supplies, making it easier to plan pricing and menu offers.

Furthermore, food produce organisations will start using analytics to reduce the incidence of foodborne illnesses, similar to how the medical industry is already using predictive analytics to create proactive strategies that curb illnesses.

Retailers use food analytics to track consumer behaviour to predict consumer choices and even personalise the customer experience for their shoppers. Loyalty cards and other retail perks are all derived from this strategy.

Preparing for the future with data analytics

Given the numerous challenges the food industry are facing, it’s important to invest in a versatile solution that will improve operational efficiency and give organisations better insight into their operations.

By using food analytics, organisations involved in the production, distribution and selling of produce are better placed to make the changes needed to resolve the problems the industry faces.

What is IoT analytics and its role in the future?

The internet of things is set to play a huge role in the near future, especially in industrial sectors. Sensors, manufacturing equipment, pipelines and smart meters all have the potential to transform how organisations work. IoT generates a lot of buzz because it expands the functionality of organisations and shores up any weakness in their existing operations. For example, the State Department in the US is having IoT devices installed in embassies to study various touchpoints of the embassies, including air quality to assess conditions in these embassies. However, what is of particular importance to us is the connection between IoT and data analytics. In this blog post, I am going to discuss IoT analytics and its role in the future.

What is IoT analytics?

IoT analytics is the analytics platform that can assess the data collected from IoT devices. This variant of analytics is particularly well-suited to analyse IoT data because the devices typically generate a lot of information, in a relatively short time. Statistics show that IoT devices produce 2.5 quintillion bytes of data on a daily basis.

IoT data is similar to big data but there are differences not just in terms of size, but also because of the diversity of sources. The heterogeneous data sources make data integration an incredibly complex process – in fact, data integration is one of the biggest challenges to overcome. This is where IoT analytics comes into play.

Why analytics will play a huge role?

According to a study by 2020, IoT will include over 30 billion connected devices by the year 2020 – all these devices are going to collect a large volume of data and the amount of data produced by a sensor in the manufacturing assembly line, for instance, would take a lifetime to assess. This means we are looking at an immense volume of data that organisations will struggle to handle.

A recent survey incidentally also revealed that over 26% of companies said that their IoT initiatives were successful. However, many organisations do struggle with incorporating and handling IoT because they simply lack the right systems that can handle the volume, velocity and variety of IoT data. Without IoT analytics, it would not be possible for organisations to glean the benefits of the tech.

IoT analytics is incredibly versatile and can be used for any purpose. Whether it is assessing the current condition of manufacturing equipment or studying market trends, IoT data analytics can be used in any industry and to perform any operation. Furthermore, analytics can also act as a lynchpin for different functions like industrial automation, developing cloud solutions, creating mobile apps and hardware development.

Profit and non-profit organisations will see a massive increase in the volume of data coming into their system thanks to this and, if their analytics infrastructure is not ready, it will lead to a significant reduction in the rate of data analysis, which means lower operational efficiency. In other words, data analysis will take place at slower speeds, even possibly denying some of the benefits of IoT, like the real-time analysis of data. However, with IoT analytics, it is possible to maintain data analysis at an acceptable rate. Both for-profit and non-profit organisations will benefit from this version of the data analytics platform.

What does this all mean?

IoT analytics is the best choice for organisations with an eye for the future. Data from IoT is already at 5 quintillion bytes and will only continue to grow in the future. Furthermore, IoT devices are going to draw the data from several different sources, which makes it even harder to use because integrating data from heterogeneous sources is incredibly challenging. However, what IoT analytics does prove is that it is the solution most organisations need. The analytics solution is perfectly suited for the rigours of analysing IoT data, giving organisations insight into operational efficiency, a better understanding of the market and a much needed competitive advantage.

Modern businesses use interconnected devices that form the internet of things. Data for IoT can provide valuable information on ways to optimise your organisation’s performance and you can analyse this data using our Selerity analytics desktop. 

Contact us to find out more.