Everybody loves to watch sports with detailed and insightful commentary. Have we ever stopped to think, though, about how broadcasters and athletes make sports so compelling for us insatiable viewers?
Behind the scenes, today, big data is playing a major role in making these events more competitive and engaging for audiences.
This is not necessarily a new concept. The early 1990s, in fact, saw the introduction of data analytics to the sports industry. Since then, everyone, from amateur athletes to the big leagues, has used it to enhance athletic performance, audience engagement, and marketing and branding strategies.
It has become so popular that the sports data analytics industry is estimated to grow to a whopping value of $4 billion by the end of 2022.
In this post, we take a closer look at why big data analytics became so popular and how it is changing the sports industry—forever.
Player recruitment is an important process for any professional sporting team. At the end of the day, while talented players are important when winning championships and sponsorships, talent, alone, is not the only important factor.
Other considerations play a role when it comes to athletic performance.
Modern sports franchises have understood this and are using data analytics tools to recruit the right players for their team culture.
The most famous example of this is played out in the movie, Moneyball. It is a movie based on real events involving a baseball coach from a cash-strapped club who uses data to hire talented, but undervalued players, in the hopes of winning the championship.
Since then, sports like soccer, cricket, football and basketball have adopted these techniques with great success. It has now become the de-facto method of recruiting players.
Anyone who has followed any sport for a considerable period will know how sports broadcasting has evolved over the years.
Sports commentary, for example, has evolved from just calling the play to educating viewers with stats and facts to make the viewing experience more compelling.
Broadcasters now even employ stats gurus to provide more information for viewers to understand the context of any performance.
Even simple graphical elements now carry important contextual information to help the viewer understand the importance of each event; all of which is made possible with big data analytics.
Strategising is an integral part of any sport, be it an individual or team sport. Professional athletes and teams are dependent on these strategies to compete and win against their rivals.
Modern coaching makes use of big data sets to create winning strategies to help individual athletes and the team as a whole. Data science allows coaches of professional teams, in particular, to create hyper-personalised athlete matchups and other strategies for every match the team plays. This way the team’s tactics are left unpredictable yet effective.
One good example is Liverpool FC’s use of data science to dominate opponents in the recent Premier League and Champions League. Liverpool’s coach used data science to change the outcome of the games as they were being played to great effect—they’re, after all, the winners of last year’s Premier League.
Broadcasters and sports officials have always looked to increase fan engagement in a bid to increase revenue. Big data has given them a new tool to do both.
Fantasy sports have become a quick and profitable way to increase fan engagement across any sport. They essentially allow any participant to build their virtual professional franchise based on real athletes. Fantasy football, alone, accounts for 25 million active participants, mostly from the US.
Many fantasy sports leagues are designed around big data analytics to help participants build an all-round fantasy team. According to a study done by Intel, 75% of fans wanted detailed real-time data to create better fantasy teams.
Big data has improved many industries over the last few years, and is no stranger to the sporting field. It has helped athletes enhance their performance, broadcasters their fan engagement, and coaches their gameplay tactics. Marketing data analytics plays a huge role in this process too.
Dive into data analytics to find out how you can transform sporting events and athletic performance with a handful of invaluable insights.
Big data analytics play a major role in not just the world of sports, but the business world as well. With our Selerity analytics desktop, you can have a SAS pro-analytics environment at your fingertips for all your data analysis needs. Speak to us today for more details.
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.
Here are some of the ways big data analytics in manufacturing can add value to your operations.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
In an increasingly politically aware world, big data analytics is the “trump” card most organisations and movements have when planning election strategies. With great power comes great responsibility and data analytics is a powerful tool that can be utilised to harness and reap a plethora of benefits.
We are seeing politicians use big data analytics to optimise their campaigns. For example, experts and journalists have coined Trump’s campaign a ‘data machine’ powered by AI capable of swinging voters, demonstrating the power of data and analytics systems.
This is not a new phenomenon, since the 50’s every party has used big data analytics to strategise their election campaigns.
Let us look into some of the big data systems and applications that have allowed countries and groups to manipulate the results in their favour.
The idea of using data in elections is not new. For example, the Kennedy administration used the “People Machine” to great success. It was, at the time, the largest such project ever conducted and it involved the use of massive data decades before “big data analytics” became a buzzword.
It was during this time that the use of computer simulation, pattern detection and prediction for election campaigns began. Opinion poll data from the archives of pollsters, George Gallup and Elmo Roper, created a model of the US electorate. The information gathered was pivotal in creating relevant strategies and ensuring those votes were coming in. This was seen even further back in our history when data was collected to better understand the masses.
A British “global election management agency” gained global traction because it utilised advanced data analysis along with strategic communication during electoral processes, which proved to be successful. They started in 2013 as an offshoot of the private intelligence company and self-described “global election management agency”. They were essentially in the big data analytics business.
This company used personal data to sway the outcome of the US 2016 presidential election and the UK Brexit referendum. But its reach extends well beyond the UK and US having supported more than 100 campaigns across five continents.
These data methods were used twice to help secure victory for Kenyan President Uhuru Kenyatta – first in 2013, then again in 2017. Officially, the company’s website boasts of doing in-depth research to uncover the issues driving voters in the country. They rebranded the entire party twice, wrote the manifesto, did research and finalised the messaging. They essentially created the platform, basing it on their findings derived from big data analytics to curate what the masses exactly wanted to see.
Jacinda Ardern – the Prime Minister in New Zealand – utilised big data analytics to bridge the gap between her policies and her voters, which swayed the constituents to her side and secured a second term, highlighting the importance of analytics in both elections and policy-making (although, it should be noted, that the Prime Minister’s aim was to refine her policies).
If we gauge anything, it is that these big data analytics platforms are mere tools and can be used for so much good, on the flipside of the coin, in a nefarious manner, if we are not cautious.
However, not all campaigns set out to exploit big data analytics platforms, we see politicians utilising such data to know where their possible voters could originate from, cross-reference them with the topics supported by the candidate and use the feedback to refine their policies.
If current trends are any indication, future election campaigns will be further entrenched in data analysis methods so as to glean the best approaches, efforts and results. Data analytics will be used much after the campaigns as well, with it being an integral part of understanding and flagging problems plaguing different population sections.
Data analytics has evolved itself to become the brain of every election campaign since the early 2000s. Data analytics helps the election campaign committee understand the voters better and adapt their policies to their sentiments, demonstrating the versatility of analytics platforms.
To know more about the ways big data analytics and its value in different industries, please visit our website.
The global entertainment and media industry is set to grow at 4.4 per cent compound annual growth rate (CAGR) through 2020 to reach just under $2 trillion this year, according to PwC. This growth will be driven by the industry diversifying their offerings, the employment of big data analytics platforms as well as higher demand for new content.
The entertainment industry is evolving at an exceptional level. New technologies like smartphones and digital media allow consumers to connect to a new entertainment world enabled by these disruptive technologies. Technology is making entertainment a more data-oriented experience. Content creators can leverage the data to generate new practices and innovative opportunities to create better quality content and create new material for consumers.
As a result, there are some significant benefactors within the entertainment industry when it comes to leveraging big data analytics platforms, resulting in increased profitability and a better connection between producers and customers. It is no longer sufficient to merely publish a daily newspaper or broadcast a television programme. Contemporary operators must look to drive value from their assets at every stage of the data lifecycle.
To better understand the transformation of the entertainment industry as they adopt big data analytics platforms.
According to Deloitte, how people consume media has changed dramatically over the past decade, creating both challenges and opportunities for the industry. Millennials today spend more time streaming content over the internet than watching it on television, and more than 20 per cent of them habitually view videos on their mobile devices. Streaming services like Hulu and Netflix, continue to flourish with approximately 60 per cent of consumers subscribing to them. By 2021, 209 million people will be using video-on-demand services, up from 181 million viewers in 2015.
With millions of digital consumers across the globe, media and entertainment companies are eager to reshape media platforms. They want to provide more personalised content to their audiences. In that sense, they are in a unique position to leverage their big data analytics platforms to improve customer engagement and profitability.
Data insights could be utilised to predict the demand for specific genres of shows, music, movies and weekly shows for a given age group, and what each viewer could potentially be interested in based on prior interests.
Big data can also help media and entertainment companies generate more revenue. The data does so by suggesting new ways to incentivise consumer behaviour, revealing the true market value for content, feeding into the creation of new content and providing more data for targeted marketing. Targeted ads are a gold mine in the entertainment industry. Channels are utilising big data analytics to understand how and when to push ads to consumers along with other value-added information that appeals to the consumer (boosting their bottom line). Understanding what part of their demographic wants helps curate sales strategies to be more effective and generate more revenue as it only hits the customer with the most interest.
Using insights gleaned from big data analytics, companies can understand when customers are most likely to view content and what device they’ll be using when they view it. With big data’s scalability, this information can be analysed at a granular ZIP code level for localised distribution.
This means that the ability of big data technology to ingest, store and process different data sources in real-time is a valuable asset to the companies who are prepared to invest in it.
By using big data to understand why consumers subscribe and unsubscribe, entertainment companies can develop the best promotional and content strategies to attract and retain customers. Unstructured big data sources such as call detail records, email and social media sentiment reveal often overlooked factors driving customer interest.
The amount of data that media and entertainment companies can gather regularly is mind-blowing; however, it’s what they do with all that information that really makes all the difference. The entertainment industry is making a rapid transition towards a more personalised experience for consumers, thanks to big data analytics platforms. Marketing and distribution, in particular, are undergoing a rapid transformation to create an experience that is beneficial to both consumers and content creators.
Please visit our website for more information on how you can transform your industry with big data analytics platforms.
If you want to experience the benefits of big data analytics for your organisation, our innovative Selerity analytics desktop will give you access to a SAS pro analytics environment that you can use for many analytics applications. Call our support team for more details.
With each passing year, the amount of data created on online platforms has increased astronomically. All of this information is crucial, as it allows businesses to understand the myriad of intricacies that exist in today’s markets. With more and more companies going international, the competition is fierce – and it’s only going to get tougher as the new decade rolls on. Similarly, with so many options available to them, consumers are pickier than ever, and gaining their trust and attention is paramount to making sales and conversions. It’s no surprise then that most organisations have begun to adopt big data analytics into their business process.
Big data systems are able to collect, store and process vast chunks of user data and make sense of them. Essentially, it translates all of this unstructured data into valuable insights that can be used in your everyday business processes. Now, it may seem that the utility of big data is mostly confined to business-consumer transactions; that the ultimate goal is driving conversion. In reality, the potential uses for big data analytics are far greater – it can play an integral role in the advancement of various industries and sectors. One of the best examples to illustrate this is the education sector.
Future insights can play a vital role in an academic setting and big data analytics can prove to be an amazing tool that can help improve the processes of teaching and learning. Here’s how.
Good curricula serve as the backbone of any education model, and designing one can often prove to be a challenging task, requiring extensive analysis and expert supervision. Still, no matter how inclusive and intricate a curriculum will try to be, it simply won’t be ideal for everyone that’s taking it up. Though it may seem impossible, it is possible to create customised curricula on an individual level through a combination of big data and online learning programs. So, how exactly can this be done?
Users these days are connected to a wide variety of platforms and devices, from smartphones and smartwatches to social media sites and online forums, and these are an excellent source of information for educators. Based on the big data analytics derived from all these platforms, you can create educational courses that better fit the needs and preferences of your student base.
Traditional classrooms stick to static courses with no real room for flexibility – everyone is expected to follow the same set of steps. The more streamlined and personalised curriculums that big data analytics allow for are a great start, but there’s no reason to stop there. You can take things a step further with big data systems by way of online learning classes. Here, you can allow students to do their own self-learning while you pinpoint target areas best suited for them.
Increasing student performance and providing an effective, stress-free learning experience should be the primary objective for contemporary educators. In order to achieve this, it’s paramount that they identify what the problem areas are for students, understand what their learning difficulties might be and correctly predict which students might be struggling and thinking of dropping out.
Implementing online learning methods, as we mentioned earlier, is one way to achieve this. But remember that you can keep track of this data as well. With big data analytics, you can easily visualise what subjects or areas your students prefer to self-learn. You can set up self-assessments that will store all the scores your students achieve, and through that, you will be able to identify which students might need additional help.
Accessing student performances, interests and strengths – especially over the course of their academic life – can provide a clear understanding of what their ideal career paths should be. This information can easily be relayed to students by educators; they could point out these patterns to undecided individuals and help them reach a decision.
What’s more, you can then begin collecting data on students who went through your education system and joined the workforce. Here, you can assess how effective their career choice was, and use it to optimise your curricula and make even better decisions in the future.
The rapid rate at which organisations have adopted big data in order to provide better experiences for their consumers has transformed the way markets look at data. The amount of raw data available to be processed is quite astronomical, and there is immense potential in the insights you can derive from big data analytics.
As far as the education sector is concerned, efficient utilisation of big data can lead to a great many improvements – for both students and educators. And even though it may take a while for education systems around the world to unlock the true potential of everything big data has to offer, the future certainly looks bright.
Smart grid big data analytics is promising to shake up an industry not known for its technological innovations: Utilities. There is a growing overlap between utilities and data, with data sensors and other equipment being integrated into the provision of utility resources. Energy companies are using smart grid analytics to measure various variables, like the amount of energy distributed from smart network triggers. Smart data analytics is, therefore, going to have a huge impact on how we live (if it hasn’t already). Hence, in this blog, we are taking a look at smart grid big data analytics.
Before explaining the benefits of smart grid analytics, it is necessary to explain what the smart grid is. It refers to the energy infrastructure of the future, fusing transmissions, transformers and substations that direct energy to households with modern technology like computers, automated technology along with other new equipment, which allows for digital communication or transmission of information, while also providing energy to households and organisations.
The ‘smart’ in smart grid refers to the additional infrastructure layer that allows for two-way communication between consumer devices and transmission lines. This two-way communication is possible thanks to the development of several innovations, like IoT and cloud computing.
The smart grid is a vital part of energy because it allows energy providers to draw full value from the smart grid. The smart grid refers to the new infrastructure where there is an emphasis on connected devices. It allows for a layer of communication between local actuators, central controllers and logistic units. This layer of communication is useful in many different areas because it allows for better response time during an emergency, more efficient use of resources and even improve the delivery of the network through automation. While the smart grid is all about keeping different devices like generators and consumer-end devices connected.
The smart grid is an exciting development because it represents a massive leap forward for the energy industry. It brings several benefits, like more efficient energy transmission, lower management costs, better security, operations costs and better integration of renewable energy. Naturally, the smart grid generates a lot of data, and smart grid analytics is needed to analyse the information produced. Otherwise, it would be impossible to extract any value from the data.
The smart grid produces large volumes of data, thanks to IoT devices like smart meters. IoT devices are placed in different areas of the smart grid, like the substations and consumer devices. These devices produce petabytes of data, and it’s impossible to make sense of the data without smart grid big data analytics. The analytics platforms can analyse data to generate invaluable findings that lead to several benefits, like cost reduction and operational efficiency. With smart grid analytics, energy companies can address issues, like finances and grid operations effectively and in a short time. This leads to several other improvements in grid optimisation and customer engagement.
A smart grid produces a lot of unstructured data and analysing this format of data can be very challenging. Moreover, in certain cases, unstructured data needs to be analysed in real-time. Unstructured data can be analysed by smart grid big data analytics. For example, SAS Asset Performance Analytics captures sensor and MDM data to improve performance, uptime and productivity.
Smart grid big data analytics comes in different formats to suit the needs of the energy company. Utility firms can choose between point solutions and a software platform containing a suite of software solutions. Point solutions are effective because they target a specific problem. However, a single multisolution platform offers its fair share of benefits because it allows for greater flexibility and can be seen as a long-term investment.
Furthermore, organisations can choose between an on-premise solution or a managed service. The on-premise solution provides the organisation with direct control over the analytics platform. However, it requires a significant investment to get the right talent and technology. Furthermore, building a team from the ground up takes a lot of time because a said team needs to get acclimatized to their work environment. Meanwhile, a managed service is much more affordable to set up because organisations do not have to deal with talent recruitment and capital expenditures. However, the tradeoff is that organisations do not have direct control over the platform. This level of flexibility between on-premise, managed and software as a service is one of the reasons why smart grid big data analytics is appealing to organisations.
Research indicates that the industry for smart grid big data analytics will grow to $4.8 billion by 2022 with a compound annual growth rate of 16%. Several trends in the utility industry are responsible for this growth.
The technology is relatively new, so most managers and executives are not as quick to embrace analytics. However, the growing popularity of IoT combined with the immense value to be gained in different areas, like customer management has made analytics an enticing proposition to many executives. However, it should be noted that in an industry as heavily regulated as utilities, change takes time to manifest.
While MDMs are still the norm and will continue to be so for quite some time. There is a growing trend where data sensors will overtake MDM as the device to measure utilities. Devices like cap banks, distributed PV solar panels, transformer sensors and voltage regulators represent the next wave of innovation in the utility industry.
Between the rise of unstructured data and the next wave of IoT devices, there is going to be a lot of data collected from different sources. To make sense of all the data collected, it needs to be integrated and represented in a format that generates useful insights. For these reasons, data integration is getting a lot of focus.
While smart grid big data analytics has the potential to transform the utility industry. It needs to be used properly to maximise its value. Not all data can be analysed in the same fashion. For example, some data should be assessed in the device itself, while other forms of data should be added to a data lake for analysis at a later time. To assess the right data at the right time, organisations need to look at analytics platforms that work at the right location. Hence, why smart grid analytics is vital, it can be divided into two categories: Back-office analytics and distributed analytics.
Backoffice analytics are perfect for certain functions, like overseeing grid connectivity, load forecasting and reliability reporting. For example, load forecasting collects data for analysis, so that utility companies know how much power is needed to meet short, medium and long-term demand. It reduces uncertainty, increases operational efficiency and provides better insight when making investment decisions. Meanwhile, distribution analytics can analyse data from meters, sensors and other devices. This type of analytics platform performs several real-time functions that include outage decision, voltage management and real-time load disaggregation. For example, real-time load disaggregation can identify how energy is used in distant loads and daily usage patterns. If utility organisations can learn about loads in real-time, they can devise measurements that improve energy management. It also identifies new ways to better serve customers.
When the right data is analysed in the right place, it brings several benefits to the organisation. Firstly, it allows for quick action. For real-time decision-making to be effective, granular, one-second data is needed to address the problem. This type of data can only be found on a local device due to lower latency and higher data volume. Having the right analytics platform analyse the data also ensures that useful information is generated at the right time and place. Secondly, organisations can be assured that they have the right data for the right purpose. For example, if there is a problem, having the right data allows organisations to determine if the problem is a device-based issue, a network-level issue or a system-wide issue. Furthermore, the right data analytics platform can make a huge difference, especially if real-time data is important for operations. Leveraging the right data in the right place leads to several improvements, like better customer engagement, smarter energy efficiency, superior asset management and stronger system integrity. It is also a better use of resources by the organisation.
Smart grid analytics is going to have a huge impact on the future. With the energy infrastructure of the developed world moving towards a smart grid, there needs to be an analytics platform that can capture and analyse data from different endpoints. The right analytics platform allows utility companies to distribute resources more efficiently, cut costs and discover better ways to serve customers. Furthermore, the right data analytics platform allows them to make the most out of the data produced. Every analytics company is looking to provide some variant of smart grid big data analytics, including SAS because utility companies will be looking for any way to improve energy management.
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Big data analytics is used to make sense of the growing volume of data to generate profitable insights. However, did you know that it can protect against cybercrime as well? With over 4.5 billion data breaches in the first half of 2018, cybercrime has grown in both frequency and scope, breaching conventional defences and exposing the information of both institutions and individuals. However, a solution is difficult to come by because cyber crimes are constantly changing in nature. Is there a fool-proof solution that will block cybercrime? In this blog post, I am going to explain why data analytics is the future for combatting this serious threat to information.
Before big data analytics, there were two challenges to combatting cybercrime: The growing volume of data and the range of attacks. Cybercrime does not follow one distinguishable pattern or method (at least, on the surface) because there are several attacks that occur, ranging from hacking organisation records to credit card fraud. It becomes nearly impossible to combat these incidents, which are growing more and more frequent.
The second reason is the growing volume of data. With organisations like banks, hospitals and government organisations gathering petabytes of data, it becomes even more difficult to find suitable methods for protecting the data. Without data analytics, employees have to comb through large data volumes, forcing them to look for a needle in a haystack. For these reasons, cybercrime has been impossible to combat, at least with conventional methods.
Big data analytics provides a solution because it is built to handle growing volumes of big data. Data analytics is more than capable of handling the large volumes of data organisations store. The reason behind this capacity is because of the sophisticated data algorithms that make up a data analytics framework. By using big data analytics, it is possible to process, manage and secure large volumes of data.
The second reason is that big data analytics frameworks can breakdown and discover the differences between various cybercrime attacks, like hacking and online fraud. This is because analytics can breakdown the data surrounding the attack and discover the similarities by studying patterns with pattern recognition technology, despite the differences in the attack method.
One of the biggest advantages of big data analytics is its ability to detect anomalies. Whether it is in the network or on devices, analytics can detect odd behaviour, which can then be flagged for further investigation. Big data analytics can detect anomalies because it can analyse data on a massive scale to discover connections and patterns. Hence, if there is a deviation from the norm, analytics will sense it at once, and flag it for further investigation. It is an incredible asset to have because it can pinpoint and help network analysts in the right direction, allowing them to target the time and energy towards the most likely causes of an attack.
Big data analytics can do more than just analyse data – it can also predict future attacks before they even occur. Analytics can predict the future (or some variant of it) because of its ability to study data and draw conclusions from it. This is especially the case when AI and machine learning are incorporated into an analytics platform. The ability to anticipate attacks before they happen is one of the most effective ways to combat cybercrime because it allows organisations to protect their data more effectively and develop a network that guards data.
By investing in big data analytics, organisations can identify the scope and breadth of the cybercrime offense taking place. Organisations can categorise the type of cybercrime attacks and how frequently they occur, ensuring heightened levels of criminal justice. Analytics can also leverage historical data to study the type of attack, the frequency of attacks and the type of information that’s frequently targeted. With this information, organisations can plan for cybercrime attacks intelligently, pouring more resources into vulnerable areas.
Cybercrime does not happen in isolation, there is a growing consensus amongst professionals, that there is an ulterior motive for stealing information from organisations. In fact, some security professionals have stated that cybercrime is an important source of funding for terrorism. Therefore, if organisations can cut down the incidence of cybercrime by half, they would able to cut funding to terrorism by half. It is one of the reasons why SAS, one of the leading providers in commercial analytics, devotes a lot of time and resources to combatting cybercrime. If organisations are to combat cybercrime meaningfully, they will need to install or optimise a big data analytics platform to protect their data – all while improving the livelihoods of the people around them.
Tax evasion is a huge cost to the Australian government. In 2018, ABC revealed that evasion and fudgers cost the Federal government over $8.7 billion in a single year, prompting the question: Is there a way to tackle fraud to cut losses? However, it is not just tax evasion that is causing the problem. Tax collection practices are laden with problems that make it difficult for corporations and individuals alike to follow the tax code. Fortunately, there is a solution in the form of data science and big data analytics.
Improve tax collection practices
Tax collection refers to the methods authorities use to complete different transactions, like collecting information. Here are a few ways data science and big data analytics streamlines these processes.
Faster data collection and procession
The tax code is a complex beast – one that requires a lot of data to process. However, while businesses and individuals have a hard time delivering the required information, state organisations have a hard time collecting and processing the large volume of information coming in. It significantly slows down the rate taxes are processed and dues are distributed. The slow data collection and processing time is a red tape issue, one of the largest problems state organisations have. However, data science and big data analytics can speed up data collection and processing significantly, which leads to a more efficient tax collection process.
Begin sharing information across different departments
Data science and big data analytics breaks down information silos and encourages data sharing across different organisations. Taxes are often overseen by different departments. For example, the state and national governments have their own codes to follow and little information is shared between the two segments. However, data analytics encourages data sharing because analytics benefits from a large pool of data. Sharing data leads to several benefits for state organisations like faster processing, less waste and a better chance of exposing fraudulent activities.
Data science and big data analytics prevent tax fraud
Data science and big data analytics are the perfect solutions to preventing tax fraud. Here are a few reasons why.
You can differentiate between a legitimate taxpayer and fraudster
One of the biggest problems state organisations face is distinguishing between well-meaning taxpayers and those who try to game the system to either underpay their taxes or exaggerate their income to get a larger rebate. Data science and big data analytics address this problem using data classification, clustering and trail-based pattern recognition to organise taxpayer data based on certain attributes making it easier to separate and distinguish between fraudsters and genuine payers. Data analytics can even be used to track activities in real-time.
Use different sources for analysis
Tax collection entails different variables ranging from income level to job status. Data science and big data analytics are excellent in leveraging both structured and unstructured data. The use of so many different variables leads to comprehensive analysis that allows state organisations to get in-depth insight, and gain a deeper understanding of the situation.
For example, incorporating future GDP projections allows the state to anticipate how much tax revenue they should earn in a time period. The state can then compare projections to what they actually earned to determine how much is lost from tax fraud. Other data sources to inform analysis include deadlines for application forms, declaring business losses, changed residences and so much more.
They scale down information
Tax fraud occurs because there is so much information to process and state organisations have a hard time processing this information in a timely manner, allowing fraudsters to take advantage of loopholes for their own benefit. However, with data science and big data analytics state organisations can scale down information by fusing social relationships. Using analytics, a tax fraud system can reduce the number of suspects and the doubtful transactions associated with them to make fraud detection easier than before.
Reduce fraud with analytics
Banks and many financial institutions are using sophisticated data analytics programs to detect and catch fraud in real-time. Hence, it makes sense for state organisations to take similar measures to reduce the incidents of tax fraud. Tax fraud prevention is complex because both corporations and individuals use loopholes to reduce the amount they pay in taxes or increase the amount they get back in refunds.
However, data science and big data analytics remove these complexities – making it easier to prevent fraud and protect the tax system.