Elastacloud Channels

Discover the technologies and techniques used day-to-day by team Elastacloud. Read & Tweet us your opinions @elastacloud

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The day-to-day goings on at the Elastacloud offices.

23 posts

Parquet.Net is a .NET library to read and write Apache Parquet files.

6 posts

How does one build a secure, resilient and performant Cloud solution?

22 posts

Revolutionary approaches that will modernise the Data Stack.

23 posts

How Elastacloud are changing the face of Data Science, as well as tips, tricks, and best practices.

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Tracking the latest developments in the world of Technology.

4 posts

Learn about Elastacloud's past and present projects for clients.

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Latest updates on Elastacloud's community activites, including the Azure User Group and Azurecraft.

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Open Source R package for automated data exploration.

12 posts

How Elastacloud are changing the face of Artificial Intelligence.

4 posts

Socket Service within Service Fabric.

38 posts

Everything Engineering coming out of Elastacloud right now.

14 posts

What can the Elastacloud Team do to innovate within different sectors?

New Posts
  • Today, energy companies are at the forefront of cutting-edge technology, with an onus on supplying energy in the most sustainable way. The diversification of the UK energy supply mix incorporates low-carbon renewable energy sources such as wind, hydro and solar. Today more than 20 percent of energy in the United Kingdom comes from renewable sources. In summer, it’s often closer to 100 percent. Many energy companies have diversified their supply sources to incorporate these low-carbon and sustainable energy sources, but this has led to complications in balancing demand and supply. The Balancing Services Use of System Charge (BSUoS), charged by National Grid to help recoup the costs of balancing the grid from energy production, is a tariff which energy generators are liable to pay. The fee is paid to the system operator in half-hour periods for balancing the supply to meet demand, ensuring providers are generating energy efficiently. But if generation is not tightly monitored, the charge accumulates, severely damaging profitability. Difficulty predicting charges The charge is also difficult to predict. The price is volatile with external factors, such as weather conditions, which reflects the energy mix of the UK. The intermittent power generation and intermittency of weather conditions make calculating predictions complicated. With the ability to predict the charge, generation companies can better respond to the inherent intermittency of renewable energy generation and pricing. Equally, they will learn more about the drivers of the BSUoS charge and how to avoid over or under producing energy. The Elastacloud team were approached by a large UK energy generation company to discover if this could be possible with their data, and so, in a recent project we built the BSUoS Forecast. The solution: the BSUoS Forecast The forecast was built using Microsoft Azure machine learning services. Our Data Scientists used historical BSUoS, demand, and generation data to build the machine learning model for the forecast that generates automated reports for the user. These reports provide a prediction of the BSUoS charge for the customer with significantly greater accuracy than other industry standard models. The service enables energy companies to reduce costs by avoiding generation during the high price periods, and a reduced error risk. The proficiency and accuracy of the model not only provides advanced, industry-leading predictive reports, but makes it understandable and intuitive. As for the wider energy sector, the Forecast is a market-leading data science service, allowing generation companies to more profitably diversify their energy mix through improved optimisation of their own generation profile. Our unparalleled experience using Azure machine learning cloud services, combined with our energy sector expertise has enabled us to provide our clients in the industry with cutting-edge business solutions. The BSUoS forecast is just one example of how we boost our customers’ value through the optimisation of data. Get in touch today to find out more about the BSUoS forecast. The tool is available for energy companies who want to predict their BSUoS charge.
  • In the last decade or so the Higher Education (HE) sector has become a very competitive environment. Year-on-year, applications come in from across the UK, with institutions fighting to fill their courses with students who they believe have the best chance of succeeding for themselves and for the university. In this increasingly competitive market, attracting and retaining students is a top priority for all HE institutions. The general shift toward a more student (or customer) focused business model by many institutions shows evidence of this and suggests that universities are strategizing how they can best acquire and retain students. Institutions are beginning to recognise the importance of data science practices to enhance their understanding of their students and staff alike. In many cases, the application of data science techniques and platforms to analyse large data sets has proved extremely beneficial. Better quality, more detailed, and wider-spread data allows for better decision making and accurate strategic management of business operations. These capabilities can provide a university with a competitive edge over rivals in acquisition and retainment of their students. A New Challenge Elastacloud's work in the Higher Education (HE) sector aims to bring intelligence to institutions' data, helping to inform decisions and operations. Uniquely, we combine expert data science and machine learning capabilities with a high level of industry knowledge, helping us to provide the best solutions to our customers. Within the last year, a large UK university with a recognised diverse student base consulted Elastacloud, wanting to understand how students from different demographic groupings at their university engaged with the digital practice enhancement applications they had created. They also wanted to measure the students' digital capabilities and find out the extent to which interventions enhanced their skills. The university wanted to understand these key points so they could make informed decisions about how to help improve their students' digital capabilities; for this they needed Learning Analytics. In the context of HE, Learning Analytics (LA) is the measurement, collection, analysis and reporting of data about students for the purposes of understanding and optimising learning and the environments in which it occurs. Universities implement LA for a number of reasons, chiefly to retain students. The project sought to understand what effects using LA to make educational interventions has on distinct demographic groupings. The Project We began to explore and aggregate the university's demographic, biographic, and VLE usage data. Our Data Scientists were there to guide and inform the data exploration process. Through applying LA, machine learning technology, and data science techniques, we were able to identify cohorts of students and observe which demographic groups had low engagement, low attainment, or had high drop-out rates. The importance of this part of the project was that we were able to use engagement with VLE as marker for students at risk of dropping out of university. By using LA programmes, Elastacloud provided the university's project team with key information and insight into VLE usage and student digital behaviour trends. In addition, a baseline of engagement with the VLE was determined so that when interventions were made, they could be analysed for their effectiveness. The objective was to increase student and staff involvement with VLE and deliver student behaviour measurement capabilities for the university. The Outcomes The project found that there is a relationship between a student’s engagement with the Virtual Learning Environment (VLE) and their likelihood of dropping out. As a result, the university now has actionable insights into their previously untapped data, allowing them to work towards reducing drop-out rates amongst all demographics of students. Additionally, on our journey to answering the key questions posed by this project's aims, we were able to create new opportunities with machine learning analytic tools. The university aims to deploy and monitor these tools for student and employer use, ensuring engagement goals are met and feedback capabilities are encouraged. These include self-auditing staff programmes and student skills reflection areas which enable them to find help and access personalised feedback to improve their digital capabilities. The adoption of these machine learning tools has given the university the means and understanding to identify students requiring extra support and make interventions accordingly thus deterring them from dropping out. The university was impressed by what we were able to achieve together and how quickly we were able to achieve it. As such they continue to consult Elastacloud on large scale data projects as we help to answer some of the sector's most pressing questions.
  • Rail companies use data analytics daily to run everything from operations and maintenance to making key business decisions based on intelligence from the collected data. In the last 10 years, the emergence of the digital railway has incurred a demand for Big Data techniques to respond to an overwhelming surge in data collection capabilities and speed. Largely, the UK rail sector has embraced this development and has begun implementing Big Data analysis and Data Science technologies to stay ahead of competitors and provide a better modern service to their customers while maintaining assets and efficiency. The Digitisation of Rail Historically, the rail industry has been criticised for lack of innovation, falling behind the times in many areas and failing to capitalise on emerging technologies. Certainly, this has been improving in recent years. Today, companies collect vast amounts of data from rail stakeholders who provide intelligence via computer systems, the Internet of Things (IoT) and Cloud computing, which constitutes the move towards ‘smart railways´. To ensure the data’s business potential is realised and optimised, companies analyse and work to unlock the potential of the collected data, as Porterbrook, the train leasing company have done in the last year. The digitalisation of rail also offers new services for customers. Mobile ticketing has undoubtedly revolutionised the travel experience and new technologies such as the Trainline´s voice activated customer communication greatly enhances the way in which customers interact with companies. Undoubtedly, digital technology governs a great deal of rail customers behaviours including purchasing, expectations, operator information and reservations.  These leaps in digitisation in rail have led to improved monitoring of assets, automation in operations, and customer interactions, and as the technology becomes readily available and cheaper, the digital railway nears reality. Where Has the UK Rail Industry Been failing? While digitisation has provided the means for innovation and improvement of operations, recent reports indicate performance of trains services have not met expectations. Since the privatisation of Britain’s Rail Network, the government predicted that more competition between the companies in the sector would lead to a better service. In fact, the opposite has been true as ticket prices have risen, and lateness hit a 13 year high in the UK in 2018.  The issue is that while UK rail has certainly improved the maintenance and delivery of rolling stock assets, it is still way behind other countries in customer experience. Customers simply do not trust rail companies to deliver on time, and why should they given recent statistics? According to the Office of Rail and Road , 86.3% of trains arrived on time in the UK. In comparison, the Spanish high-speed network achieves 98.5% punctuality. Dan Ascher´s BBC article suggests that future rail companies should be underpinned by punctuality and excellent customer experience.  Many argue the root of this problem is a reluctancy to invest in smart technology combined with a lack of data science workers within rail companies. Indeed, it will require a change of mindsets and business models as well as significant financial investment in rail digitisation to improve operations and rekindle customer faith. Improvements will also need to be made into customer interactions utilising new technology to provide up to date, accurate and accessible information for passengers. How Can Data Science Improve the Rail Industry? Data Scientists extract meaning from and interpret large quantities of data. In rail, this includes all the current and emerging data used by railways to help monitor infrastructure and equipment and optimise maintenance to improve safety. “Big data analytics have the potential to influence several dimensions of the railway sector and can overcome organisational, operational and technical complexities, including economic and human effects and information handling.”  - Professors D. Galar, U. Kumar and R. Karim at Luleå University of Technology. Operations and maintenance are areas generating considerable excitement for rail companies due to self-learning and smart systems that predict failure, make diagnoses and trigger maintenance actions. Companies looking to introduce smart technology systems require data scientists to build these predictive Machine Learning (ML) models and provide a service that helps uncover the potential of the data. ML models and data science services that predict delays across the network are popular because they can: Help make savings on delay-repay compensation Identify interventions to minimise disruption Inform better train scheduling Inform better maintenance scheduling Improve public performance metrics To utilise the insights from these models, the right platforms needs to be well utilised for a company's specific data sets and requirements. Data scientists and rail industry experts should seek to collaborate to build the necessary algorithms and analysis tools to deliver the most effective solutions for the business' needs. The UK rail leasing company Porterbrook recently consulted Elastacloud to realise these advantages. Read the full case study here. Follow us: @elastacloud https://www.linkedin.com/company/2451879