Category Archives: Big Data Analytics

DIAD-Power BI-CloudMoyo

Know 5 key benefits of Microsoft Power BI before attending Power BI workshop

Power BI is a business analytics solution helping users to visualize data, create stunning dashboards and embed them in any application. It’s availability over a cloud platform means that it can be used without any capital expenditure or infrastructure support. It is free from legacy software constraints and is easy to start with. The most striking aspect is that it enables end users to create reports and dashboards by themselves, without any dependency on information technology (IT) team or database administrators.

What is Power BI Dashboard in a Day?

To increase awareness of Power BI and help adoption, Microsoft and its partners host multiple Power BI training workshops called ‘Dashboard in a day’. – This initiative offers the attendees (user of Power BI from beginner or intermediate level) the benefits and other potentials of Power BI. The event helps the users to know how Power BI can effectively deliver them their reporting needs. Dashboard in a day (DIAD) is an effort to make data analytics transparent for businesses. The users get a clear idea of where the data is coming from and how to have competitive advantages by leveraging Power BI in the business. Benefits of Power BI services, Power BI analytics, Power BI dashboards and visualizations, etc., are some of the major points of discussion that DIAD covers. The content of this one-day event also covers demonstrating how to implement Power BI, build basic Power BI visualizations and showcases various Power BI dashboard examples.

DIAD helps you understand that Power BI is competent to perform all the technical work and can simplify a tedious task to enhance the business!

Also read: 5 reasons why you need a Power BI implementation partner

CloudMoyo has successfully delivered numerous Power BI implementations, Power BI dashboards, self-service BI and end-to-end BI solutions to Fortune 1000 organizations. Apart from Power BI dashboards, we have expertise in all aspects of data warehousing, data modelling and the Microsoft Azure Data Platform including the design, implementation and delivery of Microsoft business intelligence solutions to customers. With our deep competency in delivering Power BI analytics and Microsoft business intelligence solutions, our experts have listed the features of Power BI that have captured the most attention in the DIAD sessions so far –

  1. Power to transform business: Users get a knowledge of how to format or shape any piece of information in a certain way and how to fix it at the source itself. Query Editor, one of the most potent & effective features of Power BI Desktop, allows many customary transformations like changing data types, transforming by adding a new column, splitting & merging, and adding a query. This feature helps bring the transformation in place i.e. resulting in effective formatting & visualization of reports.
  2. Power of Interactivity: Interactivity between the reports can be realized clearly once multiple visualizations have been added to it. To see visualization changing its output, a user can simply click a bar on a bar chart. Similarly, to view values of location charts, lists and KPIs just choose a location in map visuals. Turn-off the filter option in Power BI, if you don’t want to offer filtered-based options within the chart. Therefore, Power BI offers clarity and enhancement in structure, enabling you to put your report in action by shredding off time in creating & analyzing them.
  3. Advanced measure: Data Analysis Expression (or DAX) is a formula language used throughout Power BI. It works very similar to Excel, but it eliminates the complications of the piles of Excel reports. Therefore, with DAX, you can create your own metrics (like last quarter’s net sales) in Power BI easily and with a much faster approach. Power BI offers an advanced feature (Quick Measures) to create complex DAX expression like monthly growth, YTD, a percentage difference, etc.
  4. Extract hidden information: The ‘Insight’ option in Power BI allows you to check the hidden information on your data. Multiple charts are generated within the chart which have the potential to provide more effective and strong metrics. To revisit these useful insights, you can pin this visualization to your dashboard. This makes way for a next level transparency for data analysis in business. You can easily realize if more revenue is generated in a certain section/category for business. Therefore, this also helps in identifying trends and saving on costs.
  5. Excellent storage capacity: DIAD showcases millions of data sources in the form of multiple excel sheet or files. The spreadsheet/flat files with 11 million rows will not open or load easily in a regular machine. Even if you get success in opening the file in your machine, you will face issues in generating substantial reporting information from those data. However, Power BI has the bandwidth to load and transform millions of rows chart in a shorter span of time. Not only this, Power BI also has the capability to compress the file without compromising its quality and performance. For example, the total storage of these files is 420 MB, it can be reduced to 50 MB once it is uploaded to Power BI.

To emphasize the benefits of Power BI, CloudMoyo is offering qualified customers a customized, 10-day proof of concept to showcase the value of Power BI for your organization. We’ll demonstrate a simple, yet impactful, use-case of your choice, using your own data. We’ll use this to create a data model as well as a front-end report with visualizations, all intimately tailored for your business. Kick-start your Power BI journey here!

Data Quality Strategy-CloudMoyo

5 Myths about the ‘Data Quality’ that could derail your analytics project

Data quality is crucial to any successful Business Intelligence project. Poor data leads to poor reporting and decisions making capabilities. Data quality is a common issue in Business Intelligence as most of can identify and acknowledge. But, how do we define data quality?

Do you know some of the major characteristics that make up data quality? Data must be quantifiable, historical, uniform and categorical. It should be held at the lowest-level granularity, be clean, accurate and complete, and displayed in business terminology, etc.  These characteristics could be the difference between poor and good data quality or may even help you identify where your data needs improving.

Implementing a data quality strategy is not as simple as installing a tool or a one-time fix. Organizations across the enterprise need to work together to identify, assess, remediate, and monitor data with the goal of continual data improvements.

Are you planning to implement an enterprise-wide data quality strategy? Here are 5 myths that you need to know before implementing the data quality assessment.

Myth No.1: Organization’s data is accurate and clean

You may have built several safeguards to filter and refine your data, but it is nearly impossible to get rid of the issues. Unclean data will manage to enter no matter how many safeguards you have. Business and its data grows together. However, some business groups do not understand the impact of wrong data-entry. For example, Sales team faces constant pressure that they keep data entry as the low priority task. Training must be given to the data entry teams to ensure data is entered and managed correctly.

Myth No.2: Profiling and interrogating the data values is not important:

Common mistake done by almost every business- ignoring the evaluation of their data and knowing/understanding the value of the most critical data elements. Remember, you cannot improve the quality of the data without first knowing its value or current status. Whereas data profiling tool helps in visualizing how a data set (each data element) looks like, what’s the current status of a data, how valuable a data is, etc. It provides all the information related to physical structure of a data set like name, size, type, data values, etc. These information helps the data governance and business team to know and identify data issues, data values and solution to the issues.

Myth No.3: Following the data quality roadmap is not mandatory

Agreed! It could be difficult to stick with the scope of the data quality roadmap. When the project starts, take multiple directions and navigates to different routes without hitting the original roadmaps. However, keeping the data quality project synchronized with the scope and sequence of the original roadmap is important. It helps avoid multiple diversions, support team members, database developers, the data governance team, the business community to ensure they are going the right directions. Roadmaps help in making sense of the set of business domain. Unless there are severe circumstances that force a change, roadmap needs to be followed. Any changes can only be brought with standard change control processes, with proper documentation, review and approval.

Myth no.4- We can dodge the Assessment Phase

You cannot afford to bypass the organization’s data assessment phase. Some organizations believe they are well-versed with their business data, its quality and the value that they can draw from it. Therefore, they don’t analyze or evaluate the critical data quality and miss the chance to bring significant value to the business assets. This is the reason, you must never skip the enterprise data and application assessment phase that identify, assess, remediate and monitor data. In this phase, the business experts, domain experts, data governance team work together to identify data elements that can bring value to different/important business domain. They profile and analyze all the critical data elements to know their worth. In the assessment phase, they also develop metrics to give a high-level view to data quality and associated data elements.

Myth No.5: Data quality strategy can be built in one large project

Creating a data quality strategy in one large project is always a wrong idea. Start with small sub projects. It gives you an opportunity to test your idea in relatively smaller landscape and helps you ensure everything is going as planned. It will also give you a vision to know whether you’re going on a right direction. As you move further in the process, if needed, you can work on improvising the processes, tools, reporting metrics, etc. You can also continually improve the quality of other new projects.

Are you planning a revamp of your data platform? Do you need to improve your data quality? Then let us help you. Take our ultimate 5-day data modernization assessment to evaluate your current state for data management and BI. Register now!

Data Lake Implementation-CloudMoyo

3 questions you need to ask before implementing a Data Lake

What does it take to successfully implement a data lake? – Well, the answer is having a clear idea of what you aim for or why you need a specific set of data from data storage. If you’re are thinking whether or whether not to implement a data lake, here are the key questions you must ask:

  • The first and foremost question is how big the problem is? What kind of data can help you to address that problem, what kind of data you don’t need to save, etc. This will also help you know how you can accomplish with the stored data.
  • Is the data transactional or non-transactional? If the data is non-transactional or a mix of both then data lake is the right option for you.
  • What would be the best technology platform – on-premise or a cloud data lake?

Data Lake at a glance:

Choosing the right model of data architecture is crucial. The first thing to know before opting for a data lake is to understand what a data lake is? How is it different from a data warehouse? Is it the right model for your enterprise?

Well, data warehouse is a data architecture that necessitates on having only structured data in a tabular format while data lake allows the storage of both structured and unstructured (it can be a ‘messy’ combination of audios, videos, images, other data information, etc. in its natural format) in one storage/repository. A data lake has the capability to serve a number of data analytics.

In other words, data lake is a storage or a repository that stores data from disparate sources, generated in high volume, variety and velocity. This gives an enterprise flexibility to think on how a specific set of data can be used.

Also Read: Difference Between a Data Warehouse and a Data Lake

Role of Machine Learning

Machine learning helps in finding patterns and assists an automated analyst with determining what to do with the specific pattern of data. Machine learning provides you an option to analyze the data in the data lake itself.

Due to lack of skills and talent on board, most enterprises stumble upon the idea of developing a machine learning strategy after accumulating billions of data. Remember, billions of unnecessary data can sometimes turn a data lake into a data swamp.

It turns out to be frustrating in driving insights from a data lake without proper approach and right data strategy.

Also Read: How to build Enterprise-class Machine Learning apps using Microsoft Azure

Listed down are the three considerations you need to take before implementing data lake as this will give you a clear idea of whether a data lake is a right approach or not:

  1. Data type: As mentioned above, a data lake consists of all types of data- structured and unstructured, if you want to gain insights for this type of data, then go for a data lake without giving it a second thought. On the other hand, you might want to stick with a data warehouse if you are going to work with much structured, traditional data in a tabular format.
  2. Need for data: Do you just want to store a data to analyze it later? This is the core tenet of a data lake. Unlike data warehouse, data lake provides the flexibility to use a stored data for later use. The advance structuring of data not only requires a high cost of investment but also limits the repurposing power of any data in the future for new use cases. A data lake could be a good fit if you want to provide a higher level of flexibility for your future BI analysis.
  3. Skills and tools: A data lake typically requires significant investment in big data engineering. A big data engineer is difficult to find and is always on high demand. The data lake approach might prove difficult if your organization fall short of the skills of a big data engineer.

Data lakes are often criticized as chaotic and impossible to effectively govern. Whichever approach you choose, make sure you have a good way to address these challenges. It is advisable to start small. To gain proficiency in this landscape, you must start with a smaller data lake instead of kicking off the enterprise-wide lake. You can also use the data lake as an archive storage and let your business users access the stored data like never before.

Also read: A deep dive Into the Microsoft Azure Data Lake and Data Lake Analytics

You can use these top three considerations we have posed above as a general guideline for deciding whether your company or organization should be thinking seriously about building a data lake. If you want to know the difference between a data warehouse and a data lake, read this blog.

Talk to us and learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other Business Intelligence tools.

Take Our Ultimate 5-day Data Modernization Assessment today!

Azure SQL Data Warehouse-CloudMoyo

Cloud analytics with Compute Optimized Gen2 tier of Azure SQL Data Warehouse

Data has the power to transform a business in and out. In order to remain relevant and gain competitive advantage, an enterprise needs to have the ability to transform data into breakthrough insights.

Data is growing exponentially. To control the flow of this huge chunk of data and to convert this data into meaningful insights, businesses harness the power of data warehousing. Microsoft has empowered businesses around the globe to do this better with its robust Azure Data Platform. To help businesses deliver insights even faster and better, Microsoft Azure has recently launched the new generation of its Azure SQL Data Warehouse:  Compute Optimized Gen2 tier.

The new generation Azure SQL Data Warehouse (SQL DW) 

1). Query performance improvement through adaptive caching technique

The Azure SQL DW Compute Optimized Gen2 tier is a fast, flexible and secure modern data warehouse, designed for fast complex queries performance. If your enterprise is using the next generation Azure SQL DW, you will experience a dramatic query performance improvement. Additionally, it can support up to 128 concurrent queries with 5x computing power (as compared to the older version of Azure SQL DW).

Interactive queries performance is the top most requirement of any organization. What leads to a suboptimal query performance is the disk access. The potential gap between the computer layer and the storage layer in cloud computing causes a bottleneck for achieving high query performance. The updated version of Azure SQL DW uses its adaptive caching technique that automatically caches data allowing the SQL data warehouse to automatically move all frequently accessed data across different caching tiers. This strategy helps Azure SQL DW to deliver faster and next level query performances.

2). Powering enterprise-wide dashboards with high concurrency

In order to keep their valuable, confidential and sensitive data secured, organizations are compelled to restrict access of their data warehouses. The rigorous control over the stored data leads to analysis delays. Well, not anymore! Azure SQL Data Warehouse Compute Optimized Gen2 tier empowers enterprise-wide dashboards while increasing the number of concurrent queries that can be performed. Azure SQL DW delivers 4x concurrency as compared to the previous product generation. This allows seamless availability of data to the business users. Azure data warehouse has extended the workload management functionality to allow this next level high concurrency.

3). Predictable performance through scaling

The previous product generation-Azure data warehouse fulfills the ever-growing demand of organizations i.e. storing and operating huge data sets. Therefore, Azure SQL data warehouse is highly elastic. The Compute Optimized Gen2 tier brings 2 additional powers in this space -namely the capabilities to store unlimited data in SQL’s columnar format and the availability of new Service Level Objectives with an additional 5x compute capacity. Therefore, the SQL Data Warehouse now can deliver unlimited column store storage capacity.

Get started with Azure SQL Data Warehouse

Microsoft rolled out the Azure SQL DW Compute Optimized Gen2 tier to 20 regions initially that includes US, Australia, Canada, Japan etc. Take advantage of upgrading your existing data warehouse to Gen2. If you are getting started with cloud data warehousing or have any queries with respect to upgrading, we’ll be glad to help. Just give us a shout here!

Big data in rail-CloudMoyo

Why should Rail industry move towards the Cloud for Big Data Analytics?

Rail Transportation is one of the industries that persistently looks for master strategies to mitigate operating and maintenance costs. Like many others, railroads are tippy-toeing into the era of digitization, data analytics, predictive maintenance, etc. Over the past few years, railroads have been aggressively adapting emerging and innovative technologies (from software to SaaS applications to modern data solutions) to earn returns from an unconventional asset called ‘data’.

The scale of Big Data in Rail

Railroads generate mountains of data daily from sensors on rail cars, tracks, signaling equipment, communications systems, enterprise applications, accounting software, invoicing and many more. Modern trains are equipped with up to 200 sensors with data of up to 150 Mbps being transferred every day. There are hundreds of trains in various fleets with thousands of spare parts generating gigabytes of diagnostic messages and terabytes of sensor data per year.

Challenge of harnessing Big Data in Rail

The management and security of this big chunk of data is the biggest challenge for the industry. Cloud-based analytics system can help businesses in reducing unannounced downtime, improving productivity by enhancing velocity, hence driving outcomes. The technology can also help in driving efficiency in billing matters.

However, railroads face many hurdles in implementing big data and cloud analytics. Apart from financial challenges and heavy operating costs, railroads businesses face many difficulties in training their workforce how to implement latest technologies and tools. Emerging tools such as Stream Analytics can provide the ability to make use of streaming of event data records in real-time, which becomes useful for regulations such as Positive Train Control (PTC).

Rail transportation companies are still learning how to leverage the latest technological trends for a keeping a tab on their growing number of rail tracks, equipment and locomotive data. This data can be a real asset for these railroad companies as it is used not only for drawing insights but also to boost productivity, safety and operational efficiency. Railroads can drive efficiencies by predicting the failure of any major component/equipment by combining the theory of big data with advanced analytics.

How the Internet of Things (IoT) in Railroads is leading to data revolution

Railroads spend enormous amount annually in the maintenance and management of yards, assets, tracks, crew, infrastructure, etc. Inefficiencies and disorganization in the railroads trigger high operating cost to the system. Class 1 and Short Line railroads can save millions by adopting a modern cloud-based BI architecture to manage its assets, infrastructure and crew efficiently.

Putting Big Data to use in Rail Transportation

A Class I US railroad operator is just doing that and reaping the benefits of introducing modern technology to classical rail operations.

With our help, they defined a high-level digital transformation plan and an implementation roadmap for business intelligence platform in the Microsoft cloud. The Azure cloud-based data analytics system utilizing billions of rows of data & complimented by visually appealing Power BI dashboards enabled the customer to gain operational insights into Rail Crew management and train scheduling.

Read the complete story on rail analytics here

Difference Between Data Warehouse & Data Lake|CloudMoyo

Difference between a Data Warehouse and a Data Lake

Is a data lake going to replace the data warehousing system in near future? Whether to use a data warehouse or a data lake or both? These are some of the common queries raised by the business users. Businesses should understand the concept of both data lake and data warehouse, most importantly when and how to implement them.

A data Lake is a repository that stores mountains of raw data. It remains in its native format and transformed only when needed. It stores all types of data irrespective of the fact that whether they are structured, semi-structured or unstructured.

On the other hand, a data warehouse is a storage repository that stores data that are extracted, transformed and loaded into the files and folders. A data warehouse only stores structured data from one or more disparate sources that are processed later for the business users. Data extracted from a data warehouse helps the users to make business decisions.

Read and know-Towards which direction is the Data Warehouse is moving?

What is Right for Your Company- A Data Lake Or A Data Warehouse Or Both?

Organizations, nowadays, generate a huge amount of data and access the huge number of disparate datasets. It makes the gathering, storing and analyzing of data more complicated. Therefore, these are the factors to choose data management solutions- for data gathering and storing and later analyzing them for competitive advantages. Here’s where data lakes and data warehouses help the business users in their own way. Data Lakes can be used to store a massive amount of structured and unstructured data that comes with high agility -can be configured and reconfigured when needed. The data warehouse system as a central repository helps the business users to generate one source of truth. It needs IT help whenever you use the data warehouse to set up new queries or data reports. Some data, which is incapable of providing answers to any particular query/request, is removed in the development phase of a data warehouse for optimization.
Take a deep dive into the Microsoft Azure Data Lake and Data Analytics
Classifications give Clarifications

Let’s explore and classify a few points to present some key differences between the Data Lake and Data warehouse:

  1. Data: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured, etc… Unlike a data lake, data warehouses are quite picky and only store structured, processed data. When the data warehouse is in its development stage, decisions are made on the grounds of which business processes are important and which data sources are to be used. A data Lake allows business users to experiment with different types of data transformations and data model before a data warehouse gets equipped with the new schema.
  2. User: Data lakes are useful for those users who are looking for data to access the report and quickly analyzing it for developing actionable insights. It allows users like data scientists who do an in-depth analysis of data by mashing up different types of data, extracted from different sources- to generate new answers to the queries. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis. A Data warehouse is appropriate for predefined business needs.
  3. Storage: Cost is another key consideration when it comes to storage of data. Storing data in a data lake is comparatively cheaper than in a data warehouse. A data warehouse deals with data of high volume and variety, thus, is designed for a high cost storage.
  4. Agility: A data warehouse is highly structured, therefore, comes with low agility. The data lakes, on the other hand, requires to technically change the data structure from time to time as it lack a defined structure that help developers and data scientists to easily configure queries and data model when need arises.

Below is a handy table that summarizes the difference between a Data Warehouse & a Data Lake –

Basis of Differences Data Warehouse Data Lake
Types of data Stores data in the files & folders Stores raw data (Structured/Unstructured/Semi-Structured) in its native format.
Data Retention Do not retain data Retains all the data
Data Absorption Stores transaction system or quantitative metrics Stores data irrespective of volume and variety
User Non-cosmopolitan like the business professionals Cosmopolitan-the Data scientists
Processing Schema-on-write, meaning- cleansed data, structured Schema-on-Read, raw data which only transforms when needed
Agility Needs fixed configuration-less agile Configuration and reconfiguration are done when required-Highly agile
Reporting and Analysis Slow and expensive Low storage, economical

In the concluding lines, it is quite tempting to say, “go with your current requirements” but let me advocate you here that if you have an operative data warehouse just go for implementing a data lake for your enterprise. Alongside, your data warehouse, the data lake will operate using new data sources you may want to fill it up with. You can also use the data lake as an archive storage and like never before, let your business users access the stored data. Finally, when your data warehouse starts to age you can either continue it by using the hybrid approach or probably move it to your data lake.

Learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other BI tools.

Rail analytics-CloudMoyo

Unlocking value with Big Data Rail Analytics

Big Data and rail analytics is fast becoming the new normal and progress towards a smart railway system, once a daunting prospect, now seems unstoppable. It’s been a long time coming.

In previous decades, air and road transport technology sped ahead of rail in terms of modernization, but the railways have been catching up quickly, and finding innovative uses for the vast potential that data analytics can play in creating an efficient and robust rail system.

Smart Railroads

Smart railways require a natural convergence of three systems: cyber-physical systems, coupled with the internet of things (IoT) and cloud computing. Together, these systems have the ability to predict failures before they occur, make diagnoses as to where the problems lie and trigger actions that will speed up maintenance on all aspects of the system.

While the frameworks are in place to make this happen, the reality is that the operations are intensely data-heavy, in terms of access, quality of data and the multiple sources from which that data is required.

Many organizations can see the value in using data to drive decision-making in an organization but lack the capacity to invest in big data analytics. But the growth of Big Data as a Service (BDaaS) has meant that companies no longer have to develop their own Apache Spark or Hadoop resources, they can outsource to data-focussed organizations that are setup to process this kind of information.

See how CloudMoyo helps railroads harness their big data using Microsoft Azure platform .

How Big data and Rail analytics making a difference?

Scheduling

The explosion of big data and data analytics in the rail industry can make a huge impact on the timetabling of trains, as well as asset management of a fleet. With real-time data coming in and being interpreted instantly, there is great potential to reduce disruptions and improve reliability on any given day.

Data is generated via a number of sources to paint a compelling picture of a fleet:

  • Maintenance logs
  • GPS units
  • Weather data
  • Visual & Acoustic Sensors
  • Handheld devices to record speed, arrival time, location and much more…

Augmented Reality (AR) & Virtual Reality (VR)

The use of augmented and virtual reality has been increasing in the rail industry for some time, particularly in the field of training. Preparing drivers for the railways is a costly and time-consuming business, but the use of VR makes it a more affordable and immersive experience.

In the foreseeable future, that technology will make its way into maintenance crews too.
AR displays could potentially aid crews, working on trains with up to 200 wagons, to pinpoint exactly which wheel needs attention in order to prevent a de-railing. That kind of data is invaluable to maintenance crews.

Yard Maintenance

Keeping track of rail cars and other assets while in the yard is an often overlooked aspect of a railway company’s process. Data analytics play an invaluable role in keeping track of inventory, monitoring downtime of assets and tracking when railcars and trains enter and exit the yard.

Crew Management

One of the great challenges of operating a rail company is the effective management of crews over great distances. Cloud computing and analytics have made a big difference in a company’s ability to minimize downtime for employees, check availability, deploy the right skills at the right times, and use real-time insights to improve workforce performance and efficiency.

Check how cloud based crew management is helping the modern railroad to improve efficiency

Rail data innovation

Big data has the potential to transform the current state-of-the-art railway technology platforms into a network of collaborative communities seamlessly moving freight and passengers and delivering services in a planned way. The smart railways of the future are almost here, and will change how we manage railway systems and crew forever, thanks to the power of data analytics.

Big Data analytics changing the face of rail freight industry - CloudMoyo

How Big Data analytics is changing the face of rail freight industry?

The science of Big Data analytics is transforming global business practices in ways that would have seemed unimaginable just a few years ago. With that data comes insights and strategies that can generate massive savings and improve efficiencies for companies that understand them.

The transport industry understands this dynamic deeply, and is integrating Big Data analytics into every aspect of their operations. The initial drive came from road and air logistics, but in the past few years attention has focused on another sector of the transport economy that is ripe for disruption: the railroads.

How big data impacts railroad industry?

The modernization of railroads is an opportunity to create intelligent rail systems. In recent years, the rail infrastructure managers, train operating companies and the rail maintenance companies started to collect and store data in order to monitor different assets and rolling stock.

Simply put, it’s the ability to gather and integrate large batches of information together in one place, then interpret it in such a way that it leads to a recommended course of action. Sounds simple, but it’s a seismic shift for almost all businesses. Companies have moved from trying to understand what happened in a given scenario, on to why it happened, and then used their data to predict what will happen in the future. The next stage is really empowering; the ability to say “What shall I do to shape what happens next?”

There are a number of railroad sectors where Big Data analytics are having a big impact. Rail Crew management, the scheduling of trains and workforce, real-time monitoring and tracking of moving assets, safety, and the rapidly growing Internet of Things (IoT) , which is set to deliver more information and more changed than ever before to global freight companies.

Siemens, one of the world’s premier providers of railroads infrastructure in over 60 countries, has come up with a concept called ‘Internet of Trains which basically involves harnessing Big Data, sensors and predictive analytics for guaranteeing close to 100% reliability and ensuring that trains are never late!

CloudMoyo, a premier cloud technology consultant, has worked on a number of large railway network solutions across the continental United States in recent years in these areas of expertise, and has witnessed first-hand how fast the industry is changing. Benefits include major saving of up to 25% in freight costs, a 5-15% improvement in asset utilization, as well as reductions in inventory and an improved capability to acquire capacity. This has all led to higher customer satisfaction as an end result.

Unexpected events happen such as unplanned delays, urgent maintenance and accidents happen all the time on a large, dispersed systems such as the railways. Monitoring and responding to change quickly is vital. Big Data analytics uses multiple real-time sources, such as sensors and GPS on the trains, along with RFID systems in terminals, to feed information into the system constantly so that companies can use to manage their assets and resolve crises when they occur.

By using a cloud native product to monitor your railroad, you achieve faster adoption and deployment, with better mobility and at a lower cost. Serviceability improves almost instantly alongside reduced costs which leads to a positive revenue impact and differentiation from the competition.

The volume of data which is generated by railroads is staggering. Lyndon Henry, a leading authority on railroads, has stated that U.S railroads “need to handle and monitor the movement of approximately 1.5 million freight cars each day” over a rail network that is 140,000 miles long across North America.  When you add to that a scale-out infrastructure, unstructured data from social media, online feedback and machine learning, you get a sense of how big and complex the picture is.  “Routing, sorting and blocking cars, scheduling, assigning locomotives, and dispatching are some of the really complex railroad activities increasingly dependent on analytics” says Henry, “but an untold number of smaller tasks, such as remote diagnostics and real-time monitoring of rail yards, are also being facilitated or expedited via analytics.”

Add to this Positive Train Control (PTC) -the regulation mandated by US authorities for rail safety. While debates may run around arguing over effectiveness of PTC in preventing accidents, the implementation of the same is ongoing. And it is delivering waves of vital data that will have profound implications in our understanding of the rail system.

So, how do we handle all this Big Data in Railroads?

CloudMoyo empowers Rail & Transportation companies to gain greater insight and unlock efficiencies in Crew Management, Fleet & Asset Management, Contract Management and in other critical areas of operations and maintenance.  CloudMoyo’s pedigree in modern cloud-based analytics combined with our experience with both Short-Line and Class I rail customers allows us to be the ideal partner for railroads who wish to leverage large amounts of data into actionable Business Intelligence.

CloudMoyo prefers the use of various services from the Microsoft Azure stack such as Data Lake & Azure Warehouse for accumulating massive chunks of data, and using Azure Analysis services as well as Machine Learning to gain intelligence from the same. What’s more is that CloudMoyo is specialized in creating beautiful dashboards using Power BI so that decision makers can take action.

CloudMoyo has developed an Azure-based data analytics system utilizing billions of rows of data & complimented by visually appealing Power BI dashboards for simple interpretation. Metrics in the categories of inventory, crew and locomotives, utilization ratios, deadheads (and whatever else is needed) are presented to executives as actionable insights. Ultimately, these analytics are projected to create annual cost savings of 8-10% per crew per wagon via simple operational efficiency.

Of course, these are still early days in the relationship between big data and the railways. The Amadeus Research Group explains that “For railroads, big data demands big ideas and the courage to implement them. Managing and analyzing data is no longer an issue for IT departments alone, instead it is driving the travel industry’s business agenda.”

Ready to get started with Big Data but not sure where to start? Book a 5-day workshop with our experts and kickstart your rail analytics journey!

HOW CLOUD & BIG DATA ANALYTICS IS CHANGING RAIL SCHEDULING - CloudMoyo

How cloud and Big Data analytics is changing rail scheduling

There is a reason that the phrase “Making sure the trains run on time” is used to indicate successful management and efficiency around the world. Getting the trains to run on time is a complex process that involves sophisticated scheduling of crews, stock and schedules and so much more. Rail Scheduling is at the heart of a successful railway system and is often the difference between a profitable growing operation and a struggling, overwhelmed rail operator. This is one of the arenas of the rail business where modern technology and big data has had the most impact.

Why is rail crew scheduling a problem?

Freight railroad crew scheduling comprises generation of crew duties for running trains on a schedule in a cost effective manner while adhering to all labor regulations and operational requirements. Typically, a freight railway operation uses thousands of trains and requires thousands of crew members to operate them. Because of the massive scale of this problem, even tiny savings in crew costs can result into large financial savings. However, freight railway operations are complex, and a crew-scheduling problem is difficult to solve

Success can be measured against two key scheduling measures in a rail environment.

  • Are we able to provide the number of trips that were agreed on with the rail authorities?
  • And in doing so, are we minimizing the number of empty runs (“dead runs”) that take place, typically at the beginning and the end of a schedule?

An inability to meet either of those challenges poses a severe threat to the operator’s viability and profitability. But with more trains and passengers on the rails than ever before, in addition to a huge mobile workforce, the logistics of managing all those moving parts becomes harder and harder to get right. Cloud-based analytics has proven itself to be a vital tool in the fight for better rail scheduling.

Cloud Technology in rail scheduling

A premier Cloud & Big Data Analytics consultant, CloudMoyo, recently took on a big data challenge with a major North American rail operator, who deal with movement of large volumes across a large geographical spread on a daily basis. Their CEO & President, Manish Kedia, explains “Proper trained personnel must be deployed for the efficient and timely movement of goods. Any delay in movement results in huge financial loss to the operator. With over 2000 crew members, the operator also needs to ensure that crew allocations are done based on their skillset, which ensures increased utilization of assets and lower downtime.”

Find out how a Class 1 Railroad automated its crew scheduling using modern technology

The solution was to develop a cloud-native rail crew management system which can ensure that all available resources are optimally utilized to maximize existing investment. The key to successful rail scheduling is to look at the whole system. Not only management of the rolling stock, but fuel management, route optimization, control center ops and crew analytics all come under the spotlight, so that holistic change spreads throughout the system.

CloudMoyo’s Scheduling module is based on advanced algorithms that are used for determining the most optimal schedule based on various parameters that are configured in the system. It is based on history as well as traffic patterns. The planning output desires to satisfy agreed upon policies on service span and desired frequencies for the schedule. Using advanced algorithms that take into account ticketing history as well as traffic patterns, a big data analyst will use flexible travel time definitions, frequency definitions, revenue and cost considerations, and resource constraints for vehicles and crew to create the optimal schedule for an operator.

These types of interventions have been shown to produce a 5 -15% improvement in asset utilization, as well as up to 10% better driver utilization and ultimately a 2-5% reduction in overall costs and a massive leap of up to 30% in customer satisfaction.

Book a demo of CloudMoyo Crew Management – a one-stop solution for the modern crew manager

Rail Scheduling and effective crew management make a dramatic difference to profitability and sustainability of rail operators.  A rail system that runs on schedule has major knock-on benefits for the whole local economy and the civic life of society itself. At the end, a happy crew is what drives efficiency!

If you would like to find out more about how Big Data Analytics can make an impact in your organization, get in touch to book a 5-day Azure Assessment with our Data experts.

railroads crew management Solution - CloudMoyo

Why railroads crew management solution is important?

Railroads typically have to answer numerous questions around on how to use their capacity most effectively, how to move trains optimally how to operate their yards, how to maximize their throughput and how to keep their locomotives busy. Using their assets most effectively is the key to their profitability. Apart from this, there is one more major issue and that is railroads crew management system.

It has become a cliché to say that a company’s biggest asset is its workforce. Yet time and time again, it’s proven that a well-managed talent pool achieves the kind of breakthroughs in efficiency and productivity that all top-level managers is looking for.

Why is railroads crew management system too complex?

For railroad industry, crews are at the heart of its workforce. The logistics of managing a large, decentralized crew are extremely challenging. What’s required is a solution that can address the challenges of operating a multifaceted railroads crew management operation. A well-managed and empowered workforce makes all the difference to a company’s bottom line and to the culture of the organization. Focus needs to be placed on workflow management, mobility, modernization and continuous improvements around customer service.

Technology for modern Crew Management

CloudMoyo’s Rail Crew Management assists in managing a crew effectively on the basis of availability, allocation order as well as train schedule compliance. In so doing, Crew Call is able to minimize costs around crew pay, benefits and travel expenditure and keep the focus of employees on customer service and efficiency.

Being able to apply a Big Data lens to a company and using the insights generated to manage a large, complex workforce is the hallmark of good railroad management in the 21st century. Across the continental U.S, there are  140 000 miles of tracks, being used by approximately 1.5 million freight carts every single day. To keep this system running requires an army of dispersed employees who are spread out over vast distances, yet must work together as part of an interconnected system.

These are the kind of issues that cloud computing is built for. CloudMoyo Crew Management delivers a system with a flexible crew profile and skill profile management, configurable work hour and service cancellation rules, as well as a performance assessment engine and availability tracking for crew members. Biometric devices record the attendance data of crew members, while an interactive voice response system is used to notify crew of upcoming duties. What’s more is that it integrates various crew accommodation service providers so that lodging providers know who’s coming and staff accordingly. It lets railroads control stays, eliminate wait times and manage costs better.

Applying data analytics to railroads crew management

Of course, many companies are aware of the usefulness of workforce data. But they are frequently unable to process that data successfully and turn it into actionable insights to drive decision-making, particularly when it comes to workforce management. Poor decision-making in this arena leads to wasted man hours, unwieldy and complicated logistics, missed targets, a steadily lowering morale that affects the whole organization and ultimately, a weak bottom line.

But on the flipside, when big data is being continually processed, analyzed and acted upon, it enables a company to ‘get ahead of the curve’ to analyze its future workforce needs, identify potential shortfalls and develop strategies that will bridge gaps. “Good analytics help firms to stop wasting money on programs that don’t help them achieve their business goals, and focuses them on those that do,” notes Laurie Bassi of McBassi & Co, a consulting firm that specializes in workforce analytics.

Read how an Effective Crew Management Solution can Modernize Railroads

Integrating new tools into a crew management system is a time-consuming process. For that reason alone, many companies find it more rewarding to outsource their data analytics and work with specialist providers who employ a suite of tools that are designed to work in harmony with each other. CloudMoyo prides itself on delivering a solution built on the robust Microsoft Azure Data Platform that incorporates an enhanced user experience via an intuitive mobile app, easy integration with systems like SAP, IBM-MQ, Payroll, IVR etc, round the clock technical support and a flexible payment solution.

While a productive and happy crew is the central driver of the Crew Management system, there are other spin offs that make it even more valuable. Valuable data insights are gathered and presented with a smart analytics engine that integrates easily with Power BI and is presented in easy-to-understand graphics and dashboards. With such advanced reporting, companies are able to continually monitor such metrics as inventory, crew and locomotives, crew utilization ratios and much, much more.

Ultimately a cloud-based workforce management system that is integrated and well executed leads directly to increased job satisfaction and productivity throughout an organization, and fosters a culture of sustainable excellence.

Book a demo of CloudMoyo Crew Management – a one-stop solution for the modern crew manager

railroads crew management-CloudMoyo

Role of Cloud & Big Data in railroads crew management system

One of the most gratifying things that any Big Data Analytics firm can be a part of is the transformation of an established company. Big Data helps in integrating new practices and insights generated by company’s data.  Here’s an example of the digital transformation in railroads crew management which happened in the collaboration between CloudMoyo and a North American railroad operator.

Logistics are the lifeblood of a transportation company. This North American  railroad company has been in operation even before the 1900s and operates in central U.S, Mexico and Canada. It has over 10000 freight cars, 1050 locomotives and their rail network comprises approximately 7000+ route miles that link commercial and industrial markets in North America.  It has approx 500 trains running per day with an average of 800+ crew members daily across 180+ interchange points with other railroads. Add to this the complexities of repairs, re-crews, duty allocations, scheduling, incidents, services, people & goods movement, vacations, communications and it turns out to be a heck of a day. Needless to say, it’s a massive transportation and logistics business with mountains of data.  In order to get its operations right, railroad turned to Big Data for a solution.

Why do you need a cloud-based rail crew management system?

Thanks to Cloud and Big Data analytics, CloudMoyo was able to come into this industry as a relative outsider and deliver a next generation, cloud native crew management system that addresses all the challenges of managing complex transit operations. In order to implement the crew management software, a 12-month schedule was created with the aim of turning the client from a company without a centralized or integrated system that relied on text messaging for duty notification into an information-centric, data-smart organization. As a result of the new system, 100% of all the train scheduling, as well as the deadheading (the movement of commercial vehicles or crews in non-revenue mode for logistical reasons) are managed through railroads crew management solution.

But while infrastructural efficiency has been greatly improved, that was only half the problem solved. As in most businesses, the improved management of the workforce is where the real value lies. Without a centralized and integrated system, all crew tracking was done manually, and there was a text-message based system of duty notification. Outdated processes and human error often came together to create train delays, spoiled goods and many wasted man hours for the nearly 2000 employees of the company.

CloudMoyo CrewCall System

CloudMoyo developed a CrewCall system, which was able to schedule crews effectively by managing crew availability, crew allocation order and train schedule compliance, as well as assigning crew to trains with a cloud-based notification and acceptance system. Attendance records, automatic crew selection, member availability and tracking were all new and vital aspects of crew management that were built into CrewCall and transformed the workforce management for the client.

Ultimately, the system uses Microsoft Azure cloud and the advances in mobile technology to balance crew and equipment needs in a cost effective way and enables quicker time to deployment for the operators, thereby leading to quicker ROI. Workforce and logistics management, which is delivered via a state-of-the-art user experience, leads to an improved work experience for all the crews involved, as well as a reduction in overtime and therefore cost savings, and a stricter adherence to labor laws.

A happier, more efficient and more productive workforce are all huge benefits derived directly from excellent Big Data analysis. That intervention allowed a company to look towards the future and expand its operations with the knowledge that they now have, all system in place which can scale easily and can carry them into the future effectively.

Click here to read more on how CloudMoyo helped a North American railroad to manage its crew efficiently.

Seeing is believing, so if you want to see this solution for yourself, then please click here and get in touch with us.

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Top 3 ways Big Data analytics is transforming the pharma industry

Advances in the arena of pharmaceutical development have suffered from declining success rates for some time now, due to a number of critical factors such as decreased R&D, as well as multiple challenges to growth and profitability, and the increasing cost of regulatory compliance. But there are a number of bright spots on the horizon, most notably with the incredible advances in the capabilities of big data and analytics, and their integration into all aspects of the pharmaceutical industry.

Global research firm McKinsey Global Institute estimate that $100-billion in value can be generated within the US health-care system through the strategic and targeted use of big data. A Mckinsey study says that by optimizing innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers, and regulators can meet the promise of more individualized approaches.

But in order for this to happen, big data analysts need an integrated approach in gathering data; from patients to caregivers and retailers of pharmaceuticals, as well as from the R&D process itself. This holistic view of the entire pharmaceutical chain will provide a pathway to finding the most effective medications from all the data and dramatically changing lives for those most in need.

  1. Breathing New Life Into R&D: A number of factors are critical when it comes to re-invigorating the R&D market: analysis needs to happen in real time in order to avert any safety concerns and costly delays. Data can no longer be handled in a cut-off or siloed approach, it requires a more integrated method of gathering across multiple departments. Furthermore, the makeup of clinical trials can be significantly improved with big data. Using tools such as social media, real-time sensors and genetic information to target specific populations, clinical trials can be streamlined thus making them more efficient and cost-effective.
  2. Steps to a Better Industry: In order for Big Data to deliver a more profound impact on the pharmaceutical industry, CloudMoyo – a partner for Cloud & Analytics – has suggested a number of measures that need to be implemented in order to bring about massive improvements. Firstly, data needs to be managed and integrated at all stages of the value chain, then all stakeholders need to collaborate to enhance linkages across drug research, development, commercialization, and delivery. Thirdly, portfolio management needs to be data-driven for the analysis of current projects, and pharmaceutical R&D should employ cutting-edge tools which will enhance future innovation. Biosensors which are linked to apps are making health-measurement more effective and more affordable than ever before. All of these measures should result in improved clinical trial efficiency and a better safety and risk management record.
  3. Multiple Benefits to the Industry: Apart from the direct arenas of R&D and Clinical Trials, big data has a lot to offer the pharmaceutical industry in terms of sales and marketing, regulatory compliance, consumer support, as well as complex contract management solutions to create win-win solutions with multiple stakeholders and payer organizations. It’s no exaggeration to say the rapid uptake of cloud computing is changing every aspect of the pharmaceutical industry.

CloudMoyo’s Role:

Big data analytics firm CloudMoyo has pioneered the use of advanced analytical models to improve targeting of customers and to gain insights into the different areas of the complete business. In one such instance, an increased visibility into the sales pipeline and an ability to track the entire sales cycle led to an improvement in the conversion rates of a US-based pharma CRO by 15% and a reduction of the sales cycle by 10 days.

Sales analysis is not the only aspect of the pharma industry where CloudMoyo has been getting involved. CloudMoyo has also helped to transform pharma contract management through Analytics thereby extracting valuable insights and helping their regulatory compliance needs. The company understands that advances in sensor technology and cloud-based data management are helping to provide control to both patients and their healthcare professionals. So the company developed technology that would deploy architecture patterns for streaming and analyzing real time data from sensors on the cloud, and integrate real-time video feed and analytics in order to improve the digital health initiatives of their clients.

The potential of big data to provide predictive and evidence-based analysis, coupled with a reinvigorated R&D environment and the cost-cutting measures that flow from these initiatives, suggest that big data has an enormous role to play in the pharmaceutical industry in the years to come.

If you feel your company would benefit from our Azure Assessment where CloudMoyo looks at your data structure and provides feedback and a roadmap for the way forward, then please get in touch with us.