The Power of Data and The Importance of Data Engineering Solutions.

The Importance of Data and Data Engineering Solutions.

Data has become the lifeblood of Modern Businesses. From Customer Insights to Operational Efficiency, Data is at the heart of every decision-making process. However, data on its own is not enough. Without proper management, data can quickly become overwhelming and useless. This is where Data Engineering Solutions come in.

Data Engineering Basics

Data Engineering is the process of transforming raw data into usable data, allowing businesses to make informed decisions. It involves:

  • Collecting,
  • Storing,
  • Processing, and
  • Analyzing Data,

while Creating Pipelines that deliver the right data to the right people at the right time.

The importance of this service cannot be overstated, as it enables businesses to unlock the true value of their data.

Benefits of Data Engineering

One of the key benefits of Data Engineering is the ability to create a unified view of data across an organization. Data Engineering Solutions can bring together data from multiple sources, such as Customer Data, Financial Data, and Operational Data, and create a single, accurate view of the business.

This unified view can help businesses make more informed decisions, as they have access to all the data they need in one place.

Data engineering solutions can also help businesses automate their Data Processing and Analysis. By creating automated pipelines, businesses can save time and reduce errors. This can be particularly beneficial for businesses that deal with large volumes of data, as manual processing can be time-consuming and prone to errors.

Another important aspect of Data Engineering is Data Governance.

Data Governance refers to the process of managing the availability, usability, integrity, and security of data used in an organization. Data engineering solutions can help businesses ensure their data is compliant with regulations and standards, such as GDPR, HIPAA, and PCI DSS. This can be crucial for businesses that deal with sensitive data, such as financial or medical data.

Tarams’ Data Solutions

At Tarams, we specialize in providing Data Engineering Solutions to businesses of all sizes. Our team of experts has years of experience in collecting, storing, processing, and analyzing data. We use the latest technologies and tools to create customized solutions that meet the unique needs of each business.

Whether you need help with

  • Data Integration,
  • Data Warehousing,
  • Data Lakes, or
  • Data Governance,

We can help.

Our Data Solutions are designed to be scalable, so they can grow with your business. We also offer ongoing support and maintenance to ensure your data is always accurate, secure, and available when you need it.

Along with this preview of our Data Engineering Services, our earlier published article sheds some light on how to capitalize Data.

Summation

In conclusion, Data Engineering is essential for businesses that want to unlock the true value of their data.

By providing a unified view of data, automating data processing and analysis, and ensuring data governance, data engineering solutions can help businesses make more informed decisions and gain a competitive advantage.

At Tarams, we are committed to providing the best data engineering solutions to help businesses succeed.

How can we help you?

Business Intelligence(BI) Services in 2023

Business Intelligence(BI) Services: Why They Are More Important Than Ever Before?

In the modern business world, Data is King. Companies that are able to effectively gather, analyze, and act on data, have a significant advantage over their competitors. This is where business intelligence services come in.

Business intelligence (BI) services provide companies with the tools and expertise they need to collect and analyze data, turning it into actionable insights that can drive better decision-making.

BI has been around for decades, but its importance has only grown in recent years. In 2023, we are seeing a number of trends that make BI services more essential than ever before.

The Rise of Big Data

Perhaps the most significant trend driving the need for BI services is the explosion of data. With the rise of the internet and social media, there is now an unprecedented amount of data being generated every day. This data comes from a variety of sources, including customer interactions, social media posts, online sales, and more.

While this data can be incredibly valuable, it can also be overwhelming.

Companies that try to manage and analyze this data on their own often struggle to make sense of it all. BI services provide a solution to this problem by offering advanced analytics tools that can sift through vast amounts of data to identify patterns, trends, and insights that might otherwise be missed.

The need for Real-Time Insights

Another trend driving the importance of BI services is the need for real-time insights. In today’s fast-paced business environment, decisions need to be made quickly.

Companies that are slow to respond to changing market conditions or customer needs risk falling behind.

BI services can help companies stay on top of these changes by providing real-time insights into key metrics like sales, customer behavior, and more. With these insights, companies can make informed decisions quickly, improving their agility and responsiveness.

The rise of Artificial Intelligence

Artificial intelligence (AI) is another trend that is driving the need for BI services. AI technologies like machine learning and natural language processing are becoming more advanced every day, and they have the potential to transform the way companies analyze and act on data.

BI services that incorporate AI technologies can help companies identify patterns and insights that might otherwise be missed.

For example, machine learning algorithms can be used to identify customer segments that are likely to churn, allowing companies to take proactive measures to retain those customers. More on this can be found in our earlier insight article.

The need for better Data Security

As data becomes more valuable, it also becomes more vulnerable. Cyberattacks are becoming increasingly common, and companies that fail to protect their data risk significant financial and reputational damage.

BI services can help companies protect their data by implementing advanced security measures like encryption, access controls, and more.

With these measures in place, companies can be confident that their data is secure and that they are in compliance with regulations like GDPR and CCPA.

In 2023, Business Intelligence services are more important than ever before.

The rise of big data, the need for real-time insights, the rise of artificial intelligence, and the need for better data security are all trends that are driving the need for BI services.

Companies that invest in these services will have a significant advantage over their competitors, as they will be able to make informed decisions quickly and stay ahead of changing market conditions.

If you haven’t already, it’s time to start exploring the benefits of business intelligence services for your organization.

Tarams’ Solution Offering

Tarams offers a comprehensive range of Business Intelligence services, including consulting, implementation, and analytics. Tarams’ BI consulting services empower companies to move from insights to action and optimize performance.

Our areas of BI expertise include:

Consulting and Architecture –

Strategizing a business roadmap with solutions from consultation to architectural design and development with end-to-end solution capabilities has been at the forefront of our BI Services. This helps wire together the entire organization into a network of centralized architecture with a decentralized approach.

Tarams’ solution enables employees to perform the following Business Intelligence functions on a self-service platform:

  • Business Intelligence Strategy and road map
  • Technology Evaluation & Rationalization
  • Optimization and Performance enhancement of the Business Intelligence Infrastructure
  • Big Data strategy
  • Conceptualizing, designing, developing, and implementing DW solutions

From a development perspective Tarams prepares the architectural road map by focusing on: 

  • Discovery/Requirement Gathering
  • Data Visualizations and setting up for Reporting and Analytics
  • Implementation of Business Intelligence Solutions
  • Continued support and maintenance

Big Data –

These solutions work by synchronizing disparate data in a single platform with high-performance algorithms. Tarams’ Big Data solutions help companies enhance operational efficiency and gain a competitive edge. They have the following features and deliver the following benefits:

  • Manage Capacity Planning and Performance
  • Provide the right solution, regular upgrades, and Data Optimization
  • Define Strategies
  • Create a workable blueprint for realizing Data Goals
  • Provide technology evaluation and recommendations
  • Build Proofs-of-Concept and use cases
  • Feasibility assessment of solutions implementation

Data Warehousing –

The Data Warehousing Service Support consists of maintenance, operation, and data cleansing. Tarams helps users connect, visualize, and share data seamlessly, anywhere, and on any device. It helps run ETL activities efficiently with the support of the latest tools and technologies while ensuring connectivity between various repositories from databases, mainframes, file systems, web services, and packaged enterprise applications.

It connects with Data warehouses, OLAP applications, Software-as-a-Service, and Cloud-based applications. Some of the other features include:

  • It provides a workable data modeling solution for managing the complex data environment
  • Captures real-time information delivery to support quick data-driven decision-making
  • Deploys data integration to aggregate disparate data sources across the business
  • Builds custom data mart for specific areas which require special attention
  • Queries historical data to strategize future roadmap with deliverables
  • Cleanses and improves the overall organizational data quality

Tarams develops the Data warehousing solution with the following processes and functionalities:

  • Data Mart Development
  • Tools, Portal and Platform Selection
  • Information Design
  • ETL
  • Data Integration
  • Performance Optimization

Tarams also provides service support including maintenance, operation, and data cleansing.

In Summation

Tarams is a leading provider of Business Intelligence services that help businesses turn their data into valuable insights. With the right skills, tools, and expertise, Tarams can analyze and integrate both structured and unstructured data, providing clients with a comprehensive view of their business operations.

One of the core strengths of Tarams is its expertise in Cloud Computing Platforms, including AWS, Google, Azure, IBM, and Oracle. This enables the company to provide cloud-based Business Intelligence solutions that are scalable, secure, and cost-effective.

Tarams’ solution helps structure the chaotic and complex organizational data and helps in developing the right analytical tools and solutions that can augment the decision-making process. With the help of Tarams, businesses can build a robust framework that provides a standardized, robust, and easy-to-use data analytics platform.

The end-to-end data analytics solution offered by Tarams encompasses consulting, engineering, implementation, maintenance, and enhancement services. The company’s approach is designed to deliver measurable business benefits to clients, whether they are looking to improve their customer experience, increase revenue, or optimize their operations.

The company’s Business Intelligence solutions are designed to meet the needs of businesses of all sizes and industries. Whether a business is looking to improve its supply chain management, customer experience, or financial performance, Tarams can help.

In conclusion, at Tarams, the focus is always on delivering value to clients. The company’s team of experts works closely with clients to understand their specific needs and develop tailored solutions that meet their unique requirements. With a commitment to excellence, Tarams is dedicated to delivering the highest quality Business Intelligence services to its clients.

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DevOps and DataOps – An Overview

DevOps and DataOps – How can these approaches optimize your operations?

In recent years, there has been a growing interest in DevOps and DataOps methodologies. These two approaches aim to improve collaboration, automation, and continuous delivery in software development and data management. While there are many similarities between DevOps and DataOps, there are also some key differences that set them apart.

 

In this article, we’ll explore what DevOps and DataOps are, compare their similarities and differences, and provide examples of how they can be used in practice.

What is DevOps?

DevOps is a methodological approach that emphasizes collaboration, automation, and continuous delivery in software development. It’s about bringing the development and operations teams within an organization together for better collaboration, with the goal of reducing the time it takes to move software from development to production.

DevOps is based on a number of principles, including:

  • Collaboration: DevOps emphasizes collaboration between development and operations teams, as well as other stakeholders such as quality assurance (QA) and security teams.

 

  • Automation: DevOps aims to automate as much of the software development process as possible, from testing and deployment to monitoring and reporting.

 

  • Distribution: DevOps encourages distribution models where applications are developed, tested, and deployed on a small scale rather than large, infrequent releases.

 

  • Improved feedback loop: DevOps incorporates feedback loops back into the development process, allowing teams to respond quickly to changes and adapt their approach as needed. 

 

  • Tools: DevOps relies heavily on tools to automate tasks and streamline workflows with a focus on using open-source and cloud-based technologies.

Tarams with its extensive experience in DevOps has been successful in implementing it for numerous clients. Our services gives you an insight into how we can assist in solving any current obstacles you may have.

Key Benefits of DevOps

  • Faster time to market: With an emphasis on automation and distribution, DevOps enables teams to get applications into production faster.

 

  • Improved Collaboration: By splitting silos between different teams, DevOps improves communication and collaboration, which leads to better results.

 

  • Better quality software: By integrating feedback loops and testing into the DevOps development process, it helps teams catch bugs and bugs that lead to better quality software.

 

  • Greater efficiency: By automating tasks and streamlining DevOps workflows, reducing manual labor and increasing efficiency, increasing time for more important tasks.

Scenarios for DevOps

  • Continuous Integration and Deployment (CI/CD): DevOps can be used to automate the process of building, testing, and deploying code changes to production. With this approach, developers can release new features and bug fixes more frequently and with less risk, while operations teams can ensure the stability and reliability of the system.

 

  • Infrastructure as Code (IaC): DevOps can be used to manage infrastructure as code, which means defining and provisioning infrastructure resources such as servers, databases, and networking components using code. This approach helps to ensure consistency and repeatability in the infrastructure deployment process and makes it easier to scale and maintain the infrastructure.

 

  • Monitoring and Alerting: DevOps can be used to implement a comprehensive monitoring and alerting system for applications and infrastructure. This includes collecting and analyzing performance metrics, logging and tracking errors, and setting up alerts to notify teams when issues arise. This approach helps teams proactively identify and resolve issues before they impact users.   

 

  • collaboration and Communication: DevOps can be used to improve collaboration and communication between development and operations teams. This includes implementing tools and processes for sharing code, documentation, and knowledge, as well as fostering a culture of collaboration and mutual respect.

 

  • Security and Compliance: DevOps can be used to ensure that applications and infrastructure meet security and compliance requirements. This includes implementing security best practices such as code reviews, vulnerability scanning, and penetration testing, as well as ensuring compliance with regulations such as HIPAA, PCI-DSS, and GDPR.

What is DataOps

DataOps is a way of applying DevOps principles to data processing. It’s about bringing together different teams involved in data management, including data engineers, data scientists, and data analysts, for greater collaboration and automation of the data management process.

DataOps is based on a number of principles, including:

  • Collaboration: DataOps emphasizes collaboration between business units involved in data management, with a focus on breaking down silos and improving communication.

 

  • Automation: DataOps aims to automate the data management process as efficiently as possible, from data processing and processing to analysis and reporting.

 

  • Continuous integration and delivery: DataOps promotes a continuous integration and delivery process, where data is processed, analyzed, and delivered in small increments rather than in large batches in which they are rarely done.

 

  • Quality and governance: DataOps adds quality and governance to the data management process, ensuring that the data is accurate, reliable, and secure.

 

  • Tools: DataOps relies heavily on tools to automate tasks and streamline workflows, with a focus on using open-source and cloud-based technologies.

Key Benefits of DataOps

  • Faster time to insights: By emphasizing automation and continuous delivery, DataOps enables teams to get insights from data more quickly.

 

  • Improved collaboration: By breaking down silos between different teams, DataOps improves communication and collaboration, leading to better outcomes.

 

  • Better quality data: By incorporating quality and governance into the data management process, DataOps helps teams ensure that data is accurate, reliable, and secure.

 

  • Greater efficiency: By automating tasks

Scenarios for DataOps

  • Data Pipeline Management: DataOps can be used to manage data pipelines that move data from source systems to target systems. With DataOps, you can automate data ingestion, cleansing, and transformation to ensure that data is properly managed and of high quality.

 

  • Data Governance and Compliance: DataOps can be used to ensure that data is properly governed and meets compliance requirements. This includes implementing data access controls, managing data lineage, and ensuring data privacy and security.

 

  • Data Quality Management: DataOps can be used to manage data quality and ensure that data is accurate, complete, and consistent. This includes setting up data quality checks, monitoring data quality, and identifying and resolving data quality issues.

 

  • Analytics and Reporting: DataOps can be used to manage analytics and reporting systems, ensuring that data is properly aggregated, analyzed, and visualized. This includes setting up analytics pipelines, managing data models, and ensuring that insights are accurate and actionable.

 

  • Collaboration and Communication: DataOps can be used to improve collaboration and communication between data engineering, data science, and business teams. This includes implementing tools and processes for sharing data, documentation, and knowledge, as well as fostering a culture of collaboration and mutual respect.

In Conclusion

DevOps and DataOps are two methodologies that aim to improve collaboration, automation, and continuous delivery in software development and data management, respectively. While there are many similarities between DevOps and DataOps, they are designed for different tasks.

DevOps is best suited for software development, where the focus is on building and delivering software in a more efficient and reliable manner. It emphasizes collaboration, automation, and continuous delivery, enabling teams to get software into production more quickly.

DataOps, on the other hand, is best suited for data management, where the focus is on processing and analyzing data in a more efficient and reliable manner. It emphasizes collaboration, automation, and continuous integration and delivery, enabling teams to gain insights from data more quickly.

Both methods have their strengths and can be useful to organizations in different ways, depending on their specific needs and goals.

Data Engineering Team

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4 Technology Trends Poised To Skyrocket in 2023

What are Technology Trends?

Technology trends refer to the direction or pattern of technological advancements in a particular field or industry over time. These trends can include emerging technologies, new or evolving applications of existing technologies, changes in user behaviors and expectations, and shifts in the competitive landscape.

It is difficult to predict the exact technology trends in 2023, as the technology landscape is constantly evolving and new advancements are being made. However, there are a few areas that we predict might see a lot of traction and attention in 2023

In this blog, we will explore these trends and how we at Tarams, an IT solutions company can help.

Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly advancing fields that have the potential to transform many industries and have a significant impact on our daily lives. In 2023, we can expect to see continued growth and development in AI and ML, with new applications and use cases emerging in a wide range of industries. This field is likely to top technology trends in 2023.

One area where AI and ML are expected to make a significant impact is in Healthcare and its Services. AI-powered solutions have the potential to revolutionize the way healthcare is delivered, from early diagnosis of diseases to personalized treatment plans. For example, AI-powered tools can help medical professionals analyze vast amounts of medical data to make more informed decisions about patient care. In 2023, we can expect to see AI and ML being increasingly used in areas such as image analysis, drug discovery, and predictive analytics.

Another industry that is expected to be significantly impacted by AI and ML is Financial Services. AI-powered solutions have the potential to improve many aspects of financial services, such as fraud detection, risk management, and customer service. In 2023, we can expect to see more financial institutions investing in AI and ML solutions to streamline their operations, reduce costs, and improve the customer experience.

In the Retail Industry, AI and ML are expected to play a big role in optimizing supply chain management and personalizing the shopping experience for customers. For example, retailers can use AI-powered solutions to analyze customer data and make personalized product recommendations, and also use ML algorithms to optimize their supply chains and improve the accuracy of demand forecasting.

The Manufacturing Industry is another area where AI and ML are expected to have a significant impact in 2023. AI and ML can help manufacturers improve their operations by reducing waste, optimizing production processes, and increasing efficiency. For example, AI-powered solutions can help manufacturers monitor their production lines in real-time and identify areas for improvement, and ML algorithms can be used to optimize production schedules and reduce downtime.

The Transportation Industry is also expected to be impacted by AI and ML in 2023. AI-powered solutions have the potential to improve many aspects of transportation, such as route planning, traffic management, and autonomous vehicles. For example, AI-powered systems can be used to optimize route planning and reduce congestion, and self-driving cars powered by ML algorithms can improve safety and increase efficiency on the roads.

In 2023, we can also expect to see AI and ML being increasingly used in areas such as education, energy, and entertainment. AI-powered solutions have the potential to revolutionize the way we learn and improve access to education, and also help optimize energy production and distribution. In the entertainment industry, AI and ML can be used to create more personalized experiences for consumers and improve the way content is produced and distributed.

AI and ML are rapidly advancing fields that have the potential to transform many industries and have a significant impact on our daily lives. New applications and use cases emerging in a wide range of industries. While AI and ML present many opportunities, it’s also important to consider the ethical and social implications of these technologies and to ensure that they are developed and used in a responsible and sustainable manner.

Tarams with our Product Engineering and Data Science & Engineering Services have been extensively working with AI and ML from the nascent stages of the technology. Our earlier work in Sentiment Analysis gained a lot of traction and was widely circulated.

5G Technology

The roll-out of 5G networks is expected to accelerate in 2023, and IT companies across the globe may want to focus on developing solutions that leverage the high-speed, low-latency capabilities of 5G.

5G is the fifth-generation wireless network technology that promises to revolutionize the way we use the internet and connect to devices. In 2023, we can expect to see significant growth and development in the 5G space, as more and more countries roll out 5G networks and a growing number of devices and applications become 5G-enabled. Though the trend has been ongoing, we predict a stellar rise in 5G being a strong contender to top the technology trends in 2023

One of the key benefits of 5G is its speed and low latency, which have the potential to transform many industries and improve the way we live and work.

For example, in 2023, we can expect to see more businesses and consumers adopting 5G-enabled devices, such as smartphones, tablets, and laptops, that offer faster download and upload speeds and improved video streaming quality.

Additionally, 5G has the potential to support a growing number of IoT devices, such as smart homes, connected cars, and industrial IoT, allowing them to communicate and share data faster and more efficiently.

Tarams has been active in the Mobility Solutions segment for over a decade and some of our use cases speak volumes about our effective solutions for enabling Products to Scale in the Mobility space.

Blockchain Technology

Blockchain is a relatively new technology, but it has already found applications in many industries, such as Finance, Supply Chain, and Healthcare. Blockchain Technology has been gaining popularity over the last few years due to its Secure, Decentralized, and Transparent nature.

Here are some advancements in blockchain technology that we may see in 2023 ensuring Blockchain Technology to be a contender in technology trends in 2023:

Increased Scalability: One of the most significant challenges of blockchain technology has been its limited scalability. Currently, most blockchain networks can only handle a small number of transactions per second. However, with advancements in technology, it is expected that blockchain networks will become more scalable and capable of processing thousands of transactions per second, making it more feasible for use in real-world applications.

Enhanced Privacy Features: Privacy has been a significant concern for blockchain technology, as all transactions are recorded on a public ledger. However, advancements in technology are expected to improve privacy features and make it possible to conduct confidential transactions while still maintaining the benefits of a decentralized network.

Integration with other technologies: Blockchain Technology has the potential to be integrated with other technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI). These integrations could enable the creation of innovative solutions for various industries and further expand the use cases for blockchain technology.

Increase in adoption by businesses: As blockchain technology continues to mature and become more accessible, it is expected that more businesses will adopt it. Blockchain technology can offer significant benefits to businesses, such as increased security and efficiency, which can lead to cost savings and improved operations.

The emergence of new blockchain-based applications: The growing adoption of blockchain technology is likely to lead to the emergence of new applications that utilize blockchain in innovative ways. Some potential applications include decentralized finance (DeFi), supply chain management, and voting systems.

Blockchain technology is continuously evolving, and the advancements we may see in 2023 are likely to make it more scalable, private, and accessible. With the growing adoption of blockchain technology, we can expect to see new and innovative applications emerge, and improved regulations to ensure that it is being used in a legal and ethical manner.

Tarams has worked on a few projects that were focused on improving their advent into Blockchain Technology and we will be more than happy to share the use cases and help you better implement the technology to scale your business and catching up on the technology trends in 2023.

Cloud Computing

Cloud computing is the practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than using local servers or personal computers. It has been a game-changer in the world of technology, and its importance is only expected to grow in the coming years. Here are some reasons why cloud computing is expected to be important and make it to the top technology trends in 2023:

Cost-Effective: One of the most significant advantages of cloud computing is its cost-effectiveness. Businesses can save money by only paying for the resources they need, rather than investing in and maintaining costly physical infrastructure. Additionally, with the use of cloud services, businesses can avoid expensive software and hardware licensing fees.

Scalability: Cloud computing provides the ability to scale up or down resources quickly, depending on the needs of the business. This means that businesses can quickly respond to changes in demand and scale up or down as needed, without the need for significant infrastructure changes.

Accessibility: Cloud computing allows users to access data and applications from anywhere with an internet connection. This makes it easier for businesses to work remotely, and it also makes it easier for employees to access their work from anywhere.

Collaboration: Cloud computing facilitates collaboration among teams, whether they are in the same office or spread out across the world. This is because cloud-based applications provide real-time access to data and enable team members to work on the same document simultaneously.

Security: Cloud computing providers have security measures in place to protect data, making it more secure than traditional methods of data storage. Additionally, with the use of the cloud, data can be backed up and stored offsite, reducing the risk of data loss due to natural disasters or other unforeseen events.

Machine Learning: Cloud computing can enable the use of machine learning (ML) and artificial intelligence (AI) technologies, which can provide businesses with insights that were previously impossible to obtain. By analyzing large amounts of data, businesses can gain a better understanding of their customers, improve their products or services, and optimize their operations.

Big Data: The amount of data being generated is growing at an unprecedented rate. Cloud computing provides the ability to store, manage and process this data, making it easier to analyze and extract insights from it.

Businesses that embrace cloud computing will have a competitive advantage, as it allows them to quickly adapt to changing business needs and take advantage of new opportunities. As a result, the demand for cloud-based solutions is expected to grow, and businesses that fail to adopt cloud computing may be left behind.

In Conclusion

All of this is well and good; but the key factor for any startup, enterprise, or organization’s success and growth is finding the right partner who enables them to do so with ease. A partner who will help you ride the technology trends in 2023.

Tarams is the right choice for a startup, enterprise, or organization looking to scale their existing business or start a venture or even do a parallel shift into an unknown domain.

Our team of engineers, designers, entrepreneurs, and technologists are always looking for challenges that enable them to put their best foot forward in achieving the customer’s vision while ensuring the right technology is being efficiently utilized.

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Data Science And Data Analytics for Revenue Generation

Introduction

Data science and Data Analytics play a critical role in revenue generation for modern businesses. The ability to collect, process, and analyze large amounts of data is essential for understanding customer behavior, identifying sales trends, and making data-driven decisions.

What is Data Science?

Data Science is the process of extracting insights and knowledge from data using a variety of techniques. These include statistical analysis, machine learning, and data visualization among others. This allows businesses to make sense of large and complex data sets and make informed decisions.

What is Data Analytics?

Data analytics is the process of examining, cleaning, transforming, and modeling data. This is used in discovering useful information, informed decision-making, and supporting business operations.

Revenue Generation

One of the critical avenues Data Science and Data Analytics is used to drive revenue is through customer segmentation and targeting. By analyzing customer data; businesses can identify patterns and trends that reveal which segments of their customer base are most valuable.

They can then use this information to develop targeted marketing campaigns and sales strategies that are more likely to resonate with these customers.

Another important application of Data Science and Data Analytics is in the area of pricing optimization. By analyzing data on customer buying habits, businesses can determine the optimal prices for their products and services. This can lead to increased sales and revenue, as well as improved customer loyalty.

Data science and Data Analytics can also be used to improve supply chain management. By analyzing data on inventory levels, supplier performance, and demand patterns, businesses can optimize their operations and reduce costs. This can result in increased efficiency and higher profit margins.

In addition, it can also be used to improve the customer experience. By analyzing data on customer interactions, businesses can identify areas where the customer experience can be improved. They can then use this information to make changes that will lead to increased customer satisfaction and loyalty.

Yet another scenario is to improve risk management. By analyzing data on past events, businesses can identify patterns and trends that indicate potential risks. They can then use this information to develop strategies to mitigate these risks and protect their revenue streams.

Finally, Data Science and Data analytics can also be used to improve business intelligence and decision-making. By analyzing data on sales, marketing, and financial performance, businesses can identify patterns and trends that indicate areas for improvement. They can then use this information to make data-driven decisions that will improve their bottom line.

Conclusion

Data Science and Data Analytics are essential for revenue generation in today’s business environment. They provide businesses with the insights and information needed to understand customer behavior, identify sales trends, and make data-driven decisions.

By leveraging this, businesses can improve their customer targeting, pricing, supply chain management, customer experience, risk management, and decision-making, which can lead to increased sales and revenue.

Tarams’ Data Engineering team has always understood the value and core strength of Data. We have been the founding partners with some of the big names in the industry, and this experience has enabled us to design and execute services suitable for enterprises and startups over the past two decades.

Data Engineering Team
Tarams Software Technologies Pvt Ltd.,

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Google OAuth Review Process – for Restricted Scopes

What is OAuth ?

OAuth (Open Authorization) is an open standard authorization framework for token-based authorization on the internet. It enables an end user’s account information to be used by third-party services, such as Facebook and Google, without exposing the user’s account credentials to the third party.

Google OAuth Review Process

You are likely to receive an email as depicted here if you are an API developer.
The process can be broadly divided into two phases:

1. The OAuth review process
2. The security assessment

If your app accesses Gmail’s restricted scopes, you have to go through both these phases. More details here

1. The OAuth review process

It starts with initiating the review process on your Google Developer Console. You will have to go through a questionnaire which is mostly about helping Google understand the usage of the restricted scopes in your app. You only have to do this for the production version of the app. Lower environments can be marked as “internal” and they need not go through this process.

After you initiate the review, Google’s security team will reach out to you requesting a YouTube video to demonstrate the usage of restricted scopes in your app. Once you share the video, Google will either respond with an approval or a feedback email requesting more information/changes. We had some feedback from Google and we had to share a couple of videos before we got an approval from Google.

Listed below are a few pointers which might help you to reduce feedback from Google.

Google usually takes a long time to respond in this regard. Despite multiple follow ups we had to wait for a month or two to get response for some of these emails – Possibly because they had a lot of requests from app developers during that time.
Also, in general we felt there was some disconnect in their responses as it looked like every response from our end was reviewed by a different person at Google – we received an email stating that we have missed the deadline for initiating the security assessment weeks after we had initiated the process. However, Google did acknowledge the mistake on their end after we responded with the SOW that was already executed.

  • Follow the design guidelines given by Google for styling the sign in button https://developers.google.com/identity/branding-guidelines#top_of_page
  • Have a web page for your app which people can access externally, without having to sign in.
  • Ensure that users have access to your Privacy policy page from your home page. A link to this should be given on sign in and users should only be allowed to proceed on accepting the privacy policy.
  1. While recording the video, go through the “privacy policy” on sign in and demonstrate that users need to accept it before proceeding.
  2. Your policy should explicitly quote the use of all restricted scopes.
  3. The policy should also mention how and why the restricted scopes are being used. Who has access to this data and where is it stored? Can it be viewed by your support staff or it’s just used by the app and humans cannot access it.
  • While recording the video try to capture as much details as possible demonstrate the usage of Google’s restricted scope within your app.
  1. Code walkthrough wherever necessary Ex. Fetching OAuth token and its use
  2. Demonstrate the storage of sensitive data and usage of encryption

If Google is satisfied with all the details about your app and is convinced that your project is compliant with their policies, you will get an approval mail. You will also be informed if your app has to undergo a security assessment as depicted.

2. Security Assessment

The security assessment phase relatively involved more live discussions and meetings with the assessors and therefore the overall process is quicker. You have a dedicated team assigned to help you. Google gave us the contacts of 2 third-party security assessors.We reached out to both of them and felt that ‘Leviathan’ was better in terms of communication. They shared more information about the overall process and we were more comfortable going ahead with them.We had to fill in and sign a few documents before we got started, which involved

  • Filling up an SAQ(Self assessment questionnaire) – to understand about the app and the infrastructure.
  • Signing the SOW
  • Signing a mutual NDA

After which we made the payment and got started with the process. We had an initial introduction meeting where we were introduced to their team and our assessment process was scheduled. To give you a rough idea, our schedule was about 2 months after we had the initial discussions.As per the SOW, the assessment would include the following targets. These would possibly differ based on individual applications and the usage of the restricted scopes. For reference, our’s was an iOS app.

  • Website
  • RESTful APIs
  • Mobile Application (iOS)
  • External Facing Network
  • Developer Infrastructure
  • Policy & Procedure Documentation

The assessor would retest after we complete resolving all the vulnerabilities. The first retest is included in the SOW and additional retests are chargeable.The timeline we had before Google’s deadline was pretty tight and we wanted to understand from the assessor if we can do anything to increase our chances of getting it right on the first pass. The assessors were kind enough to share details about some of the tools they use for the penetration testing so that we could execute them ahead to understand where we stand and resolve as much as possible before the actual schedule.

Preparation for the assessment

As part of preparation for the assessment, you can use these tools which help you identify the vulnerabilities with your application and infrastructure. Also, ensuring that you have some basic policy documentation will save you some time.

Scoutsuite – It’s an open source multi-cloud security-auditing tool. You can execute this on your infrastructure. It will generate a report listing out all the vulnerabilities. Resolving as many as you can before the assessment would surely help.

Burpsuite – Burpsuite is not open source but you can either buy it or use the trial version. It’s a vulnerability scanner which scans all the API endpoints for security vulnerabilities. Executing Burpsuite and taking care of vulnerabilities marked as High or more will help significantly before going through the assessment. It’s recommended to run Burpsuite on your lower environments and NOT on production because Burpsuite tests every endpoint by calling it more than a thousand times. You will end up creating a lot of junk data on whichever environment you run Burpsuite on.

Policy Documentation – We were asked to share a whole set of documents before the assessment. We already had most of these documentations in place so it was not a problem for us. But, if you don’t have any documentation for your project, it would save some time to have some basic documentation for your project as a preparation. I have listed out a few here:

  • Software Development Guidelines
  • Network diagrams
  • Information security policy
  • Risk assessment policy
  • Incident response plan

We reached out to both of them and felt that ‘Leviathan’ was better in terms of communication. They shared more information about the overall process and we were more comfortable going ahead with them.

We had to fill in and sign a few documents before we got started, which involved

  • Filling up an SAQ(Self assessment questionnaire) – to understand about the app and the infrastructure.
  • Signing the SOW
  • Signing a mutual NDA

 

After which we made the payment and got started with the process. We had an initial introduction meeting where we were introduced to their team and our assessment process was scheduled. To give you a rough idea, our schedule was about 2 months after we had the initial discussions.

As per the SOW, the assessment would include the following targets. These would possibly differ based on individual applications and the usage of the restricted scopes. For reference, our’s was an iOS app.

  • Website
  • RESTful APIs
  • Mobile Application (iOS)
  • External Facing Network
  • Developer Infrastructure
  • Policy & Procedure Documentation

The assessor would retest after we complete resolving all the vulnerabilities. The first retest is included in the SOW and additional retests are chargeable.

The timeline we had before Google’s deadline was pretty tight and we wanted to understand from the assessor if we can do anything to increase our chances of getting it right on the first pass. The assessors were kind enough to share details about some of the tools they use for the penetration testing so that we could execute them ahead to understand where we stand and resolve as much as possible before the actual schedule.

Actual penetration testing from the assessor

The assessor initiated the process as per the schedule. The first thing they did was create a slack channel for communication with our team and theirs. We had to share with them the AppStore links, website details and necessary credentials for our infrastructure. They also shared a sharepoint folder for sharing all the documentation and reports. We started uploading all the necessary documents and in parallel they started the penetration testing and reviewing our infrastructure. Again, do NOT share the production environment for penetration testing as it will create a lot of junk data and may delete existing entities.

After two days of testing they shared an intermediate report and we started addressing the vulnerabilities. After about a week we got the final report of the vulnerabilities. We addressed all the vulnerabilities and shared the final report. Here are a few remediations that were suggested for us:

  • We had to add Contact details for users in our web page to report vulnerabilities
  • Enable Multi Factor authentication on our AWS logins
  • Requested for logs around Google OAuth token usage
  • Encryption on RDS, EBS volumes
  • Documentation demonstrating KMS(Key management system) usage.

Upon completion of the assessment, the assessor will provide a document containing the following components:

  • Executive summary, including a high-level summary of the analysis and findings and prioritized recommendations for remediation
  • A brief description of assessment methodologies.
  • A detailed discussion of analysis results, including relevant findings, risk levels, and recommended corrective action.
  • Appendices with relevant raw data, output, and reports from the analysis tools used during the engagement.

That was the end. Couple of days after the approval from the assessor, we got an approval email from Google.

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Sentiment Analysis

A study on implementation towards resolving IT tickets

Need for Sentiment Analysis


The Internet today is used widely to express opinions, reviews and comments among other things. These are primarily expressed on various topics such as current affairs, social causes, movies, products, friend’s pictures, etc.

 

All these opinions, reviews and comments implicitly express a sentiment that the author was feeling at the time of its expression. These sentiments range from happiness, positivity, support to anger, disdain and sadness.

Studying and analyzing these sentiments are necessary for certain individuals or groups, particularly individuals or groups about whom these opinions are being expressed. This involves going through all the comments, reviews, and opinions to gather the information to study and analyze. Physically sifting through all the messages, comments and reviews is a laborious process and there are tools available which eases the burden albeit inefficiently.

This in brief is one of the needs for Sentiment Analysis

IT Tickets


Tickets, in this context – IT Tickets, or ‘Support Requests’ are generated daily across the globe in any organization which has a customer support system in place. These are mainly to resolve the various glitches, errors or downtimes experienced when using devices that are digitally connected. The tickets or requests contain, depending on the service provider, simple fields to report a problem with minimal words and/or screengrabs/screenshots. The tickets or requests are then put through the customer support system, and they go through resolution based on the process dictated by the organization.

In any customer support system, these requests are sorted and analyzed and resolved based on the process in place. A quick turnaround in resolving the ‘support requests’ is important to retain the customer base, as a happy customer is more likely to stick to a service than an unhappy one.

Sentiment Analysis for IT Tickets

This study documents our efforts in implementing ‘Sentiment Analysis’ to sort IT tickets in any organisation to achieve faster turnaround time and quicker resolution

Our Approach

The Support requests or IT Tickets come with various requests ranging from simple to complex. They also carry a variety of emotions ranging from mild annoyance to severe discontent. A mechanism to address ‘priority’ requests is essential to ensure that customers who are very unhappy or distressed be attended prior to others. Ensuring this priority and resolving these issues is proportional to the retention of the customer base.

In analyzing the different solutions available currently, Sentiment Analysis by virtue of its approach, stood out for detecting these ‘priority’ IT tickets and was used to achieve the desired results. The process was carried out without any human interaction, hence the quick detection of ‘unhappy’ or ‘priority’ requests resulted in a quick turnaround time for resolving the tickets.

Sentiment Analysis

Sentiment Analysis deals with identifying the hidden sentiment (positive, negative or neutral emotion) of a comment, review or opinion. It is extensively used these days to understand how the general populace is feeling about a movie, a product or an event.

Identifying ‘Sentiments’


IT Ticket comments come with descriptions that are usually short and sometimes precise. The ‘objective descriptions’ of these comments seem inherently negative, but are neutral in IT Support context.

A typical example is – “The program is throwing up an error“. This statement does not necessarily emote any sentiment. The challenge lies in ignoring the objective parts of the comment and concentrate on the ‘sentiment’ part expressed in the ‘subjective part’ of the IT Ticket. A typical example for that is – “This is terrible and I am frustrated”.

Segregation based on the above theory entails a complete understanding of the product and service for which the IT tickets are being raised. This understanding enables us to identify the words, phrases and sentences that are being used to describe the ‘undesirable behavior’ or ‘malfunctioning’ of the product or service.

This, to a layman, appears very straightforward and simple, but in reality poses a serious challenge in distinguishing between the objective and subjective parts of the issue or comment.

Choosing the RIGHT approach


The approach that we chose had to be able to work on the type and amount of data we had. It also needed to be easily tunable in the future. There are two popular approaches to implement Sentiment Analysis,

Machine Learning Based


In this approach, we needed to generate a vector representation of each comment and train a model with this vector as the ‘feature vector’ and the ‘sentiment’ of the comment as the target. The trained model then would predict the sentiment polarity score of a new comment feature vector, which is then fed into the model.

Keyword Based

Here we looked for keywords and assigned sentiment scores to the text, based on the sentiment values of the keywords.

We did not use the machine learning based approach as we did not have access to ample number of comments for the training; we were able to access only around 9000 comments of which very few – below 50, were manually classified as negative.

We chose to go with the Keyword Based Approach.

Keyword Based Approach


In the keyword based approach, we used a NLTK based library which assigned sentiment polarity scores ranging from -1 (most negative) to +1 (most positive) for pieces of text.

We filtered comments to remove artefacts like personal details, URLs, email addresses, logs and other metadata since these do not have any sentiment value. The text of the filtered comment was then used in the scoring process. If any sentence had a non-alphabet content greater than 25%, then it was not taken into consideration while scoring. Such sentences usually do not contribute to the sentiment polarity of the text – due to the texts usually not being dictionary words. For example, the snippet of code: C = A + B

We took a granular approach when assigning sentiment scores to text as we specifically wanted to ignore parts of text which seemed inherently negative, but were neutral in the IT support context. The approach we used was to divide each sentence in a comment into ‘Trigrams’.

A trigram is a window with just three consecutive words. This window was slid over the words in the sentence to identify constituent trigrams. We manually went through a large collection of comments and came up with trigrams which should not be assigned a sentiment value, in the IT support context.

Any such trigrams which showed up in a sentence were ignored. The remaining trigrams were scored and we took into consideration only trigrams which had a sentiment score that was significantly different from Zero. Also, if a sentence had less than three words in it, then a sentiment value for that sentence was calculated directly and if that score was significantly different from zero, it was used for calculating sentiment score for the comment. We also manually came up with a list of such sentences that we should ignore.

In adjacent trigrams with overlapping tokens; if their scores hadn’t changed much and the score for the common part contributed the vast majority of the trigram score, then only the first trigram’s score was taken into consideration. For example, let us consider the sentence “it is frustrating to have to go over this again.” Here, the trigrams “it is frustrating”, “is frustrating to” and “frustrating to have” all have a sentiment score of -0.4 and the word ‘frustrating’ alone contributes to that score. So, we just took into consideration the score for the first trigram above and ignored the other two.

All trigrams which satisfied the conditions mentioned above were collected along with their scores. If there are no such trigrams, a sentiment score of zero was assigned to the comment. If there were any such trigrams, we then checked to see if there were one or more trigrams with a score less than or equal to a threshold value. If yes, then we took the mean value of the sentiment value of all trigrams with a sentiment score value less than or equal to the threshold and assigned this value as the sentiment score of the comment. This was done as part of an effort to aggressively go after negative comments.

Here, xi represents a sentiment score and the angle brackets denote mean value.
If there were no trigrams with a score of less than or equal to the threshold, then we took the weighted average of all the sentiment values of all the scored trigrams and assigned this value as the sentiment value for the comment. The weights were chosen in such a manner that for negative scores the weight was greater than 1 and increased as the score decreased and for positive scores the weight was less than 1 and decreased as the score increased

Here xi denotes the sentiment score for a trigram and wi denotes its weight

Results


To evaluate the approach/algorithm, we needed to ensure that the comments were categorised correctly according to their sentiment. “Precision” and “Recall” are two standard pointers in analysing such results. We created a gold standard set of comments consisting of a small balanced set of negative and non-negative comments. Here, ‘Precision’, is the fraction of comments that are actually negative out of the comments which are classified as negative. The closer the precision is to 1, the fewer the number of false negatives compared to the number of true negatives. ‘Recall’ is the fraction of negative comments which are classified correctly out of the total number of negative comments. The closer the recall is to one, the higher the fraction of negative comments which are classified as negative.

Here TN represents true negatives, i.e. comments which are negative and are classified as negative. FN represents false negatives, i.e. comments which are not negative but which are classified as negative. FP represents false positives, i.e. comments which are negative, but are classified as non-negative.

The results are as displayed below


The reason why the precision is so small on the second set is that the vast majority of tickets are non-negative and there will be a certain percentage of these which are wrongly marked as negative by our algorithm. This number is large compared to the number of negative comments which are marked as negative.

We also ran the raw comments through the library we used for Sentiment Analysis and got the following results:

So, we can see that our algorithm involving trigrams and assigning scores according to the above mentioned procedure vastly improve the Sentiment Polarity Prediction process over assigning scores to the raw comments all at once.

Conclusion


We came up with a commendable method to ignore the objective parts of an IT support ticket by coming up with a list of text snippets which did not typically have a sentiment value in the IT support ticket context. The current method of assigning sentiment scores is lexicon based and relies on keywords to which a sentiment score is attached. This might not be able to pick up subtle ways of expressing negative sentiment which a human reader would easily pick up.
Machine learning methods would pick up most of such expressions. But, we, unfortunately, did not have enough data or a balanced set to run supervised learning algorithm
We managed to get a good performance out of a tool which was not explicitly developed to deal with the difficult task of separating out the objective and subjective parts of an IT support ticket and then assign a sentiment score to it.

Conclusion

Jyothish Vidyadharan

Jyothish is an ML Engineer working with Tarams for over 5 years. He is passionate about technology and coding

Babunath Giri T

Babu is an engineer who’s managerial skills have been helping Tarams tackle projects and clients with much success. He has been a part of Tarams for over 3 years.

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Big Data for a huge change

Today, millions of users click pictures, make videos, send texts, and communicate with each other through various platforms. This results in a huge amount of data that is being produced, used and re-used everyday.

In 2013, the total amount of data was 4.4 zettabytes. This is likely to increase towards 44 zettabytes by 2020 (One zettabyte is equivalent to 44 trillion gigabytes)

All of this ‘Data’ is a precious resource, which can be harnessed and understood by deploying certain techniques and tools. This is the gist of Big Data and Data Analytics. Using Big Data and Data Analytics, many organizations are able to gain insights into the customer mindsets, trending topics, imminent next Big things, etc.,

Let us take a look at how Big Data Applications has influenced various industries and sectors, and also the ways in which they are benefited from the same.

Education

The education industry is required to upkeep and maintain, a significant amount of data regarding faculties, courses, students and results. Requisite analysis of this data can yield insights that enhance the operational efficiency of the educational institutions. This can be put to avail in numerous ways.

Based upon a student’s learning history, customized schemes can be put into place for him/her. This would enhance the student results in entirety. Similarly, the course material too can be reframed based upon what students learn quicker, and the components of the course material that are easier to grasp. As a student’s progress, interests, strengths, and weaknesses are grasped in an improved manner, it helps suggest career paths most lucrative for him.

Healthcare

Healthcare industry generates a significant amount of data and Big Data helps the industry make a prediction for epidemic outbreaks in advance. It may also help postulate preventive measures for such a scenario.

Big Data may help with the prediction of disorders at an early stage, which can act as a preventive measure against any further deterioration, and makes the treatment more effective as well.

Government

Governments of all nations come across a significant amount of data every day, as enabled by sources such as the various databases pertaining to their citizens and geographical surveys.

By putting Big Data Analytics to the best avail, the Governments can come to recognize the areas that are in need of immediate attention. Similarly, challenges such as exploitation of energy resources and unemployment could be dealt with better. Centering down upon tax evaders and recognizing deceit becomes easier as well. Big Data also makes occurrences of food-based infections easier to determine, presume, and work upon.

Transportation

There are various ways in which Big Data makes transportation more efficient and easier, and the technology withholds a vast potential in the field.

As an example, Big Data can be used to access commuters’ requirements of different routes and can help implement route planning which reduces the waiting times. Similarly, traffic congestion and patterns can be predicted in advance, and accident-prone areas can be identified and worked upon in a suitable manner.

Uber is a brand that puts Big Data Analytics to avail. They generate the data about their vehicles, each trip it makes, the locations and drivers. This can be used for making predictions about the demand and availability of cabs over a certain area.

Banking

Data in the banking sectors are huge and enhances each day. With a proper analysis of the same, it is possible to detect fraudulent activities such as misuse of debit or credit cards or money laundering. Big Data Analytics help with risk mitigation and bring business clarity.

As an example, Bank of America has been using SAS AML for over the past 25 years. The software is based upon data Analytics and is intended towards analysing customer data and identifying suspicious transactions.

Weather patterns

Weather satellites and sensors are located across the globe and collect a significant amount of data, which is then used to keep a tab on weather and environmental conditions as well. By use of Big Data Analytics, the data can be used for weather forecast and understanding the patterns of natural disasters in a better way. It can also come across as a resource for studying global warming.

The Governments can put in efforts in advance towards preparing themselves in the event of a crisis. It may even help determine the metrics related to the availability of drinking water across geographies.

Media and entertainment

People own and have access to digital gadgets that they use to stream, view, and download videos and entertainment based applications. This significant amount of data generated can be harnessed and some of the prime advantages that can be derived from putting this data to the best possible avail involve making a prediction of audience taste and preferences in advance. This can be further used towards making sure that scheduling of media streams is optimized or on-demand.

The data can also be used to study customer reviews and figuring out the factors that don’t delight them. Targeting advertisements over media become easier as well.

As an example, Spotify is a provider of on-demand music and uses Big Data Analytics to analyse data collected from the users across the globe. The data is then used to give some fine recommendations for a user to choose from. This is based upon the user’s browsing history and the most preferred videos seen by users of the same geographical region or the same demographics.

In terms of Big Data, it is important that the organizations are able to use the data collected to their best advantage in order to gain a competitive advantage. Merely a collection of the data is not enough.

In order to ensure efficient use of Big Data, Big Data solutions make the analysis easier. Application of Big Data expands further still to fields such as aerospace, agriculture, sports and athletics, retail and e-commerce.

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Insight into Big Data Trends and Future

Just at the start of the century, we came across a number of technologies such as wireless, web access and relational databases coming into more prominence than ever before. Analysis of huge databases came across as a challenge that was very real. The entire practice required a name.

It was in July 2013 that the name Big Data was adopted by the Oxford English Dictionary. But Big Data essentially is a term that has been around for a significant period of time.

Big Data essentially refers to data-sets that a very large and sometimes complicated to be processed by traditional forms of data processing.

With the advent of IoT and Mobile Technologies, Big Databecame more popular. This was aided by people using their digital devices and generating a significant amount of data, such as their geolocations, messages, images, videos, documents, etc., by using various applications to do so.

Big Data evolved to be known as a term for gathering, analyzing and using significant amounts of data in order to improve business operations. This data is growing at a rapid pace as more and more applications are becoming real time. In order to keep pace with the developments, Big Data and the processes are taking huge leaps in adapting to technology.

We live in a world where digital transactions are of great importance. A consumer is on a lookout for instant gratification. Things happen instantly, such as digital sales, providing feedback and making improvements as well. Correspondingly, a significant amount of data is produced as well. Putting this information to a requisite avail in real time offers access to the target audience. If a business fails to accomplish the same, the audience may move on to another brand. Here are some of the ways in which Big Data can transform an organization.

Business intelligence is the term used to define application and analysis of Big Data. It gives a competitive edge for business. By use of Big Data, difficult areas of operation and the most lucrative avenues and times for sales can be defined in advance. An organization can shape up its strategies accordingly.

By a deeper analysis of interactions and understanding the anomalies, certain patterns can be created. Big Data hence brings creative tools and products, which are new to the market.

Let us understand this by using an example.

If a certain appliance sells more than another one in warm weather, it may imply that heated conditions are adding to the sales. It can call for a study for markets most lucrative for sales of the gadget. Similarly, with a marketing campaign, brands can let consumers know about the availability of the gadget in places where it is likely to sell, and highlight it as the best selling product. This works towards boosting benefits to a significant extent.

5 Vs of Big Data

With reference to Big Data, the industry experts associate the 5 Vs. They should each be addressed distinctively in order to understand the effect that they lay on business cycles and profits, and how they interact with other Vs as well.

Volume

Upon dealing with Big Data, it is very important to presume the amount of data an organization is planning to use to gain insights. Similarly, the organization must be sure about where it is planning to store the data, and in what manner.

Variety

An organization must be comfortable in dealing with different types of data, and must be possessed with the right set of tools in order to ingest the information.

Velocity

If the Big Data technologies deliver outcomes fast, they make it easier for a business to put in continuous efforts towards improvement and streamline their work structures in real time. The results should be generated closer to in order to enhance their usability.

Veracity

The data input into the server should be accurate. The bigger picture should be considered in order to make sure that the outcomes are workable.

Value

In order to make sure that Big Data applications deliver actionable results, and a little bit of sorting over data collected too comes into the picture. This is because each of the bits of information collected is not of equal significance.

Role of Big Data Analytics

The essence of Big Datalies in use cases and insights, and not in voluminous data itself. Big Data analytics may be seen as a set of processes that are focused upon the examination of very huge sets of data, which are used to derive patterns which otherwise may not be visible. An analyst comes to discover of correlation, which helps with a prediction of market events before they occur and enables organizations to make corresponding strategies to deal with the events in the best possible way.

Market trends are highlighted by the use of Big Data, and the technology renders a higher degree of clarity for them. As a greater deal of information about customer preferences is derived, it makes way for market insights that can work towards enhancing a business.

An organization that puts applications of Big Data to avail is now positioned to come up with questions and queries. It puts even more insights at a business’s disposal. It is in the form of refined information that holds a potential for a business to get a competitive edge in their operations which makes way for higher profits.

Big Data applications hence come forth as the desired way to define Big Data and enhance the potential for its usability.

As per Industry Experts, Big Data will be placed better in the years to come. While it will be hardly visible and deliver tremendous business value, it won’t call for putting an end to manual labor or influence employment negatively at any level. The risk associated with security and compliance will be mitigated, while automation will allow staff to focus on tasks that deliver value. Big Data may give rise to new ways of working as well. Automation will facilitate even more effective management of Big Data.

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Applications of Big Data Analytics in real life

Today, millions of users click pictures, make videos, send texts, and communicate with each other through various platforms. This results in a huge amount of data that is being produced, used and re-used everyday.

In 2013, the total amount of data was 4.4 zettabytes. This is likely to increase towards 44 zettabytes by 2020 (One zettabyte is equivalent to 44 trillion gigabytes)

All of this ‘Data’ is a precious resource, which can be harnessed and understood by deploying certain techniques and tools. This is the gist of Big Data and Data Analytics. Using Big Data and Data Analytics, many organizations are able to gain insights into the customer mindsets, trending topics, imminent next Big things, etc.,

Let us take a look at how Big Data Applications has influenced various industries and sectors, and also the ways in which they are benefited from the same.

Education

The education industry is required to upkeep and maintain, a significant amount of data regarding faculties, courses, students and results. Requisite analysis of this data can yield insights that enhance the operational efficiency of the educational institutions. This can be put to avail in numerous ways.

Based upon a student’s learning history, customized schemes can be put into place for him/her. This would enhance the student results in entirety. Similarly, the course material too can be reframed based upon what students learn quicker, and the components of the course material that are easier to grasp. As a student’s progress, interests, strengths, and weaknesses are grasped in an improved manner, it helps suggest career paths most lucrative for him.

Healthcare

Healthcare industry generates a significant amount of data and Big Data helps the industry make a prediction for epidemic outbreaks in advance. It may also help postulate preventive measures for such a scenario.

Big Data may help with the prediction of disorders at an early stage, which can act as a preventive measure against any further deterioration, and makes the treatment more effective as well.

Government

Governments of all nations come across a significant amount of data every day, as enabled by sources such as the various databases pertaining to their citizens and geographical surveys.

By putting Big Data Analytics to the best avail, the Governments can come to recognize the areas that are in need of immediate attention. Similarly, challenges such as exploitation of energy resources and unemployment could be dealt with better. Centering down upon tax evaders and recognizing deceit becomes easier as well. Big Data also makes occurrences of food-based infections easier to determine, presume, and work upon.

Transportation

There are various ways in which Big Data makes transportation more efficient and easier, and the technology withholds a vast potential in the field.

As an example, Big Data can be used to access commuters’ requirements of different routes and can help implement route planning which reduces the waiting times. Similarly, traffic congestion and patterns can be predicted in advance, and accident-prone areas can be identified and worked upon in a suitable manner.

Uber is a brand that puts Big Data Analytics to avail. They generate the data about their vehicles, each trip it makes, the locations and drivers. This can be used for making predictions about the demand and availability of cabs over a certain area.

Banking

Data in the banking sectors are huge and enhances each day. With a proper analysis of the same, it is possible to detect fraudulent activities such as misuse of debit or credit cards or money laundering. Big Data Analytics help with risk mitigation and bring business clarity.

As an example, Bank of America has been using SAS AML for over the past 25 years. The software is based upon data Analytics and is intended towards analysing customer data and identifying suspicious transactions.

Weather patterns

Weather satellites and sensors are located across the globe and collect a significant amount of data, which is then used to keep a tab on weather and environmental conditions as well. By use of Big Data Analytics, the data can be used for weather forecast and understanding the patterns of natural disasters in a better way. It can also come across as a resource for studying global warming.

The Governments can put in efforts in advance towards preparing themselves in the event of a crisis. It may even help determine the metrics related to the availability of drinking water across geographies.

Media and entertainment

People own and have access to digital gadgets that they use to stream, view, and download videos and entertainment based applications. This significant amount of data generated can be harnessed and some of the prime advantages that can be derived from putting this data to the best possible avail involve making a prediction of audience taste and preferences in advance. This can be further used towards making sure that scheduling of media streams is optimized or on-demand.

The data can also be used to study customer reviews and figuring out the factors that don’t delight them. Targeting advertisements over media become easier as well.

As an example, Spotify is a provider of on-demand music and uses Big Data Analytics to analyse data collected from the users across the globe. The data is then used to give some fine recommendations for a user to choose from. This is based upon the user’s browsing history and the most preferred videos seen by users of the same geographical region or the same demographics.

In terms of Big Data, it is important that the organizations are able to use the data collected to their best advantage in order to gain a competitive advantage. Merely a collection of the data is not enough.

In order to ensure efficient use of Big Data, Big Data solutions make the analysis easier. Application of Big Data expands further still to fields such as aerospace, agriculture, sports and athletics, retail and e-commerce.

How can we help you?