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Category: Analytics

Current Trends Likely to Shape the Future of Business Analytics

Posted on by Imarticus Learning

 

Business Analytics is the process of organizing a company’s data into a simpler and more understandable form, in order to allow the administration to take a better decision with respect to the growth of the company.

Many individuals, after taking the Business Analytics course in Thane, are employed in multinational companies. They are tasked with providing relevant information, backed with suitable data, to the company’s administration. This makes for better decision making, which in turn, increases the rate of revenue made by the company. Business analytics could also be seen as an iterative investigation of a company’s past records. Recommendations are made in accordance with the study to ensure the betterment of the company.

There are a number of trends today which are shaping the future of Business Analytics. Keeping up with these trends and analyzing them is also a major part of the future. This makes it the right time to take up the Business Analytics Course. In this article, we will specifically talk about these trends.

Machine Intelligence

Machine Intelligence refers to creating smart machines which could act and perform specific activities just like humans. These machines are designed to work similar to human activities and natural phenomena. The machines learn from specific data sets customized to their activity. These are organized by individuals with skills in business intelligence.

Artificial Intelligence is only as good as the data provided to it.

With technological advancement, we have come to the invention of a medium both to enhance human intelligence, and replace it with something smarter and more efficient. Artificial Intelligence (also called augmented intelligence) takes its decisions on its own in accordance with its programming and learning from the data provided to it. An essential aspect of AI is that it is only as smart as the data. Business analytics serves that purpose by providing the AI program with a sorted and efficient set of data.

Internet of Things reached $170.57 billion in 2017.

The Internet of Things (IOT) is a market of devices which provide users with data on the basis of machine-to-machine communication. These devices or machines (like the Fitbit and smart watches) collect data from the user, interpret and analyze them by their parameters, and return their results accordingly. The IOT market is expected to rise to $561.04 billion by 2022. This will be a result of business analytics, through which a more organized data set is expected to be provided. Business analytics provides these wireless devices with properly understandable data which helps them provide simplified and relevant data in return.

Takeaway:

With the advent of technology, consumer expectation has shot up accordingly. Devices for instant entertainment, wireless communication, and other smart devices have experienced a rise in market demand. Moreover, the demand for sensors in these devices has also been increasing. Business analytics is that field of data simplification which is needed to convert this expectation into reality. If you are thinking of a career in Business Analytics, this is the best time to take Business Analytics course in Thane.

Can You Be Agile Without Doing Scrum If So How

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This is not a frequent question that many encounters in their Agile career, but, have you ever wondered, ‘Is Agile possible without Scrum?’
The answer to that is, yes, absolutely.
There are many instances of real-world projects and corporate projects that sometimes do not make use of any of your Scrum sensibilities. Knowing and being an Agile practitioner is more than sufficient. Let us understand further. Scrum as a framework is used to enable teams, enterprises, and organizations on their Agility pathway. It is not the only proven way to be Agile. But Agile works well without Scrum under certain circumstances only. Let us understand what these are –
1. When your project is small in size – Agile without Scrum works best only if the project size is small and the team members are also small in number. This is because the time used for Agile scaling will be shorter and will have not many disadvantages affecting the project directly.
2. Clear, simple and direct requirements – Under Agile without Scrum is applicable best to requirements from the project that are really simple, crystal clear and direct.
3. Periodical planning and requirements – The projects that you undertake must have and experience periodical planning and must be upgraded with periodical requirements, so as to not pressure the project processes. This can be used to a large extent with Agile and there is no requirement for Scrum, whatsoever.
4. Regular improvements – open communication with your team members which is quite easy to do since it is already a small team, can help you figure out already existing solutions and gain feedback regarding your work, weekly developments and progress, crosscheck these with the developer, and before you know it, you would have increased your efficiency through such regular improvements.
5. Small teams mean no hierarchies/better transparency – Small teams where everybody is an equal leads to transparent knowledge sharing, coping strategies and mutual help/support. Also since smaller teams lack a head, or rather is not hierarchy conducive, it becomes easier to be a self-organizing team which is driven by equal participation from the team members.
6. Quality output – All team members are equally held responsible for their contribution and quality output, which is responsible to keep the team going. This helps deliver success and efficient output in small scale but the quality remains very high. Each and every member ensures that their work meets a standard parameter to test quality.
7. Regular/periodical releases – It is most important to have working software in place instead of comprehensive documentation.
Sometimes you will have no release team and may have to work around this aspect of the project by yourselves. You will need to put your work into production periodically, ideally, weekly, and get the approval of the Product Manager to ensure you can move on smoothly to the next tasks at hand. You may also need to segregate tasks and assign them according to each team member’s unique capacities, to see quality output from your team’s final project work. There is an excellent way to learn Agile and go under Agile training. You can try Agile training in Mumbai. You can eventually upgrade to Agile development certification.

How have statistical machines influenced Machine Learning?

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The past few years have witnessed tremendous growth of machine learning across various industries. From being a technology of the future, machine learning is now providing resources for billion-dollar businesses. One of the latest trend observed in this field is the application of statistical mechanics to process complex information. The areas where statistical mechanics is applied ranges from natural models of learning to cryptosystems and error correcting codes. This article discusses how has statistical mechanics influenced machine learning.
What is Statistical Mechanics?
Statistical mechanics is a prominent subject of the modern day’s physics. The fundamental study of any physical system with large numbers of degrees of freedom requires statistical mechanics. This approach makes use of probability theory, statistical methods and microscopic laws.
The statistical mechanics enables a better study of how macroscopic concepts such as temperature and pressure are related to the descriptions of the microscopic state which shifts around an average state. This helps us to connect the thermodynamic quantities such as heat capacity to the microscopic behavior. In classical thermodynamics, the only feasible option to do this is measure and tabulate all such quantities for each material.
Also, it can be used to study the systems that are in a non-equilibrium state. Statistical mechanics is often used for microscopically modeling the speed of irreversible processes. Chemical reactions or flows of particles and heat are examples of such processes.
So, How is it Influencing Machine Learning?
Anyone who has been following machine learning training would have heard about the backpropagation method used to train the neural networks. The main advantage of this method is the reduced loss functions and thereby improved accuracy. There is a relationship between the loss functions and many-dimensional space of the model’s coefficients. So, it is very beneficent to make the analogy to another many-dimensional minimization problem, potential energy minimization of the many-body physical system.
A statistical mechanical technique, called simulated annealing is used to find the energy minimum of a theoretical model for a condensed matter system. It involves simulating the motion of particles according to the physical laws with the temperature reducing from a higher to lower temperature gradually. With proper scheduling of the temperature reduction, we can settle the system into the lowest energy basin. In complex systems, it is often found that achieving global minimum every time is not possible. However, a more accurate value than that of the standard gradient descent method can be found.
Because of the similarities between the neural network loss functions and many-particle potential energy functions, simulated annealing has also been found to be applicable for training the artificial neural networks. Other many techniques used for minimizing artificial neural networks also use such analogies to physics. So basically,  statistical mechanics and its techniques are being applied to improve machine learning, especially the deep learning algorithms.
If you find machine learning interesting and worth making a career out of it, join a machine learning course to know more about this. Also, in this time of data revolution, a machine learning certification can be very useful for your career prospects.

How can AI be integrated into blockchain?

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Blockchain technology has created waves in the world of IT and fintech. The technology has a number of uses and can be implemented into various fields. The introduction of Artificial Intelligence Training (AI) makes blockchain even more interesting, opening many more opportunities. Blockchain offers solutions for the exchange of value integrated data without the need for any intermediaries. AI, on the other hand, functions on algorithms to create data without any human involvement.
Integrating AI into blockchain may help a number of businesses and stakeholders. Read on to know more about probable situations where AI integrated blockchain can be useful.
Creating More Responsive Business Data Models
Data systems are currently not open, and sharing is a great issue without compromising privacy and security. Fraudulent data is also another issue which makes it difficult for people to share data. Ai based analytics and data mining models can be used for getting data from a number of key players. The use of the data, in turn, would be defined in the blockchain records, or ledger. This will help data owners maintain the credibility, as the whole record of the data will be recorded.
AI systems can then explore the different data sets and study the patterns and behaviors of the different stakeholders. This will help to bring out insights which may have been missed till now. This will help systems respond better to what the stakeholder wants, and guess what is best for a potentially difficult scenario.
Creating useful models to serve consumers
AI can effectively mine through a huge dataset and create newer scenarios and discover patterns based on data behavior. Blockchain helps to effectively remove bugs and fraudulent data sets. New classifiers and patterns created by AI can be verified on a decentralized blockchain infrastructure, and verify their authenticity. This can be used in any consumer-facing business, such as retail transactions. Data acquired from the customers through blockchain infrastructure can be used to create marketing automation through AI.
Engagement channels such as social media and specific ad campaigns can also be used to get important data-led information and fed into intelligent business systems. This will eventually help the business cycle, and eventually improve product sales. Consumers will get access to their desired products easily. This will eventually help the business in positive publicity and improve returns on investments (ROI).
Digital Intellectual Property Rights
AI enabled data has recently become extremely popular. The versatility of the different data models is a great case study. However, due to infringement of copyrights and privacy, these data sets are not easily accessible. Data models can be used to show different architectures that cannot be identified by the original creators.
This can be solved through the integration of blockchain into the data sets. It will help creators share the data without losing the exclusive rights and patents to the data. Cryptographic digital signatures can be integrated into a global registry to maintain the data. Analysis of the data can be used to understand important trends and behaviors and get powerful insights which can be monetized into different streams. All of this can happen without compromising the original data or the integrity of the creators of the data.

Top Python Libraries For Data Science

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Top 10 Python Libraries For Data Science

With the advent of digitization, the business space has been critically revolutionized and with the introduction of data analytics, it has become easier to tap prospects and convert them by understanding their psychology by the insights derived from the same. In today’s scenario, Python language has proven to be the big boon for developers in order to create websites, applications as well as computer games. Also, with its 137000 libraries, it has helped greatly in the world of data analysis where the business platforms ardently require relevant information derived from big data that can prove conducive for critical decision making.

Let us discuss some important names of Python Libraries that can greatly benefit the data analytics space.

Theono

Theono is similar to Tensorflow that helps data scientists in performing multi-dimensional arrays relevant to computing operations. With Theono you can optimize, express and array enabled mathematical operations. It is popular amongst data scientists because of its C code generator that helps in faster evaluation.

NumPy

NumPy is undoubtedly one of the first choices amongst data scientists who are well informed about the technologies and work with data-oriented stuff. It comes with a registered BSD license and it is useful for performing scientific computations. It can also be used as a multi-dimensional container that can treat generic data. If you are at a nascent stage of data science, then it is key for you to have a good comprehension of NumPy in order to process real-world data sets. NumPy is the foundational scientific-computational library in Data Science. Its precompiled numerical and mathematical routines combined with its ability to optimize data-structures make it ideal for computations with complex matrices and data arrays.

Keras

One of the most powerful libraries on the list that allows high-level neural networks APIs for integration is Keras. It was primarily created to help with the growing challenges in complex research, thus helping to compute faster. Keras is one of the best options if you use deep learning libraries in your work. It creates a user-friendly environment to reduce efforts in cognitive load with facile API’s giving the results we want. Keras written in Python is used with building interfaces for Neural Networks. The Keras API is for humans and emphasizes user experience. It is supported at the backend by CNTK, TensorFlow or Theano. It is useful for advanced and research apps because it can use individual stand-alone components like optimizers, neural layers, initialization sequences, cost functions, regularization and activation sequences for newer expressions and combinations.

SciPy

A number of people get confused between SciPy stack and library. SciPy is widely preferred by data scientists, researchers, and developers as it provides statistics, integration, optimization and linear algebra packages for computation. SciPy is a linked library which aids NumPy and makes it applicable to functions like Fourier series and transformation, regression and minimization. SciPy follows the installation of NumPy.

NLKT

NLKT is basically national language tool kit. And as its name suggests, it is very useful for accomplishing national language tasks. With its help, you can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks.

Tensorflow

Tensorflow is an open source library designed by Google that helps in computing data low graphs with empowered machine learning algorithms. It was created to cater to the high demand for training neural networks work. It is known for its high performance and flexible architecture deployment for all GPUs, CPUs, and TPUs. Tensor has a flexible architecture written in C and has features for binding while being deployed on GPUs, CPUs used for deep learning in neural networks. Being a second generation language its enhanced speed, performance and flexibility are excellent.

Bokeh

Bokeh is a visualization library for designing that helps in designing interactive plots. It is developed on Matplotib and supports interactive designs in the web browser.

Plotly

Plotly is one of the most popular and talked about web-based frameworks for data scientists. If you want to employ Plotly in your web-based model is to be employed properly with setting up API keys.

 

SciKit-Learn

SciKit learn is typically used for simple data related and mining work. Licensed under BSD, it is an open source. It is mostly used for classification, regression and clustering manage spam, image recognition, and a lot more. The Scikit-learn module in Python integrates ML algorithms for both unsupervised and supervised medium-scale problems. Its API consistency, performance, documentation, and emphasis are on bringing ML to non-specialists in a ready simple high-level language. It is easy to adapt in production, commercial and academic enterprises because of its interface to the ML algorithms library.

Pandas:

The open-source library of Pandas has the ability to reshape structures in data and label tabular and series data for alignment automatically. It can find and fix missing data, work and save multiple formats of data, and provides labelling of heterogeneous data indexing. It is compatible with NumPy and can be used in various streams like statistics, engineering, social sciences, and finance.

Theano:

Theano is used to define arrays in Data Science which allows optimization, definition, and evaluation of mathematical expressions and differentiation of symbols using GPUs. It is initially difficult to learn and differs from Python libraries running on Fortran and C. Theano can also run on GPUs thereby increasing speed and performance using parallel processing.

PyBrain

PyBrain is one of the best in class ML libraries and it stands for Python Based Reinforcement Learning, Artificial Intelligence. If you are an entry-level data scientist, it will provide you with flexible modules and algorithms for advanced research. PyBrain is stacked with neural network algorithms that can deal with large dimensionality and continuous states. Its flexible algorithms are popular in research and since the algorithms are in the kernel they can be adapted using deep learning neural networks to any real-life tasks using reinforcement learning.

Shogun:

Shogun like the other Python libraries has the best features of semi-supervised, multi-task and large-scale learning, visualization and test frameworks; multi-class classification, one-time classification, regression, pre-processing, structured output learning, and built-in model selection strategies. It can be deployed on most OSs, is written in C and uses multiple kernel learning, testing and even supports binding to other ML libraries.

 

Comprehensively, if you are a budding data analyst or an established data scientist, you can use the above-mentioned tools as per your requirement depending on the kind of work you’re doing. This is why it is very important to understand the various libraries available that can make your work much easier for you to accomplish your task much effectively and faster. Python has been traversing the data universe for a long time with its ever-evolving tools and it is key to know them if you want to make a mark in the data analytics field. For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.

Big Data Analytics With Hadoop

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Hadoop has been around forever; right from the early days of data analytics and the big data analytics, Hadoop has been an integral part and well-known name in the IT and data analytics industry. Formally known as Apache Hadoop, it is an open source software developed partly in partnership with Apache Software Foundation. 

Today, the software is known across the globe and is used in managing data processing as well as storage for big data applications which run on clustered systems. Hadoop being a well-known name in the data analytics industry is at the center of a dynamic market whose need for big data analytics is constantly increasing. The main factor that contributes to the wide use of Hadoop in data analytics is its ability to handle and manage various applications like predictive analytics, data mining as well as machine learning. 

A feature that distinguishes Hadoop from all other tools available in the market is its ability to handle both structured and unstructured data types, thus giving users increased flexibility for collecting, processing and analyzing big data, which conventional systems like data warehouses and relational databases can’t provide. 

Hadoop and Data Analytics 

As mentioned in the introductory paragraphs, Hadoop is essentially an analytics software for big data and can run on massive clusters of servers, thus providing the user with the ability to support thousands of nodes and humongous amounts of data. Since its inception in the mid-2000s, Hadoop has become an integral part of all data analytics operations mainly because of its significant features like managing nodes in a cluster, fault tolerance capabilities and many more. 

Hadoop due to its wide range of capabilities is a very good fit for any big data analytics application. Due to its capacity to handle any form of data, be it structured or unstructured, Hadoop can handle it all. One of the most notable applications of Hadoop includes its use in customer analytics. With Hadoop, users can predict anything, be it customer churn, analyze click-stream data or analyze and predict the results of an online ad. 

Top Big Data Analytics Tools

Although Hadoop is at the center of big data analytics, there are many notable tools in the market that are definitely worth checking out. Some of the most significant ones are as mentioned below. 

  • R Programming

After Hadoop, R is the leading data analytics tool in the market today. Available in Windows, Mac as well as Linux, R is most commonly used in statistics and data modelling. 

  • Tableau Public

 Tableau Public is an open source, free data analytics tools that have the capability to seamlessly connect data warehouses, Excel or any other source and display all the data on a web-based dashboard with real-time updates. 

  • SAS

SAS is the global leader in data analytics for many years and is widely known for its easy accessibility and manipulation capabilities. 

Conclusion

Hadoop and Big Data Analytics are terms that are synonymous with each other. With Hadoop and the right source, a user can analyze any type of data imaginable. 

Analytics and Agriculture

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Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.
Use of Big Data Analytics:
Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like

  • Cloud and Nanocomputing
  • Big data, digitalization and visualization use.
  • AI, IoT and ML use.
  • Saas Platforms, cloud services, and web-based apps.

Role of data and the data analyst:

Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.
Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.
Analytical technologies in the agriculture sector can be used effectively in 

  • Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
  • Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
  • Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
  • Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
  • Marketing of data and its visualization.

What is Smart Farming?

Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer

  • ML and data visualization techniques.
  • App-based integration for data extraction and education.
  • Monitoring through drones and satellites.
  • Cloud storage for securing large volumes of data.

Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.

What is Precision Agriculture?

This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.

Concluding with the challenges:

Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high.  To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.

How important is the R programming language nowadays?

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R is a popular programming language used for statistical computing and graphics by developers. This open sourced tool is not only just a programming language but also an excellent IDE. One important field of its applications is data analysis. Statisticians and data miners largely prefer R to develop their statistical software. However, R is not as popular as programming languages such as Java or Python. This article discusses the importance of R in the current era where data is everything.
How important is R?
We know that programming software like python offers an easy to understand syntax and higher versatility. Yet, R is preferred among data analysts. The reason for is that R was designed for statisticians. Hence R comes with field-specific advantages such as great data visualization features. A large number of major organizations are found using R in their operations. Google not only uses R but developed the standards for the language which got wide acceptance.
Revolution Analytics, kind of a commercial version of R was purchased by Microsoft and they provided servers and services on top of it. So, in general, despite the steep learning curve and uneasy syntax, R has its own advantages and the industry has recognized it very well.
In the opinion of experts, R is expected to remain as an indispensable resource for the data scientists for a very long time. The wide range of pre-defined packages and libraries for the statistical analyses will keep R in the top. The introduction of platforms such as Shiny has already resulted in increased popularity of R, even among the non-specialists.
So, Should You Continue Taking that Course Teaches Machine Learning via R?
It is known that every professional with a machine learning certification has huge career opportunities waiting ahead. But it is important to possess the exact skills the employers are looking for. So, is R such a skill wanted by employers? Well, it is observed that organizations are moving towards Python at a slow pace. In academic settings and data analysis R is still most popular, but when it comes to professional use, Python is leading. Python has achieved this by providing substantial packages similar to R. Even though most machine learning tasks are doable by both languages, Python performs better when it comes to repetitive tasks and data manipulation. A better possibility of integration is another advantage of Python. Also, your project may consist of more than just statistics.
It is recommended to start learning Python if you haven’t spent much time with your Machine Learning course that teaches through R. After learning python, you can use RPy2 to access the functionalities offered by R. In effect, you will have the power of two different languages in one. Since most of the companies have production systems ready for this language, Python is always production-ready. Even if you feel like learning R after learning RPy2, it is pretty easy to do. But moving to Python after R is relatively much difficult. If you are already too deep in R, ignore everything and focus on it.

How Big Data Analytics can impact business results?

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Big data analytics are for those companies, corporates, and global organizations that take their businesses and the respective business aspects such as efficiency, growth rate, profit-making, market positioning, market targets, customer satisfaction and stakeholder satisfaction seriously, and wish to see a consistent development and progress in all of these business aspects periodically.
Let us deeply examine how big data analytics can impact your business.

1. Data collection gives insight – The general big data collection made by companies and corporates sketch a deep insight into its customers, trends in markets, profit scopes and loss areas, help in understanding their product in relation to the business market, and the target products and services that will enable bigger profits and revenues to the company/organization at large.
2. Improvement of internal operations – With the impact of big data analysis not only will you be able to understand the business worlds, business markets, your goods/services value in this relation, you will also understand what business aspects and agencies within your business operations need attention and work. This can positively contribute to the progress, development, and profitability of your company/organization largely.
3. Understand employees and workforce – Through big data analysis you will be able to understand the efficiency, effectiveness, and quality of your company’s/corporate workforce at large, and be able to help them become better at their skill sets, tools, and techniques, or you may even choose to hire better, and competent professionals from the job market. This will naturally impact your organization’s success scale in various contexts.
4. Helps understand the consumer – Big data analytics not only helps you understand the needs, requirements, and standards that your stakeholder wishes you to deliver but it also imparts a deep understanding about the consumers in the marketplace. This will, in turn, inform you about the aspects where you can improve, re-build, and develop your goods and services as per the consumer market demands and requirements. This will add to your corporate profitability and growth immensely.
5. Understand your company’s strengths – Big data analytics with all its insight and analysis about the information on market trends, customers demands, market’s profits, and demands, helps you sustain your business by giving you a clear analysis and look into your company’s already existing strengths and merits. These can be worked upon further to gain more progress, development, business evolution and direct your profits in a seamless manner. You will also understand what makes your company a leader, and be able to predict its future with the help of big data analysis.

Data Science Course

The above are but just some of the advantages of big data analysis. A professional big data analyst will tell you that data mining today is equivalent to gold mining a company, and these information pieces, data, and tools must be kept with the company for the present, and future analysis, and also to learn from past failures/blunders. Thus, big data analysis is a comprehensible, strong tool in understanding an organization/corporate/enterprise in the most precise manner possible. You can gain a big data analytics course to kick start a job in data analytics as a career.

Current Liquidity Crisis and M&A

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The financial crisis of NBFCs a major concern
For a long time, various corporations, including insurance firms, had made investments through short-term instruments in the Infrastructure Finance Company IL&FS, which has to led to a significant liquidity crunch today. Amidst this scenario, the Non-Banking Finance Companies (NBFCs) have been majorly affected by the current liquidity crisis in India. The relationship between the Government and RBI is going through a rough phase as well due to the prevailing circumstances. Adding to the tension is the ban on using Aadhaar information for microlending during December 2018.
Interference of RBI to save IL & FS from the liquidity crunch
The reports from the Ministry of Corporate Affairs (MCA) states that the total debts of IL&FS as of 2017-2018 balance sheet stands at INR 63,000 crores today. The NBFCs were expecting a ray of hope from the RBI, but to their surprise, the reserve bank imposed more rigid rules and regulations for risk management, and asset-liability structures. In the last quarter of 2018, the RBI had announced to inject INR 40,000 crore to help the soaring funds through Government securities into the system.
The problems faced by NBFCs are mostly attributed to their dependencies in short-term borrowings and long-term lending loans to builders and real estate players. Therefore RBI’s ruling enforces more disciplined liquidity management in the future is a welcoming approach. However, the point to be concerned is the unknown course of action for the NBFCs to get out of the present liquidity crisis without which implementing new measures is difficult.
The financial crunch of the NBFCs has affected the loans against the property market in the fiscal year 2019 in India. A secured loan where one party pledges a property with a lender and borrow against it is a Loan Against Property (LAP). In a report from the reporting agency, India Ratings and Research stated that the weak LAP in FY19 is mainly due to lack of strong emotions on the property market and the liquidity crunch faced by NBFCs.
An insight into global M&A
The United Nations Conference on Trade and Development’s (UNCTAD) World Investment Report of 2019 release states that there is a substantial decline in the global FDI by 13% in 2018 which is a third consecutive decline. The slide in global FDI is  USD 1.3T in 2018 from USD 1.5T in 2017. However, India witnessed a 6% growth in FDI in 2018 to 42B. This growth is attributed to the activities in cross-border mergers and acquisitions, communication, production, and financial service sector.
The growth of e-commerce in India is expected to increase tentatively by a large extent. It is estimated that India’s e-commerce transactions to reach USD200B by 2026. Further, the trending online retail businesses coupled with telecommunication growth has leveraged the increase in cross-border M&As in India to USD 33B in 2018 from USD 23B in 2017.
The domestic M&A emerging as a life saver
A blockbuster merger was by the American multinational retail corporation, Walmart and India’s largest fashion e-commerce giant Flipkart. The telecommunication alliances and deals were worth USD 2B that collectively associates deals from Vodafone and American Tower. India’s blooming year for M&As was 2018, after which the first quarter of FY2019 has been low. The reason for this subdued effect is attributed to the gloomy global M&A market.
The quarterly report figures indicated a fall in M&A in Q1CY19 to $9.9B from $21.6b in Q1CY18. However, the domestic deals were a breather for India, the most significant being the merger between Bandhan bank and Gruh Finance, which was a $3.2B deal. Another agreement was between GMR airports, and Tata group led Consortium, which amounted to $1.2B. While Japan and Germany were favorite partners for cross-border M&A, the US remained at the top of the chart with 14 inbounds and 14 outbound deals with India. The Indian business executives are high on confidence that one-third of them are expected to undertake M&A in 2019.
To Sum Up
The backup of domestic consolidation for India and continued support of interests from FDI is considered a root cause for having a stable M&A in the future. Given the weak sentiment in the bond market, the current liquidity crisis may remain stubborn for NBFCs at the present moment.
Get more interesting about Current Liquidity Crisis and M&A, by applying for an Investment Banking Courses