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It’s the time of the year when everyone’s in a frenzy, trying to plan and buy the best Christmas gifts for their near and dear ones. With years of gifting and all the data gathered from it, big data analytics that retailers have been using, can now help you pick out perfect presents. 

The retail industry usescustomer analyticsand business analytics toolson its existing data corresponding to various buying trends. This not only creates a customized experience for the shoppers, but also helps grow the business exponentially.  

Here’s a breakdown of how big data analytics is leveraged by retailers during the holiday season: 

Social network analysis and target ads

Your online activities are promptly recorded by social media platforms and relevant information is used by various platforms to help you have a better experience. Your every ‘Like’ and ‘Comment’ adds data points to the graph that identifies your interests and requirements. Facebook’s partnership with online merchants has changed the game of marketing and sales by leaps and bounds. 

This season, social network analysis will be busy mining your shopping history and preferences to create the perfect wish list for you. Big data analytics will trail you through the holiday season, showing you what to get your friends and family and ace the gift giving game!

Knowing what you want and how much

Who doesn’t like an element of prophecy in their life?Predictive analytics is at your service.Knowing exactly what the consumer wants is an art that Artificial Intelligence constantly masters. Statistical analysisaids in streamlining production. The demand and the pace at which a particular product sells out is important information that helps keep both the sellers and the buyers happy. For example, smart data analytic tools figured out that the market for books in Russia soars higher as the temperature gets colder. So book dealers make sure that enough copies of popular/trending books are in stock.

Planning for the season

The demand for products of varying range will only grow during the gifting season. Business analytics helps decipher and determine when and how to strengthen the supply chain. This includes arrangements for better shipping and customer service. It also predicts when the increase in sales will skyrocket and proposes solutions to deal with it. For instance, data science can analyze the business issues and solutions over the years and provide you with the best possible way to keep your sales rocketing while meeting the demand.

Business analytics also helps decide attractive prices for products in demand by collating them with the expectations of the consumers. Every buyer belongs to a segment of consumption and targeting that are clearly defined by big data analysis. This information is one of the greatest ways to get gifts of your choice with data science’s help, without even realising it. 

It is quite amazing how data science finds its way into our lives and helps make it better. The true hero behind us being able to pat ourselves on the back for buying the perfect gift online is business analytics. This is the season to be the best gift-giver, thanks to data science. 

Here’s wishing you a Christmas filled with gifts from your wish list. Merry Christmas! 

Meet Sandra Barros, a self-driven entrepreneur who offers personality development and image management services. She works with several professionals as well as students and trains them through various interactive personal development methods. The desire to do something different motivated Sandra to give her 20-year corporate career a break to start Barros Image Consultancy. Does she regret taking such a huge risk? Absolutely not!

Sandra, who is a certified Image Consultant and has trained thousands of individuals for the corporate world, strongly recommends that everyone should focus on self-development and growth. Being a staunch believer of the writer and leadership speaker, Robin S Sharma’s ideology of investing in oneself, Sandra knew that she had to bring in some positive changes in herself before helping others.

That is when Sandra decided to enrol as an Image Consultant. The training strengthened her belief that it is important to manage your image and develop your skills to meet customer expectations, build trust and eventually become successful. With the aim to enhance her professional knowledge in emerging fields as well, she signed up for Manipal ProLearn’s Digital Marketing course. The course was recommended to her by a friend and the study format was as per her expectations – a classroom set-up coupled with hands-on practical training. She successfully completed ProLearn’s Digital Marketing program, which further helped her enhance her digital marketing and entrepreneurial skills.

Invest in yourself

Quoting Heraclitus, Sandra said, “Change is the only constant.” According to her, it is a necessity in the modern world to constantly upgrade your knowledge and skills as per the changing requirements. It is the best investment you can make. Making an effort to learn and bringing a positive change in your life through experience will help you make a difference in both your professional and personal life.

Overcoming entrepreneurship hurdles

Running a business is certainly not a cakewalk. Sandra too had her low moments while setting up her business. So how did she overcome these challenges? Sandra believes that self-discipline and focus are two qualities that help sail through testing times. She is of the opinion that if one door closes, you will always find another one. The key is to stay positive and keep working hard. 

As Sandra has helped thousands of professionals discover their positives and work on them, we took the opportunity to ask her what the most-sought skill in the market is today. Sandra was quick to answer that in today’s cut-throat business world, corporates look for employees possessing great communication skills and work ethics. Reading about the journey of successful entrepreneurs, their stories, and networking with people is her mantra of growing.

Word to aspiring entrepreneurs

Sandra’s also had some advice for our growing entrepreneur aspirants. She said, “Sometimes you may not see immediate results despite putting in efforts. Don’t let that deter you. Keep nurturing your skills and adding value to your business.”

Other achievements

As an Image Consultant, Sandra has trained over 1100 individuals, including students, on various aspects of image management and has been an individual consultant for doctors, businesspersons, working professionals, homemakers and graduating students since December 2013.

Sandra is a certified soft skills trainer accredited by the Scottish Qualifications Authority and curriculum partner Conselle Institute of Image Management, USA. She is also certified for Train the Trainer program accreditated by National Accreditation Board for Education and Training (NABET), one of the constituent boards of Quality Council of India. Sandra is trained and certified as an Image Consultant from Image Consulting Business Institute (ICBI) and is also a faculty at the institute.

With an eye for detail and a desire to learn new things, this is just the beginning for Sandra! We wish her the best in her future endeavors.

Want to share your own story? Drop us a mail at manipal.prolearn@gmail.com
 

Released in 2010, Hadoop, which is an open source Java programming framework, drastically changed how Big data and its management was perceived. Hadoop is extensible, with its ability to scale from a single server to multiple servers. Every single time it is scaled, there is a possibility to add on to the storage of the framework. Therefore, companies have understood its space and pertinence in a distributed computing environment due to its ability to process large volumes of data.

With this in mind, here are four organizations who understand Hadoop’s importance, and consequently, live by Hadoop.

Royal Bank of Scotland

Today, Customer interactions are laden with complexity and are often difficult to manage. With the explosion of unstructured data and a multitude of interactions over various channels, companies are struggling to gain an all-round view.

The royal bank of Scotland utilizes the Big data management hub Cloudera structured on Apache Hadoop to fully track and comprehend customer needs, interactions and journeys. RBS, by working alongside Trifacta, has brought its Hadoop data in an orderly fashion, to gain information from online conversations between the customers and the bank.

The bank stores unstructured data such as chat logs and metadata monthly in Hadoop. RBS leverages Hadoop to convey personalized concern and relevant experiences.

 

CERN

A revolutionary leader in the field of technology and fundamental research, CERN is impressively diverse in its roles. From operating the largest particle accelerator in the world to medicine, CERN requires a data management framework such as Hadoop. It is leveraged to gain insight from C2MON (control and monitoring platform), receiving real-time information from sensor data.

Equipped with around a hundred and fifty million sensors, the large hadron collider is one of the most powerful machines that the world has seen.

 CERN researcher Manuel Martin Marquez says: "This data has been scaling in terms of amount and complexity, and the role we have is to serve to these scalable requirements, so we run a Hadoop cluster."

"From a simplistic manner we run particles through machines and make them collide, and then we store and analyze that data."

"By using Hadoop we limit the cost in hardware and complexity in maintenance."

Quote source: https://www.mis-asia.com/tech/applications/11-hadoop-deployments-and-use-cases-in-the-enterprise/?page=1

British Airways

In 2015 April, British Airways installed its initial instance of Hadoop. It was used as a data archive meant for legal cases on its EDW platform at an elevated cost.

Ever since the deployment of Hortonworks 2.2 HDP, Alan Spanos stated that within a year, his department has seen return on its investment. Along with the ROI, the department now has seventy five percent added space for upcoming projects. All of this has led to a major cost reduction to the finance department of British Airways.  

 

Western Union

In 2014, financial services corporation Western Union deployed from Cloudera, a Hadoop data analysis platform so as to gain a sense of increased personalization in dealing with its customers.   

With the usage of Cloudera Enterprise, the ability to store real-time analytics has increased substantially in efficiency.

What the implementation of apache Hadoop through Cloudera does is, to consolidate customer data in an enterprise data hub. With this deployment come big data services such as predictive modeling and pattern recognition.

By implementing this big data analytics platform, western union customers are subjected to more personalized experiences.

A well- rounded Hadoop tutorial in world class Big data courses such as the Big data analytics training in Manipal University would exemplify the above. That is, the rising pertinence of Big data and the need for Hadoop for major corporations and conglomerates.  With the emergence of a vital need for Big data management globally, there are opportunities to excel in a career in any of the above corporations, provided that the adequate skills and knowledge are in place. By taking advantage of Manipal University’s Big data training course which includes a Hadoop certification as well, it is possible to excel at a career in Big data analytics.

A rising need for the availability and utilization of information has brought about an industry that was previously nonexistent known as equity research. Equity research basically involves the analysis of the entirety of a company’s financials. Furthermore, it involves the performance of ratio analysis, forecasting financials, and exploring various scenarios that lead to a valid recommendation when it comes to stock investments.

What does an equity research analyst do?

An equity research analyst primarily analyses financials while focusing on quantitative and qualitative facets. They study the gross financial history of a company and its economic standing to eventually help investors make an informed decision. This extensively researched information is then consolidated and presented in an equity research report.

Aspects of equity research

There are various aspects of equity research. Of course, performing financial modelling is a major element of equity research, but along with it, there are a lot of aspects pertaining to analytics of the various financial metrics of the company. Some of them are:

• Discover the valuation of listed companies 
• Analyze various economic aspects such as GDP, industry market value, growth rate and competitive aspects
• Understanding entirely, the economics that surround and back the business
• Calculation of fair price using discounted cash flows and relative valuations
• Recommendation to buy or sell depending on overvaluation or undervaluation 
• Conducting a financial statement analysis to discover the company’s financial performance in the past
• Analyzing historical balance sheets, income statements and cash flow
• Usage of equity valuation models

Equity research and financial modeling

In today’s corporate arena, automation is not only pertinent but necessary.

Through various facets of industry, from service to manufacturing; most businesses are presently focusing on slowly eliminating human error. When it comes to critical processes, reducing the human element seems to significantly advance efficiency. 

Laptops and spreadsheets have eliminated the need for calculators and ledgers.

Pursuing a career in finance would involve an extensive know-how of the industry’s workings, recent trends and the usage of automation for daily activities as a professional in the finance industry.

The image below portrays the tasks of an equity research analyst, along with the importance financial modeling holds.

 source: https://www.google.co.in/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwi-1JX5zevVAhXBq48KHQF8DIAQjRwIBw&url=https%3A%2F%2Fwww.linkedin.com%2Fpulse%2Fpreparing-your-er-interview-understand-job-daniel-martins&psig=AFQjCNHohDcOz0_4e6pVlUz9l84hGdz9uA&ust=1503517668961385

What is financial modeling?

Financial modeling involves the methodical formation of a logical structure used to analyze data. This is done for the purpose of arriving at conclusively valid financial decisions and logical conclusions.

It is possible presently, to perform lengthy calculations in seconds; which previously would’ve consumed long amounts of time. This includes the inability to completely eradicate human error. The ability to perform such tasks and build such models requires extensive knowledge of the aspects of finance as well as being adept at the functions in excel. 

Relevance of financial modeling

Financial modeling is imperative in every sphere of the world of finance. It is significantly critical to every corporate set up. It represents the financial workings of a corporate entity while enabling ideas through cost-effective estimation. It aids in creating an attractive representation of the economic performance of an organization. Learning excel would greatly increase chances of having a career in finance. There are plenty of online courses and professional courses that would help as an extensive MS excel tutorial.

Who uses financial modeling?

1. Investment banks

When it comes to mergers and acquisitions, financial modeling is an extremely effective tool that helps estimate the viability of a certain deal. In the world o corporate finance, it is used to comprehend the long-term financial well-being of a company.

2. Private equity and venture capital firms

Because these firms deal with large amounts of money, financial modeling is utilized to estimate the ROI. A range of functions in excel help identify and determine the return on investment for different amounts.

3. Equity research firms

An equity research analyst needs to showcase unbiased views of various financial instruments. While the sell side analysts are typically hired by investment banks to make recommendations and valid estimations, buy side analysts are hired by private equity funds or hedge funds.

In either case, financial modeling is used to determine a company’s economic growth and future. It can be said that Advanced excel training through an excel tutorial would help secure a career in financial modeling, and of course, a world class option is the certification program in Financial Modelling using Excel offered by Manipal ProLearn.

The applications of MS Excel are vast in number, and of course a well-known application is to create great financial models. There are various kinds of financial models one can create, but there are certain models in the industry which are exclusively created on Excel. 

These models are widely in use throughout the industry, and if you are new to them, you can learn more about them through an online course or an Excel Tutorial. 

Now to give you an idea of how excel is used in the industry to create financial models, here are three types of financial models you can ace with excel.

Company Financial Models

As you might know, a sell-side analyst or an Equity Research Analyst is one who evaluates companies and estimates future earnings, growth, and other investment criteria. These tough and complex decisions are made based on a collection of the company’s models which are nothing but excel sheets with the required data. 

Here the x-axis is used to denote time i.e quarters and years and the y-axis is used for breaking down the results by line-item this could be revenue, cost of goods, etc. Most times there is also a second sheet that keeps track of units-sold-and-estimated selling, for small companies and a revenue estimate for large companies. These models can be very detailed or fairly simple but the basic format does not change and all you need is the simple excel sheet. The only thing that changes is the guess work made by the sell-side analyst. 

Once the required estimates are fed in, the mathematical formulas need to be checked and then you’re done. With just this simple model as a base, you can build sophisticated and interconnected models for income statements, cash flow statement and much more. 

 

Picture Courtesy - 1global

Valuation Models

If you don’t have the need or desire to make company models, then why not build a valuation model. You can use metrics like price-earnings, price-earnings-growth or EV/EBITDA. If this is not what you need or if you’re looking for something more complex, look at a discounted cash flow model. 

Picture Courtesy – busysoftorder

Discounted Cash Flow (DCF)

As you know a DCF or Discounted Cash Flow model is used to understand the attractiveness of an investment opportunity. In this model one row consists of year-by-year cash flow estimates, while rows/columns beneath can hold the growth estimates, discount rate, shares outstanding and cash/debt balance.

You will also need to have an estimate for year 1, estimate growth rates, add a discount rate, net cash, shares, and debt balance. Now use the NPV (net present value) function in excel to process your cash flow estimates and discount rate into an estimated NPV. Now you can add or subtract the net cash and debt, and then divide by the shares, you also need to factor in a terminal value. 

 

Picture Courtesy - Financewalk

With these three models, you can achieve a lot and the best part is you don’t need to invest lots in Professional Courses, you can pick up these excel skills from Online Training Programs and Online Certification Training options, sign up and enhance your skills today.

Have you heard of R before? If you haven’t, you should know that it is a project, a language, and a software environment. The most remarkable thing about R is that it's part of the GNU free software project. This project is about sharing software without any license restrictions or fees. Since R does not cost anything to use, over 2 million data scientists and statisticians now use the software worldwide. 

Why is it so popular? Well, the adoption of R for business applications helps companies to understand the behaviour of the user so that they can enhance their applications and software to meet these needs. Therefore, there are many online courses and professional courses which are coming up which are related to Big Data Training as well. 

To get an insight into how R is impacting the industry, let’s take a look at 4 great uses of R by companies.

Facebook and R 

Facebook is a social media giant, but they never stop looking for new ways to keep their audience engaged. They have over 2 billion monthly users and over 500 terabytes of data a day! To understand what the user does on Facebook and what they interact with on the platform, they use R. The statistical data that R provides helps the teams at Facebook understand what its users are doing throughout the day and how they can cater to them better. They are even able to track and explain how things like memes go viral on the platform, all thanks to the inputs from R. 

 

Picture Courtesy - Quora

Google and R

If you have ever run a campaign on Google, then you know that one of the first questions you have is ‘Is the increase in traffic because of something unrelated, or is it only because of the campaign?’ Google wanted to let you, as a marketer, know what would have happened if the campaign didn’t run. They thus released a new package for R called the CausalImpact. This uses R to tell us what would have happened, how many clicks or how much interaction would have happened if the campaign didn’t run. It uses Bayesian structural time-series models give us the answers we seek. Keep in mind, for this to work, you’ll need a time series of results before and after the intervention, and a "control" time series where there was no intervention at all.

 

Example of Ra and CausalImpact Picture Courtesy - revolutionanalytics

New York Times and R 

If you think of data journalism, you think The New York Times. They use R to create interactive data visualizations and it started back in 2009. The graphics editor - Amanda Cox has gone as far as calling R "The greatest software on Earth" because of the visualizations it helps her publish. R helps journalists to transform concepts to neat visualization in just hours. They have used R to cover topics like – Election forecasts, Best places to grow up, Wealth distribution and more. 

 

Picture Courtesy - r-bloggers

The environment and R

You’d be happy to learn that it’s not just big businesses that use R, it’s also used by weather stations and more. The National Weather Service uses R to help them predict things such as floods due to overflowing rivers, this actually helps to save thousands of lives! Realclimate.org uses R to show us how climate change is negatively affecting our world, from showing us raising tides to melting ice caps. Reading numbers and figures about these issues are not nearly as impactful as seeing it as visualizations, we only have R to thank for this. 

Therefore it can easily be said that the popularity levels of R are increasing without bounds. It is sure to create a plethora of job opportunities as well and so if you would like to stay ahead, there are world class options provided by MGAIT when it comes to Big Data Analytics Training, Big Data Certification and even Big Data Tutorials. 

It is irrefutable that the HBO series, Game of Thrones is a phenomenon that has the world captivated. Since its release in 2011, Game of Thrones has steadily garnered viewership, owing mostly to its bold, grotesque and unpredictable screenplay. The season 6 finale had a total viewership of 8.9 million, across the world. This cultural phenomenon has surpassed the entertainment industry and seeped into several others; people across various domains are intrigued by the factors that make Game of Thrones the exceptional success it is and Data Science is no exception. Data enthusiasts have taken to analytics, coding and machine learning to map, analyze and predict various aspects of the series. 

Over the six seasons of Game of Thrones, a plethora of theories, predictions and patterns have been uncovered using data. Fans have predicted Ned Stark’s death to Jon Snow’s return from the dead. One of the most interesting results that came from combining Game of Thrones and Data Science was the creation of a computer algorithm that was capable of predicting the deaths in the series. Following this, mainstream media began releasing infographics and data sets about the patterns of death on Game of Thrones and how one could possibly predict the next. For instance, details such as the deadliest locations (King’s Landing with 322 deaths), deadliest killer (Cersei having killed 198 characters) and comparative data such as the possibility of death amongst men (33%) and women (23%) started surfacing and people started using these data sets to conceptualize their theories and predictions. 

Predictions and theories have become a huge part of the fandom and, with the premiere of the new season the boom in it has only gone up. Fans and Data enthusiasts have come up with theories using spheres of data analytics such as pattern recognition, data mining, scrutinizing uncertainty, and visualization. Data aficionados believe that nothing about Game of Thrones is a random event and that with the right approach, one can clearly uncover its future. Some people have gone so far as to fly drones over the sets while filming occurred to gain and confirm their theories. While some resort to statistics and data patterns they manually extracted and observed over the last seasons, some strongly believe that Machine Learning can correctly predict the future of Game of Thrones.

Some of the theories and predictions for this season, backed by data gathered over the past six seasons are so grounded that one cannot refute them. For instance, this data predicts that this season’s heroes will be Sansa and Jon, based on characters’ screen time trend, mapped over the last six seasons: 

 

 ( Source: https://looker.com/blog/data-of-thrones-part-iii) 

 

Another intriguing prediction is that the episode 7 of the season would be the deadliest, if the series followed its story-arc peaking trend from the previous seasons. Some of the most plausible theories include ‘Jamie killing Cersei’, ‘The end of little finger’ and ‘Bran will bring The Wall down’. Each of these theories are backed by patterns and occurrences observed in the past. 

Though there is a lot of debate on which aspect of Data Science can predict the future of Game of Thrones, there is no denying that Data Science is capable of it. The internet is filled with multiple predictions and analyses strongly grounded in data; George R. R. Martin has even admitted to the fact that the real ending has already been predicted by fans. Whether these predictions will come true, we will only know as the season progresses. For now, gear up because Winter is Here! 

What are some of your predictions for this season of Game of Thrones?  Tell us via comments! 

Potential of MS Excel 

When we think of Microsoft Excel, we think of a computer program capable of tracking expenses or calculating complex formulas. However, this spreadsheet software does much more than just crunching numbers. Its popularity lies in its financial functions. For this reason, MS Excel can be a very powerful tool for businesses willing to make the most out of their Big Data.

MS Excel is the most preferred and used spreadsheet in the world because of its easy to use grid interface that allows you to organize any type of information. For those in the field of information systems, employees are required to learn Excel. While Microsoft Excel is apt to analyze and segment data, Microsoft Excel 2016 takes it one step higher. It includes advanced features and improvisation to streamline data analysis and increase the efficiency of a business. Today, a number of professionals are not only utilizing MS Excel 2016 to perform everyday functional tasks in their workplace but also rely on it to make critical decisions.

 

MS Excel & Finance 

Businesses commonly use MS Excel for finance related activities - calculating annual reports, planning budgets, forecasting financial trends, tracking company sales, project reports, etc. To fully benefit from MS Excel, users need to make the most of Excel functionality while figuring out where it falls short. Here are two ways you can use Excel to become a financial rock star at your workplace and improve company productivity:-

1) Building sound Financial Models

Financial Models are simple spreadsheets designed to generate reports of a company’s performance or an investment or financial activities to determine future profitability or possible risks that can be avoided. A strong financial model caters to a specific market segment and requires understanding of a company’s financial health and great skill. The skill to build and update simple/complex financial models can be best carried out on MS Excel 2016. This new version of MS Excel has more than 50 financial functions, some of which are obvious while many are hidden. Some of the important financial features in Excel 2016 are Power Query, One Click Forecasting, Text Lines in a cell, Enhanced PivotTable, Multi Select Slicer, Power BI, and so on. To learn more about these features or other specific tasks, you can undergo Advanced Excel Training by taking up an Advanced Excel Course and voila you can use Excel to manipulate financial data and improve the investment process for your business. 

2) Track product sales & Return on Investment

To identify your product’s progress and high/low sales trends, you need to maintain a complete a track of your product sales over a period of time. Keeping a track of sales will indicate your Return on Investment, gauge your progress towards meeting goals and thereby determine whether goals are realistic or not. Developing future business strategies and preparing for various scenarios is possible through improved charts, enhanced pivot tables, new formulas and other functions in Excel 2016.

Bottom Line

Knowledge of Microsoft Excel will earn you accurate results but learning the financial features of Excel 2016 will let you reap the best possible results from your business.  Excel has become so easily accessible that anybody can readily use it as a primary tool for diverse functions. An affordable and simple way to learn Excel online is through a number of Excel tutorials. 

Create financial models or learn new formulas or simply take advantage of everything Excel has to offer. Sharpening your Excel skills will open doors to promotion and leadership opportunities, and will ensure that you stand out from the rest of the crowd when it comes to creating world class financial models.

With most businesses being completely data-centric, cloud computing and big data has been on the radar of businesses for several years now. These two concepts although of significance importance right now, might infact be on a collision course. Running Hadoop on Cloud is not a new idea, over the past year there have been a lot of developments in this area of new projects relating to Hadoop running on Cloud Environments. 

Hadoop is bound to become a necessity for organizations due to the immense number of applications using very large data sets. Hadoop in a cloud allows for speedy completion of jobs as usage of Cloud enables parallel processing across multiple servers.

Some of the reasons why Hadoop will work better in Cloud:

1. Scalable and Flexible – Businesses are constantly expanding and that usually requires more computing power than the current system is capable of and that would not only take time but also be an expensive process. With a Cloud system, however, businesses can scale to the size as required, thus saving time and money. Moving data would also be a tedious and costly task, however in a Cloud environment, that would not be required and the data would still be accessible anywhere.
2. Cost-Effective  -Maintaining and developing an in-house data centre and big data analytics is not something every company can afford. Smaller companies that cannot afford the investment for the expensive hardware  can benefit from the cost-efficacy of a the Cloud environment. Small businesses can use public clouds and only pay for what they use, while large businesses can use private clouds to replace in-house data centres, and public clouds for short term projects without having to expand their in-house system.
3. Simplification of Innovation – For Companies that are still testing out Hadoop, an investment in data centers may not make sense, usage of cloud environment however allows organizations to lower the cost of innovation and increase the investment into research and other beneficial  innovation programs.
4. Efficacy of Batch Workloads – Hadoop is a batch oriented system and this means that data is collected and fed into the analytics application a few times a day in varying schedules to extract the output. Hadoop that runs on the physical data centres, thus have to be on throughout this time frame thus consuming resources, proving to be expensive. The cloud environment however, allows you to pay for what you use, thus not only making better usage of resources in an efficient manner but also reduces cost to the company.

Completely moving Hadoop into the Cloud may not however be the best option for some organizations, a hybrid structure that takes the best of Cloud computing as well as having a physical data centre would fare well for larger organizations. However, the potential for increased efficiency and cost savings definitely makes using the ‘Clouds’ for Hadoop, an interesting propostion. If you feel passionate about big data, and think that you can contribute to this ever developing field, you could always expand your skillset through a hadoop tutorial or a big data analytics course.

Of course, a world class option is Manipal University, where you can jump on the bandwagon and undergo big data training or hadoop training and be a part of the niche sector that every organization is looking for.

A financial model outlines how a business works. The main purpose is for decision making and performing financial analysis to impact a company’s profitability. 

Developing a financial model using Excel can be complex and time consuming. It requires advanced planning and a systematic approach.  Before you start, research well and learn as much as you can through online courses.  Once you’re clear about your model idea, design it on paper. Here are the 7 key steps to follow:

1) Define & Structure the Problem

The first step is to take time to understand the reason behind creating the model. Make sure you clearly define the problem you’re aiming to solve. Discuss this with stakeholders and establish just how accurate or realistic the output needs to be. This will structure your thoughts and ensure clarity when creating your model.

2) Identify Input and Output Variables 

There are two type of variables- independent and dependent variables. Independent variables are the numbers manually fed into Excel without any formulae while Dependent variables vary depending on the Independent variables. You need to list out all the inputs your model needs based on your discussion with stakeholders so that you can enter them as independent variables in your model. 

Your input variables will have an influence on your model outputs. The output always needs to be accurately calculated to prevent error. If you lay out your spreadsheets with the output in mind, you save a lot of time. 

3) Understand Mathematical & Financial Aspects of the Model

Start building the model only if you’re certain of solving a problem by hand. Before creating the model, understand the finance and mathematics of it. Learn Excel through Excel tutorials or by undergoing Excel Training. If you’re confident in accurately cracking the calculations by hand only then can you write necessary formulae or instructions for the computer to perform.  

4) Design the Model 

This is the core phase of the process. It will be much easier to build and design the model if your specifications are in order and reflect the users’ wishes. The specifications include the detailed structure of the model and how it will be laid out in Excel. When building the Excel spreadsheet, create your own template which will be the basis for all your sheets in the model. Start with the basic sheets. Discuss details of the financial model with the users regularly so that it meets expectations.

5) Test the Model

Initially, start by testing your model on small data sets so that it becomes easier to find and fix bugs or errors. For the hidden bugs, test the model with a range of input variables. Understanding how changes in the independent variables will affect the dependent variables will increase the likelihood of creating a successful model. When building a complex model, test it at every step and at regular intervals. 

6) Document the Model

This means recording every function, formulae, flowcharts, diagrams, etc so that the model is handled properly, even by those who weren’t involved in the development process. It would also allow you or others to know where exactly edits can be made.  Document the process as you develop the model and finish it after your model is fully created. 

7) Monitor & Update the Model

It is unlikely that the model will be successful at first attempt. Keep monitoring the output and checking for errors. Changes in the model will require you to regularly update and document it. 

Takeaway

The key is to plan well, assess your progress against the plan and tweak your model to provide a clear picture of what lies ahead.