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The Basics Of The Banking Business And Lending Risks:

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The Basics Of The Banking Business And Lending Risks:

Banks, as financial institutions, play an important role in the economic development of a nation. The primary function of banks had been to channelize the funds appropriately and efficiently in the economy. Households deposit cash in the banks, which the latter lends out to those businesses and households who has a requirement for credit. The credit lent out to businesses is known as commercial credit(Asset Backed Loans, Cash flow Loans, Factoring Loans, Franchisee Finance, Equipment Finance) and those lent out to the households is known as retail credit(Credit Cards, Personal Loans, Vehicle Loans, Mortgages etc.). Figure1 below shows the important interlinkages between the banking sector and the different segments of the economy:

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Figure 1: Inter Linkages of the Banking Sector with other sectors of the economy

Banks borrow from the low-risk segment (Deposits from household sector) and lend to the high-risk segment (Commercial and retail credit) and the profit from lending is earned through the interest differential between the high risk and the low risk segment. For example: There are 200 customers on the books of Bank XYZ who deposit $1000 each on 1st January, 2016. These borrowers keep their deposits with the bank for 1 year and do not withdraw their money before that. The bank pays 5% interest on the deposits plus the principal to the depositors after 1 year. On the very same day, an entrepreneur comes asking for a loan of $ 200,000 for financing his business idea. The bank gives away the amount as loan to the entrepreneur at an interest rate of 15% per annum, under the agreement that he would pay back the principal plus the interest on 31st December, 2016. Therefore, as on 1st January, 2016 the balance sheet on Bank XYZ is:

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Consider two scenarios:

Scenario 1: The Entrepreneur pays off the Principal plus the interest to the bank on 31st December, 2016

This is a win – win situation for all. The pay-offs were as follows:

 

Entrepreneur: Met the capital requirements of his business through the funding he obtained from the bank.

Depositors: The depositors got back their principal, with the interest (Total amount = 1000 + 0.05 * 1000 = 1050).

Bank: The bank earned a net profit of 10%. The profit earned by the bank is the Net Interest Income = Interest received – Interest Paid (= $30,000 – $10000 = $20,000).

Credit Risk Analytics and Regulatory Compliance – An Overview – @Dexlabanalytics.

Scenario2: The Entrepreneur defaults on the loan commitment on 31st December, 2016

This is a drastic situation for the bank!!!! The disaster would spread through the following channel:

 

Entrepreneur: Defaults on the whole amount lent.

Bank: Does not have funds to pay back to the depositors. Hence, the bank has run into liquidity crisis and hence on the way to collapse!!!!!!

Depositors: Does not get their money back. They lose confidence on the bank.

 

Only way to save the scene is BAILOUT!!!!!

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The Second Scenario highlighted some critical underlying assumptions in the lending process which resulted in the drastic outcomes:

Assumption1: The Entrepreneur (Obligor) was assumed to be a ‘Good’ borrower. No specific screening procedure was used to identify the affordability of the obligor for the loan.

Observation: The sources of borrower and transaction risks associated with an obligor must be duly assessed before lending out credit. A basic tenet of risk management is to ensure that appropriate controls are in place at the acquisition phase so that the affordability and the reliability of the borrower can be assessed appropriately. Accurate appraisal of the sources of an obligor’s origination risk helps in streamlining credit to the better class of applicants.

Assumption2: The entire amount of the deposit was lent out. The bank was over optimistic of the growth opportunities. Under estimation of the risk and over emphasis on growth objectives led to the liquidation of the bank.

Observation: The bank failed to keep back sufficient reserves to fall back up on, in case of defaults. Two extreme lending possibilities for a bank are: a. Bank keeps 100% reserves and lends out 0%, b. Bank keeps 0% and lends out 100%. Under the first extreme, the bank does not grow at all. Under the second extreme (which is the case here!!!) the bank runs a risk of running into liquidation in case of a default. Every bank must solve an optimisation problem between risk and growth opportunities.

The discussion above highlights some important questions on lending and its associated risks:

 

  1. What are the different types of risks associated with the lending process of a bank?
  2. How can the risk from lending to different types of customers be identified?
  3. How can the adequate amount of capital to be reserved by banks be identified?

 

The answers to these questions to be discussed in the subsequent blogs.

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Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

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Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.

Help yourself with credit risk analysis course online at DexLab Analytics.

Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.

What are the challenges faced by CRM Managers?

  • Sloppy data management – Unable to access the data when it’s needed the most, due to inefficient data management issues.
  • No group-wide risk modeling framework – Banks need strong, meaningful risk measures to get a larger picture of the problem. Without these frameworks, it becomes really difficult to get to the tip of the problem.
  • Too much duplication of effort – As analysts cannot alter model parameters they face too much duplication of work, which results in constant rework. This may negatively affect a bank’s efficiency ratio.
  • Inefficient risk tools – Banks need to have a potent risk solution, otherwise how can they identify portfolio concentrations or re-grade portfolios to mitigate upcoming risks!
  • Long, unwieldy reporting process – Manual spreadsheet based reporting is simply horrible, overburdening the IT analysts and researchers.

What are the Best Practices to fight the Challenges Noted Above?

For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.

While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.

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Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:

  • Improved model management, stretching over the whole modeling life cycle
  • Real-time scoring and limits monitoring
  • Powerful stress-testing capabilities
  • Data visualization capabilities and robust BI tools that helps in transporting crucial information to anyone who needs them

In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.  

Get more insights on credit risk management including articles, research and other hot topics, follow us at DexLab Analytics. We offer excellent credit risk management courses in Delhi. For further queries, call us today!

 


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SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

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SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

The noteworthy triumphs over us, humans, in Poker, GO, speech recognition, language translation, image identification and virtual assistance have enhanced the market of AI, machine learning and neural networks, triggering exponential razzmatazz of  Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these digital natives command the daily headlines, a tug of war has been boiling of late between two ace developers –  Equifax and SAS – the former is busy in developing deep learning tools to refine credit scoring, and the latter is adding new deep learning functionality to its bouquet of data mining tools and providing a deep learning API.

Continue reading “SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis”

Transformation of Smartphones with AI

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Transformation of Smartphones with AI

Once a science fiction fantasy, Artificial Intelligence is today’s resonating reality. People are already relishing myriad advantages through advanced mobile apps and smart-forever smartphones.

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Supposedly, smartphones made our lives easier. Not only does it allows us stay in touch with our beloved ones 24/7 but also allow us easy accessibility to a humungous amount of information over the internet, help us reach our designated destinations, play games, watch movies, check mails, and lot more.. And in the thick of all the telltales of new cameras and additional storage, AI is bringing in a poignant change in the realm of smartphone technology, which will impact our lives immensely.

Integrating AI with apps

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How would you feel when your phone opens INSTAGRAM, before you tapped on it? You will be elated! Isn’t it? We are already witnessing some basic versions of above-mentioned technology on some phones where the most-used apps pop up on the top of the screen. It’s no more a thing from a science fiction novel; Google Now would know everything about you – the way you use your phone, when you call your home, when you need to tap open a map app, or even the exact moment you feel like taking a photo. No more you have to arrange your home screen icons or click on the apps you need, because whenever you will unlock your phone, the app you want to open will launch automatically.

Mark your steps with AI

Google-Maps-gas-prices

 On the other hand, if we talk about mapping systems, you must have already come by Apple maps and Google maps that can predict your next whereabouts, based on past searches and destinations put into. In the near future, this technology will get cleverer intellectually. Making decisions based on your preferred routes, the type of public transport you board, how you react when you are stuck in traffic won’t be a tad difficult, provided Google has all the information it needs about you.

Say Hi to a digital assistant

02siri

Do you wonder at times, what if your phone becomes your best friend? Though it may sound creepy at first, but this is exactly the way towards which Artificial Intelligence is heading to. Digital assistants will be more like your best buddy who will be beside you on your happiest and worst days. If you feel stressed at work, your digital assistant will know how to uplift your mood or what kind of music to play to make you better..  

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FELICITATIONS to the Personal assistant app

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How about having an inbuilt personal assistant app to do the flight bookings or order some selected items from your shopping cart? Sounds cool! From restaurant bookings and comparing gas-energy prices to sending smart replies, this personal assistant using the bounties of AI excels on a bouquet of jobs.

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Fulfilling software programs like Cortana, Siri and Google Now have already started bridging the gap between them and real-life personal assistants. In the future, this gap will further be lessened and these apps will finally be able to do many smart functions.

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Darker Clouds Covering the Cloud

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Darker Clouds Covering the Cloud
 

New age technologies are dominating the present business environment. Mobility, cloud computing, social media and analytics have been affecting the different realms of business at an ever-increasing rate. Though most of the impacts are favourable, yet it will be reckless to ignore the severity of the negative ones.

Amidst all, cloud computing grabbed the utmost attention. The benefits of cloud computing are myriad – better productivity, lower costs and quicker time to market. A surging number of employees are using cloud applications to talk about various work-related subject matters. Nevertheless, data security is still a leading concern.

 

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Traditional threats are no more potent. Most organisations have devised manipulating ways to safeguard themselves against those predictable threats, newer threats call for better IT security to realise high profile business priorities. A well-researched study by VMware, a pioneer in cloud infrastructure and digital workspace technology revealed that though businesses – small, medium and large will be more than keen to implement cloud computing to secure better future goals and efficiency, information security thriving on the cloud will have a profound impact on enterprises in the next 3-5 years.

 

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The Cloud Security

Another study by eminent research firm Kantar IMRB highlighted that though organisations are taking steps towards a modern workspace environment, they are more interested about having a safe and secured digital environment, thanks to a rising number of cyber threats and thefts. If you follow the figures, in the next 3-5 years, more than 86% of enterprises are going to enhance their IT Budget and 80% of organisations will be eager to expend more time, skill and money on cloud technology.

 

haven_secure_cloud

 

In respect to the above context, Arun Parameswaran, managing director of VMware India said, “With nearly 25% of all IT workloads being managed on the cloud today, and the number expected to double by 2021, it is evident that the traditional on-premises IT environment is undergoing a profound change.” He further added, “Today, CIOs play an extremely essential role in their organisations’ IT, and it is of utmost importance to have enterprise data available always—anytime and anywhere while tightly secured.”

Enhanced productivity and better profitability will always remain a prime priority, but now as per the recent studies, IT security has also become a chief concern in the list of business priorities. However, despite heavy investments in IT, CIOs of well-established companies are unhappy because the budget is either not structured properly or inadequate. The studies also reveal that the government and BFSI respondents think that the budget for IT security is quite low, and it should be increased at least by 25% by next year.

 

Cloud is the best thing since sliced bread. Companies are relying more on cloud to store sensitive data. Cloud is the future; so companies should look up to ways to balance the risks with explicit advantages that this evolving technology brings in.

 Data-Privacy

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ANZ uses R programming for Credit Risk Analysis

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ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

In the slides he made, one can see the following:

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

A comparison in between R and SAS for fitting such non-standard models

Mr. Ooi also notes that SAS does contain various options for modelling variance like for instance, SAS PROC MIXED, PRIC NLIN. However, none of these are as flexible or powerful as R. The main difference as per Ooi, is that R modelling functions return as object as opposed to returning with a mere textual output. This however, can be later modified and manipulated with to adapt to a new modelling situation and generate summaries, predictions and more. An R programmer can do this manipulation.

 

Read Also: From dreams to reality: a vision to train the youngsters about big data analytics by the young entrepreneurs:

 

We can use cohort models to aggregate the point estimates for default into an overall risk portfolio as follows:

A comparison in between R and SAS for fitting such non-standard models
Photo Coutesy of revolution-computing.typepad.com

He revealed how ANZ implemented a stress-testing simulation, which made available to business users via an Excel interface:

The primary analysis is done in r programming within 2 minutes usually, in comparison to SAS versions that actually took 4 hours to run, and frequently kept crashing due to lack of disk space. As the data is stored within SAS; SAS code is often used to create the source data…

While an R script can be used to automate the process of writing, the SAS code can do so with much simplicity around the flexible limitations of SAS.

 

Read Also: Dexlab Analytics' Workshop on Sentiment Analysis of Twitter Data Using R Programming

 

Comparison between use of R and SAS’s IML language to implement algorithms:

Mr. Ooi’s R programming code has a neat trick of creating a matrix of R list objects, which is fairly difficult to do with IML’s matrix only data structures.

He also discussed some of the challenges one ma face when trying to deploy open-source R in the commercial organizations, like “who should I yell at if things do now work right”.

And lastly he also discussed a collection of typically useful R resources as well.

For people who work in a bank and need help adopting R in the workflow, may make use of this presentation to get some knowledge about the same. And also feel free to get in touch with our in-house experts in R programming at DexLab Analytics, the premiere R programming training institute in India.

 

Ref – https://www.r-bloggers.com/how-anz-uses-r-for-credit-risk-analysis/

 

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The Opportunities and Challenges in Credit Scoring with Big Data

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The Opportunities and Challenges in Credit Scoring with Big Data

Within the past few decades, the banking institutions have collected plenty of data in order to describe the default behaviour of their clientele. Good examples of them are historical data about a person’s date of birth, their income, gender, status of employment etc. the whole of this data has all been nicely stored into several huge databases or data warehouses (for e.g. relational).

And on top of all this, the banks have accumulated several business experiences about their crediting products. For instance, a lot of credit experts have done a pretty swell job at discriminating between low risk and high risk mortgages with the use of their business mortgages, thereby making use of their business expertise only. It is now the goal of all credit scoring to conduct a detailed analysis of both the sources of data into a more detailed perspective with then come up with a statistically based decision model, which allows to score future credit applications and then ultimately make a decision about which ones to accept and which to reject.

With the surfacing of Big Data it has created both chances as well as challenges to conduct credit scoring. Big Data is often categorised in terms of its four Vs viz: Variety, Velocity, Volume, and Veracity. To further illustrate this, let us in short focus into some key sources or processes, which will generate Big Data.  

The traditional sources of Big Data are usually large scale transactional enterprise systems like OLTP (online Transactional Processing), ERP (Enterprise Resource Processing) and CRM (Customer Relationship Management) applications. The classical credit is generally constructed using the data extracted from these traditional transactional systems.

However, the online graphing is more recent example. Simply think about the all the major social media networks like, Weibo, Wechat, Facebook, Twitter etc. All of these networks together capture the information about close to two billion people relating to their friends preferences and their other behaviours, thereby leaving behind a huge trail of digital footprint in the form of data.

Also think about the IoT (the internet of things) or the emergence of the sensor enable ecosystems which is going to link the various objects (for e.g. cars, homes etc) with each other as well as with other humans. And finally, we get to see a more and more transparent or public data such as the data about weather, maps, traffic and the macro-economy. It is a clear indication that all of these new sources of generating data will offer a tremendous potential for building better credit scoring models.

The main challenges:

The above mentioned data generating processes can all be categorised in terms of their sheer volume of the data which is being created. Thus, it is evident that this poses to be a serious challenge in order to set up a scalable storage architecture which when combined with a distributed approach to manipulate data and query will be difficult.

Big Data also comes with a lot of variety or in several other formats. The traditional data or the structured data, such as customer name, their birth date etc are usually more and more complementary with unstructured data such as images, tweets, emails, sensor data, Facebook pages, GPS data etc. While the former may be easily stored in traditional databases, the latter needs to be accommodated with the use of appropriate database technology thus, facilitating the storage, querying and manipulation of each of these types of unstructured data. Also it requires a lot of effort since it is thought to be that at least 80 percent of all data in unstructured.

The speed at which data is generated is the velocity factor and it is at that perfect speed that it must be analysed and stored. You can imagine the streaming applications like on-line trading platforms, SMS messages, YouTube, about the credit card swipes and other phone calls, these are all examples of high velocity data and form an important concern.

Veracity which is the quality or trustworthiness of the data, is yet another factor that needs to be considered. However, sadly more data does not automatically indicate better data, so the quality of data being generated must be monitored closely and guaranteed.

So, in closing thoughts as the velocity, veracity, volume, and variety keeps growing, so will the new opportunities to build better credit scoring models.     

Looking for credit risk modelling courses? Take up our credit risk management course online or classroom-based from DexLab Analytics and get your career moving….

 

 

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Understanding Credit Risk Management With Modelling and Validation

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The term credit risk encompasses all types of default risks that are associated with different financial instruments such as – (like for example, a debtor has not met his or her legal duties according to the debt contract), migrating risk (arises from adverse movements internally or externally with the ratings) and country risks (the debtor cannot pay as per the duties because of measure or events taken by political or monetary agencies of the country itself).

In compliance to Basel Regulations, most banks choose to develop their own credit risk measuring parameters: Probability Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Several MNCs have gathered solid experience by developing models for the Internal Ratings Based Approach (IRBA) for different clients.

For implementation of these Credit Risk Assessment parameters, we need the following data analytics and visualization tools:

  • SAS Credit Risk modelling for banking
  • SA Enterprise miner and SAS Credit scoring
  • Matlab
Default Probability Curve for Each Counterparty
                                                                               Image Source: businessdecision.be

Credit and counterparty risk validating:

The models that are built for the computation of risks must be revalidated on a regular basis.

On one hand, the second pillar of the Basel regulations implies that supervisors should check that their risk models are working consistently for optimum results. On the other hand, recent crises have drawn the focus of the stakeholders of the banks (business, CRO) to a higher interest on the models.

The process of validation includes in a review of the development process and all the related aspects of model implementation. The process can be divided into two parts:

  1. Quality control is mainly concerned about the ongoing monitoring of the model in use, the quality of the input variables, judgemental decisions and the resulting output models.
  2. Quantitatively with backresting, we can statistically compare the periodic risk parameters with its actual outcomes.

In the context of credit risk, the process of validation is concerned with three main parameters they are – probability of default (PD), exposure at default (EAD) and the loss given default (LGD). And for all of the above mentioned three a complete backresting is done at the three levels:

  1. Discriminatory power: this is the ability of the model to differentiate between defaults, non-defaults, or between high-losses and low losses.
  2. Power of prediction: this is a checking using comparison between defaults and non-defaults, or between high losses and low losses.
  3. Stability: is the portfolio change between the time when the model was first developed and now.

In the below three X three matrix (parameter X level) each and every component has had one or more standardized tests to process. With the right Credit Risk Modelling training an individual can implement all the above tests and provide for the needful reporting of the same.

In terms of the counterparty credit risk context, one must consider the uncertainty of exposure and the bilateral nature of the risk associated. Hence, exposure at the default can be replaced by the EPE (expected positive exposure) and EEPE (effective expected positive exposure).

The test include comparing the observed P&L with the EEPE (make sure the violations are moderate and the pass rate does not exceed a predetermined level for instance 70%).

Deep Learning and AI using Python

For better visualization, here is an example of the same:

For better visualization, here is an example of the same:
                                                                  Image Source: businessdecision.be

Risk models:

As per the National Bank of Belgium, which is he Belgian regulator (NBB), it insists that appropriate conservative measures should be incorporated to compensate for the discrepancies of the value and risk models. For example, as per the NBB requisites there should be an assessment of the model risk, which is based on the inventory of:

  1. The risk that model covers, along with an assessment of the quality of the results calculated by the model (maturity of the model, adequacy of assumptions made, weaknesses and limitations of the model, etc) and the improvements that are planned to be included over time.
  2. The risks that are not yet be covered by the model along with an assessment of the materiality of these risks and their process of handling the same.
  3. The elements that are covered by a general modelling method along with the entities that are covered by a more simplified method, or the ones that are not covered at all.

A quality Credit Risk Management Course can provide you with the necessary functional and technical knowledge to assess the model risk.

 

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What Sets Apart Data Science from Big Data and Data Analytics

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What Sets Apart Data Science from Big Data and Data Analytics

Today is a time when omnipresent has a whole new definition. We no longer think about the almighty, omnipotent and omnipresent God when we speak about being everywhere. Nowadays we mostly mean data when we hear the term “present everywhere”. The amount of digital data that populates the earth today is growing at a tremendous rate, doubling over every two years and transforming the way we live.

As per IBM, an astounding amount of 2.5 Billion gigabytes of data is generated every day since the year 2012. Another revelation made by an article published in the Forbes magazine stated that data is growing faster than ever before today, and by the year 2020 almost 1.7 megabytes of new information will be created every second by every human being on this earth. And that is why it is imperative to know the fundamental basics of this field as clearly this is where our future lies.

In this article, we will know the main differentiating factors between data science, Big Data analysis and data analytics. We will discuss in detail about the points such as what they are, where they are used, and the skills one needs to be a professional in these fields, and finally the prospect of salary in each case.

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First off we start with the understanding of what these subjects are:

What is data science?

Data science involves dealing with unstructured and structured data. It is a field that consists of everything that relates to cleansing of data, preparation and analysis. It can be defined as the combination of mathematics, analytics, statistics, programming, capture of data and problem solving. And all of that in the most ingenious ways with an amazing ability to look at things from a unique perspective. They professionals involved with this field should be proficient in data preparation, cleansing, and alignment of data.

To put it simply, this is the umbrella of techniques which is used to extract insights and information from the data.

What do we mean by Big Data?

As the name suggests, Big Data is nothing but a mammoth amount of data. This is so huge that it cannot be processed effectively with the existing traditional applications. The processing of Big Data starts with working with raw data that is not very well aggregated and is almost impossible to store in the memory of only one single computer.

It is now a popular buzzword filling up the job portals with vacancies. And is used to denote basically a large number of data, both structured and unstructured. It inundates a business on a daily basis. It is a prime source of information that can be used to take better decisions and proper strategic business moves.

As per Gartner, Big Data can be defined as high velocity, high volume and high variety information assets which demand cost efficient, innovative forms of information processing that enable improved insight, better decision making, and a procedural automation.

Thus a Big Data certification, can help you bag the best paying jobs in the market.

Understanding data analytics:

Data Analytics is the science of assessing raw data with the purpose of drawing actionable insights from the same.

It basically involves application of algorithms in a mechanical and systematic process to gather information. For instance, it may involve a task like running through a large number of data sets to look for comprehensible correlations between one another.

The main focus for data analytics is concentrated on interference, which is the procedure for deriving conclusions which are mainly based on what the researchers already are aware of.

Where can I apply my data science skills?

  • On internet searching: search engines use data science algorithms
  • For digital ads: data science algorithms is an important aspect for the whole digital marketing spectrum.
  • Recommender systems: finding relevant products from a list of billions available can be found easily. Several companies and ecommerce retailers use data to implement this system.

Big Data applicability:

The following sectors use Big Data application:

  • Customer analysis
  • Fraud analytics
  • Compliance analytics
  • Financial services, credit risk modelling
  • Operational analytics
  • Communication systems
  • Retailers

Data analysis scope and application:

  1. Healthcare sector for efficient service and reduction of cost pressure
  2. Travel sector for optimizing buying experience
  3. Gaming industry for deriving insights about likes and dislikes of gamers
  4. For management of energy, with smart grid management, energy optimization distribution and also used by utility companies.

Here is an infographic that further describes all there is to know about these trending, job-hungry sectors that are growing at a tremendous rate:

Don’t Be Bamboozled by The Data-Jargon: Difference in Detween The Data Fields

 

Now that you know what the path to career success, looks like stop waiting and get a R Analytics Certification today.

 

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