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Role of Chief Risk Officers: From Managing Only Credit Risks to Playing Key Roles in Big Banks

Role of Chief Risk Officers: From Managing Only Credit Risks to Playing Key Roles in Big Banks

The job responsibilities of chief risk officers (CROs) have evolved drastically over the last two decades. CROs are playing key profitable roles is some of the world’s biggest banks. In the face of the global financial crisis, risk departments, particularly CROs, are handling many more tasks apart from what they were managing twenty years back, like modeling credit and market risks and avoiding fines and criminal investigations. The list of responsibilities entrusted to the CROs has grown exponentially since the last two decades. Operational risk that are quantifiable through capital necessities and penalties for nonconformity was actually developed from a set of unquantifiable ‘’other’’ risks.

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Cyber risk:

In the present times, cyber risk has become one of the most pressing global problems that the risk departments need to cope with. The number of cyber hacks is on the rise, wreaking havoc on daily lives as well as social settings. For example, Bank of America and Wells Fargo were among the major institutes hit by the DDoS attack of 2012. It is one of the biggest cyber attacks till date, which affected nearly 80 million customers. In 2016, Swift hack was only a typo away from disrupting the global banking network.

‘’What is called ‘operational resilience’ has spun out of business continuity and operational risk, financial crime, technology and outsourcing risk- anything with risk in the title, somehow there is an expectation that it will gravitate to risk management as their responsibility,’’ says Paul Ingram, CRO of Credit Suisse International. The array of responsibilities for a CRO is so immense, including regulatory compliance, liquidity risk, counterparty risk, stress-test strategy, etc, that it is imperative for the CRO to be a part of the board of directors.

Previously, CROs reported to finance director; now they are present on the board itself. They are playing crucial roles in forming strategies and executing them, whereas around two decades ago they were only involved in risk control. The strategies should be such that the capital allocated by the board is utilized optimally, neither should the limits be exceeded nor should it be under-utilized. CROs add value to the business and are responsible for 360 degree risk assessment across the entire bank.

Banks are tackling problems like digital disruption, rising operational costs and increased competition from the non-banking sector. CROs play a crucial role in helping banks deal with these issues by making the best use of scarce resources and optimizing risk-return profiles.

Regulatory attack:

‘’Since the crisis, CROs have had their hands full implementing a vast amount of regulation,’’ says BCG’s Gerold Grasshoff. However, regulation has almost reached its apex, so CROs must now use their expertise to bring in more business for their institutions and help them gain a competitive advantage. CROs need to play active roles in finding links between the profits and losses of their businesses and balance sheets and regulatory ratios.

Risk departments were once the leaders in innovations pertaining to credit and market risk modeling. They must utilize the tactics that kept them at the forefront of innovation to help their institutions generate improved liquidity, asset and fund expenditure metrics. Their skill in spotting, checking and gauging risk is essential to provide risk-related counsel to clients. Risk departments can team up with Fintechs and regtechs to improve efficiencies in compliance and reporting sections and also enable digitizing specific risk operations.

Thus risk departments, especially CROs can add a lot of value to the banking infrastructure and help steer the institutes forward.

Credit risk modeling is an essential part of financial risk management. To develop the necessary knowledge required to model risks, enroll for credit risk analytics training at DexLab Analytics. We are the best credit risk modeling training institute in Delhi.

 

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DexLab Analytics is Heading a Training Session on CRM Using SAS for Wells Fargo & Company, US

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We are happy to announce that we have struck gold! Oops, not gold literally, but we are conducting an exhaustive 3-month long training program for the skilled professionals from Wells Fargo & Company, US. It’s a huge opportunity for us, as they have chosen us, out of our tailing contemporaries and hope we do fulfill their expectations!

Wells Fargo & Company is a top notch US MNC in the field of financial service providers. Though headquartered in San Francisco, California and they have several branches throughout the country and abroad. They even have subsidiaries in India, which are functioning well alike. Currently, it is the second-largest bank in home mortgage servicing, deposits and debit cards in the US mainland. Their skilled professionals are adept enough to address complicated finance-induced issues, but they need to be well-trained on tackling Credit Risk Management challenges, as CRM is now the need of the hour.

Our consultants are focused on imparting much in-demand skills on Credit Risk Modeling using SAS to the professionals for the next three months. The total course duration is of 96 hours and the sessions are being conducted online.

 

 

 

 

In this context, the CEO of DexLab Analytics said, “This training session is another milestone for us. At DexLab Analytics, being associated with such a global brand name, Wells Fargo is a matter of great honor and pride, which I share with all my team members. Thanks to their hard work and dedication, we today possess the ability and opportunity to conduct exhaustive training program on Credit Risk Management using SAS for the consultants working at Wells Fargo & Company.”

“The training session starts from today, and will last for three-months. The total session will span over 96 hours. Reinforcing our competitive advantage in the process of development and condoning data analytics skills amongst the data-friendly communities across the globe, we are conducting the entire 3-month session online,” he further added.

Credit Risk Management is crucial to survive in this competitive world. Businesses seek this comprehensive tool to measure risk and formulate the best strategy to be executed in future. Under the umbrella term CRM, Credit Risk Modeling is a robust framework suitable to measure risk associated with traditional crediting products, like credit score, financial letters of credit and etc. Excessive numbers of bad loans are plaguing the economy far and large, and in such situations, Credit Risk Modelling using SAS is the most coveted financial tool to possess to survive in these competitive times.

In the wake of this, DexLab Analytics is all geared up to train the Wells Fargo professionals in the in-demand skill of CRM using SAS to better manage financial and risk related challenges.

To read our Press Release, click:

DexLab Analytics is organizing a Training Program on CRM Using SAS for Wells Fargo Professionals

 

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How Fintechs Help Optimize the Operation of Banking Sector

How Fintechs Help Optimize the Operation of the Banking Sector

Financial technology- Fintech plays a key role in the rapidly evolving payment scenario. Fintech companies provide improved solutions that affect consumer behavior and facilitate widespread change in the banking sector. Changes in data management pertaining to the payment industry is occurring at a fast pace. Cloud-based solution and API technology (Application Programming Interfaces) has played a major role in boosting the start-up sector of online payment providers like PayPal and Stripe. As cited in a recent PwC report over 95% of traditional bankers are exploring different kinds of partnerships with Fintechs.

 Interpreting consumers’ spending behavior has enhanced payment and data security. Credit risk modeling help card providers identify fraudulent activities. The validity of a transaction can be checked using the GPS system in mobile phones. McKinsey, the consulting firm has identified that the banking sector can benefit the most from the better use of consumer and market data.  Technological advancements have led to the ease of analyzing vast data sets to uncover hidden patterns and trends. This smart data management system helps banks create more efficient and client-centric solutions. This will help banks to optimize their internal system and add value to their business relationship with customers.

Role of Big Data

 Over the past two years, the digital revolution has created more data than in the previous history of humankind. This data has wide-ranging applications such as the banks opening their credit lines to individuals and institutions with lesser-known credit-score and financial history. It provides insurance and healthcare services to the poor. It also forms the backbone of the budding P2P lending industry which is expected to grow at a compound annual growth rate (CAGR) of 48% year-on-year between 2016 and 2024.

The government has channelized the power of digital technologies like big data, cloud computing and advanced analytics to counter frauds and the nuisance of black money. Digital technologies also improve tax administration. Government’s analysis of GST data states that as on December 2017, there were 9.8 million unique GST registrations which are more than the total Indirect Tax registrations under the old system. In future big data will help in promoting financial inclusion which forms the rationale of the digital-first economy drive.

Small is becoming Conventional

Fintechs apart from simplifying daily banking also aids in the financial empowerment of new strata and players. Domains like cyber security, work flow management and smart contracts are gaining momentum across multiple sectors owing to the Fintech revolution. For example workflow management solution for MSMEs (small and medium enterprises) is empowering the industry which contributes to 30% of the country’s GDP. It also helps in the management of business-critical variables such as working capital, payrolls and vendor payments. Fintechs through their foreign exchange and trade solutions minimizes the time taken for banks to processing letter of credit (LC) for exporters. Similarly digitizing trade documents and regulatory agreements is crucial to find a permanent solution for the straggling export sector.

Let’s Take Your Data Dreams to the Next Level

Regulators become Innovators

According to the ‘laissez-faire’ theory in economics, the markets which are the least regulated are in fact the best-regulated. This is owing to the fact that regulations are considered as factors hindering innovations. This in turn leads to inefficient allocation of resources and chokes market-driven growth. But considering India’s evolving financial landscape this adage is fast losing its relevance. This is because regulators are themselves becoming innovators.

The Government of India has taken multiple steps to keep up with the global trend of innovation-driven business ecosystem. Some state-sponsored initiatives to fuel the innovative mindset of today’s generation are Startup India with an initial corpus of Rs 10,000 crore, Smart India Hackathon for crowd-sourcing ideas of specific problem statements, DRDO Cyber Challenge and India Innovation growth Program. This is what enabled the Indian government to declare that ‘young Indians will not be job seekers but job creators’ at the prestigious World Economic Forum (WEF).

From monitoring policies and promoting the ease of business, the role of the government in disruptive innovations has undergone a sea change. The new ecosystem which is fostering innovations is bound to see a plethora of innovations seizing the marketplace in the future. Following are two such steps:

  • IndiaStack is a set of application programming interface (APIs) developed around India’s unique identity project, Aadhaar. It allows governments, businesses, start-ups and developers to utilize a unique digital infrastructure to solve the nation’s problems pertaining to services that are paperless, presence-less and cashless.
  • NITI Ayog, the government’s think tank is developing Indiachain, the country’s largest block chain. Its vision is to reduce frauds, speed up enforcement of contracts, increase transparency of transactions and boost the agricultural economy of the country. There are plans to link Indiachain to IndiaStack and other digital identification databases.

As these initiatives start to unfold, India’s digital-first economy dream will soon be realized.

Advances in technologies like Retail Analytics and Credit Risk Modeling will take the guesswork and habit out of financial decisions. ‘’Learning’’ apps will not only learn the habit of users but will also engage users to improve their spending and saving decisions.

To know more about risk modeling follow Dexlab Analytics and take a look at their credit risk analytics and modeling training course.

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Breaking the Misconceptions: 4 Myths Regarding Data-Driven Financial Marketing

A majority of low-mid financial services companies toil under the wrong notion that owing to their capacity, size and scope, the complex data-driven marketing tactics are simply out of their reach – this is not true and frankly speaking quite a shame to consider even.

BREAKING THE MISCONCEPTIONS: 4 MYTHS REGARDING DATA-DRIVEN FINANCIAL MARKETING

Over the past decade, the whole concept of data analytics has undergone a massive transformation – the reason being an extensive democratization of marketing tactics. Today’s mid-size financial service providers can easily implement marketing initiatives used by dominant players without any glitch.

Besides, there are several other misconceptions regarding data and its effect on financial marketing that we hear so often and few of them are as follows:

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Myth1 – Legally, banks are only allowed to run broad-based advertising

While it’s partially true that there are certain restrictions on banking institutions when it comes to target consumers, based on income, age, ethnicity and other factors, marketers can still practice an array of tactics, both online and offline.

Marketers can leverage a pool of data for online and offline marketing to formulate data models, keeping in mind the existing customers need and preferences. Once you have an understanding of their online behavior, how they use the data power to carry out transactions, these insights can be applied to attract new customers, who exhibit similar behaviors.

Myth 2 – Data-driven marketing doesn’t bolster customer relationship

It’s a fact, Millennials, especially wants to be aware about financial services and its associated products, and are keen to understand how can banks lend an additional support to their living and social life. Companies can start building relationship based out of it, while implementing data-driven marketing perspective into them.

Myth 3 – You need a huge budget and an encompassing database to drive marketing campaigns

Corporate honchos and digital natives certainly maintain sprawling in-house database to boost marketing activities, but don’t be under the impression that mid-size institutions cannot leverage much from virtual datamart. The impressive SaaS-based solutions houses first-party data, safely and securely and offer you mechanisms that let you integrate with other third-party data, both online and offline.

Datamarts let mid-size marketers achieve a lot of crucial task success. Firstly, you will be able to link online user IDs with offline data – this lets you derive insights about your current customers, including their intents, interests and other details. The most important thing is that it will usher you to build customer models that could target newer customers for your bank.

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Myth 4 – Data-driven marketing is too much time-consuming

A lot of conventional marketers are of the opinion data-driven marketing is a huge concept – time-consuming and labor-intensive. But, that’s nothing but a myth. Hundreds and thousands of mid-size companies develop models, formulate offers and execute campaigns within a 30-day window using a cool datamart.

However, the design and execution part of campaigns need no time, whereas the learning part needs some time. You need to learn how to develop such intricate models, and that’s where time is involved.

To ace on financial models, get hands-on training from credit risk analysis course onlineDexLab Analytics offers superior credit risk management courses, along with data analytics, data science, python and R-Programming courses.

In the end, all that matters is prudent marketing campaigns powered by data yields better results than holding onto these misconceptions. So, break the shackles and embrace the power of data analytics.

The article has been sourced from – http://dataconomy.com/2017/08/5-misconceptions-data-driven-marketing

 

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How Credit Risk Modeling Is Used to Assess Credit Quality

Given the uproar on cyber crimes today, the issue of credit risk modeling is inevitable. Over the last few years, a wide number of globally recognized banks have initiated sophisticated systems to fabricate credit risk arising out of significant corporate details and disclosures. These adroit models are created with a sole intention to aid banks in determining, gauging, amassing and managing risk across encompassing business and product lines.

 

How Credit Risk Modeling Is Used to Assess Credit Quality

 

The more an institute’s portfolio expands better evaluation of individual credits is to be expected. Effective risk identification becomes the key factor to determine company growth. As a result, credit risk modeling backed by statistically-driven models and databases to support large volumes of data needs tends to be the need of the hour. It is defined as the analytical prudence that banks exhibit in order to assess the risk aspect of borrowers. The risk in question is dynamic, due to which the models need to assess the ability of a potential borrower if he can repay the loan along with taking a look at non-financial considerations, like environmental conditions, personality traits, management capabilities and more.

Continue reading “How Credit Risk Modeling Is Used to Assess Credit Quality”

Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

It’s a hard but true fact – no more do businesses survive without leverages. In a quest for success and expansion, they need to resort to debt, because equity alone fails to ensure survival. Be it funding a new project, fulfilling working capital requirement or expanding business operations, an organization needs funding for various corporate activities.

 

Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

 

Talking of India, the credit market scenario in here is not so matured in comparison to other developed countries; hence there exists an excessive dependency level on conventional banking structure. Nevertheless, raising finance from issuance of bonds by companies is also not so rare – majority of companies in need of capital raise money from bonds and shares and this practice is widely prevalent throughout the nation.

Continue reading “Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques”

Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run

Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run

After scoring high with top notch conglomerates, Indian economy is heating up more than ever – because of flourishing Indian fintech establishments that are popping up here and now.

In this blog, we will take a deeper look down into the mechanism how startups are doing well for themselves in this competitive world from a credit risk perspective. For that, we will dig deep into the personal account of an employee working in one of the notable startups in India, which deals with data analytics product for the financial services industry – what experiences he gathered while working in a startup sector, what advices he would like share and things like that will help us crack this industry better.

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DexLab Analytics offer the best credit risk analysis course.

Pointed things to learn from a fintech startup in India:

Product is king, so is its timing – Never ever compromise with a good product. Similarly, make sure the timing is right too – may be, because you waited too long, you missed the best product. It happens.

Hit the customers right away – Don’t vouch for any product, unless 10 people have validated the product. Allow at least 10 customers to use that product and then sit with them to grab some feedback. Startups work like this, so do you!

Economics is the essence, so do proper homework – Risk and Finance go hand in hand, but are distinct in nature. Get a grip on well-structured financial models – they will help you understand the credit exchange stuffs better. Streaming costs, revenues and growth in a single line will obviously put you in a better position in predicting the impact of credit risk. FYI, credit risk’s impact is endured on not only losses, but on costs too – which is surely a matter of concern.

Teamwork is the best work – Building a potent team is an art. Creating something of your own requires a substantial amount of risk, both personal and professional. Most seasoned consultants coming under a single roof to offer something unique is in itself an exciting idea – startups in India boast of an average age of 25 or 28 years in a particular company. Nevertheless, some companies also excel with a core team whose average experience is that of 10 years – across domains like tech, product, risk, operations, sales and marketing. The figures are interesting, ain’t they?

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Fintech is more finance and less technology – As compared to other industries, fintechs’ operational mode is very different.  Though credit risk and cost management are the founding pillars of a robust fintech business setup, none of them can make up for below-standard quality products. Offering high quality product is of supreme importance for the success of any Fintech, and if you look at fintech companies in the US and Europe you will understand why we are focusing our attention on the quality part.

While we are on the closure, there is still a lot of learning to be done – but we surely believe India is on its way to success and our fintech sector is witnessing a plethora of amazing ideas. Just keep your fingers crossed, and hope our teams pull it off in a snap.

Get credit risk modelling certification from DexLab Analytics today! Their credit risk management courses are intensive, well-researched and are written down, while keeping students’ grasping skills in mind. Go give it a shot!

 


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The Basics Of The Banking Business And Lending Risks:

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.

Stay glued to our site for further details about banking structure and risk modelling. DexLab Analytics offers a unique module on Credit Risk Modelling Using SAS. Contact us today for more details!

 

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What Sets Apart Data Science from Big Data and Data Analytics

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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