Pradhan Mantri Sahaj Bijli Har Ghar Yojana Policy
Pradhan Mantri Sahaj Bijli Har Ghar Yojana Policy Prime Minister recently launched the Saubhagya scheme, also known as the Pradhan Mantri Sahaj Bijli Har Ghar Yojana. What does it aim to achieve and can it really attempt all it promises? Introduction Electricity availability is essential for the development of our country. It will have positive impact on the lives of people. It helps in boosting the education and healthcare facilities of the region which results in overall human development. According to ‘World Bank’ data in 2014 only 80% of India’s population had access to electricity compared to world average of 85%. Hence, the current government has been focussing a lot on increasing the access to electricity. Prime Minister Narendra Modi had also earlier launched two important schemes to boost the electricity connectivity namely UDAY & Deen Dayal Upadhyaya Gram Jyoti Yojana (DDUGJY). Need for Saubhagya scheme Deen Dayal Upadhyaya Gram Jyoti Yojana (DDUGJY) was launched in July 2015 to electrify the villages which didn’t have the connectivity. The government was able to electrify 14,701 villages but 2,760 were still left. Even if we consider that so many villages were electrified but still there was a lot of gap. According to the definition used: ‘A village was considered to be electrified even if 10% of household of that village had electricity connection’. Another gap was the irregular supply of electricity in the connected households. So, if out of 14000 villages electrified under this scheme, 90% of households don’t have connection and among those connected don’t have provision of minimum supply can still these villages be considered electrified? Features and Aim of Pradhan Mantri Sahaj Bijli Har Ghar Yojana It has been launched by Prime Minister on 25th September, 2017. It is different from DDUGJY in the sense that it provides access to all by last-mile connectivity. So, it brings the transition from connection villages to connecting households. The outlay for the scheme proposed is Rs. 16,320 crore out of which 14,025 crore is for rural households. The target set by the government to complete the electrification process is 31st December, 2018. Government will provide free connections to families under BPL (Below Poverty Line) category. These BPL families will be identified through Socio Economic and Caste Census (SECC) 2011 data. The whole process will be done using latest technology like using mobile app for household survey. The households which are not covered under BPL status will also be given the facility of paying the fees of Rs.500 in 10 instalments. In addition to ensuring electricity connectivity to every household the scheme aims to upgrade environment because the use of Kerosene lamps for lightning purpose will reduce. Other outcomes it expects to achieve are better connectivity, improved health and education standards and more jobs. Hence, it will help in improving overall quality of life of people. Challenges in implementation There are two major challenges to implementation: 1. Connection Bills: Though the scheme has the provision of free connections for BPL households but it doesn’t take into account the payment of monthly electricity charges. It is unrealistic to expect that BPL households will be able to pay the monthly bills s per high tariff of DISCOMs. 2. Regular supply of electricity: According to an estimate if we assume that all remaining households are connected then it will require additional 28000MW which is 7% of country’s total capacity. Meeting this high power requirement will be a challenging task as already there is a shortage of power especially in rural areas. Conclusion In last three years many schemes have been launched that are based on Government’s agenda of development, Saubhagya is one such scheme. It has a very ambitious aim of connecting every household to electricity grid network of India. But it remains to be seen whether the demand of the additional capacity will be met. If this scheme is successful then it will definitely provide a huge boost to Indian economy and overall growth.
Impact of Trump's Announcement
Israel Rage continues after Trump’s announcement December 9, 2017 marks the 30th anniversary of the first ‘Intifada’ declared against Israel. Rage has again simmered on this day with the leader of Hamas, Ismail Haniyeh declaring the third uprising against Israel. This is the result of declaration by US president which recognized Jerusalem as the capital of Israel. This declaration didn’t go well with Palestinian militant group and they consider it a declaration of war on Palestinians. At least three rockets have been launched till now from the Gaza toward the Israel town. In retaliation by Israel army two people were reported dead belonging to Hamas militant group and as many as 200 wounded and admitted in Gaza’s Shifa Hospital. Tension also rose in city of Bethlehem where protesters threw stones on Israeli troops. Demonstrations were also reported in East Jerusalem where Israeli troops used tear gas. Israel has also started targeting sites in Gaza following rocket strikes from militants. So this announcement by Mr. Donald Trump has created situation of new civil uprising in Israel and has given gift to radicalism. The Jerusalem has been one of the main obstacles for peace between Palestine and Israel after Israel occupied it in 1967. Since then Palestine has been claiming East Jerusalem whereas Israel recognizes it as its capital.
Chemical Engineering Internship advice series-2
What type of training one should undertake and how to apply for Summer Internship? Now there are basically three domains in which a chemical engineer can undertake summer internship namely Research, Production and Designing. First option is the research training. This field is good for the people who want to pursue higher studies and do research work in future. Research internship is provided by various government institutes like IITs, NITs, IISERs, IISC Bangalore and other institutes of national importance like JNCASR. Out of these selection for internship in JNCASR is very tough because seats are very few and applicants are very large. Internship program of JNCASR is a prestigious one, student gets the stipend for is work and top performers also get scholarships for further studies. Also you get the chance of getting your paper published in an International journal. Selection in many institutes is done through application forms which are out in the month of January. Another way of getting selected is that you can find out the name, e-mail id, and the field of research of the professor under whom you want to undertake internship from the Institute’s website. Then you can send your resume to that professor. Other two options are summer training in Production companies and Designing companies. Now I will advise that a student takes production or plant training in IInd Year itself so that he can go for training in designing company in III Year, thus getting training in both the domains. These options are preferable for those who want to do core jobs after completion of their graduation. Also every student should keep in mind that it is a wrong concept that if you do training in Production Company then you cannot get job in designing company or vice versa. In my last article on Summer Internship I will tell about different production and designing companies and how to choose and apply for summer training in such companies.
Return on assets case study
Title: Significance of Return on Assets (ROA) & its correct computation for determining the profitability of a company. Return on Assets (ROA) is an important criterion to judge the profitability of the company. It measures the profitability of the company in relation to its resources. Often the management of the company is in the dilemma ‘What to do with the assets available?’ ROA answers this question for them. ROA for capital intensive company will be relatively low as compared to company with low capitalization. This is because capital intensive company requires large amount of assets to do what it does. At the same time capital intensive company will have more no of assets that can be turned into money at the time of failure. Methodology I have tried to highlight the importance of the Return on Assets for a company by taking two cases. In the first case I have taken the example of a largest producer of steel, ‘Steel-giant’. The management of this company is under a lot of stress due to their decreasing return on assets. So here the importance of correct computation of ROA is highlighted by using payments made to both lenders and owners in the numerator. In the second case I have taken the example of two companies ‘PM’&‘IT’. PM is having high profit margin and IT is having high Investment turnover. I have highlighted the importance of ROA in this case by using Du-Pont analysis. Problem Statement 1 Steel -giant is a leading producer of steel in India. It is a public sector undertaking which is mainly owned by Government of India. It started its operation in India in 1954 and has slowly and steadily become the steel market leader of India. India’s steel industry from last 6 years has seen a lot of turbulence due to sharp decline in global commodity prices. As a result of which Steel-giant has seen a sharp decline in profits. This has in turn led to reduced Return on Assets. After facing huge decline in profits and return on assets, the company now is being led by a dynamic chairman from last two years. His continuous efforts and India’s push for infrastructure projects has led to increase in profits in the current year. He asks for the financial data of last 4 years from his accountant which is given below: Important data 2017 2016 2015 2014 Total Assets 426.5 411.5 407.6 411.8 EAT 1.8 -0.2 4.4 5.2 Interest 5.2 4.9 5 5.5 Tax Advantage 1.82 1.715 1.75 1.925 The chairman understands the importance of ROA and hence wants to find out Return on assets (ROA) of last 4 years and compare it with the industry figures. The accountant calculates ROA based on the conventional method: ROA= (EAT/ Total assets)*100 So this gives return on assets for: 2014 = 5.2/411.8 = 1.26% 2015 = 4.4/407.6 = 1.08% 2016 = -0.2/411.5= -0.05% 2017 = 1.8/426.5 = 0.42% Chairman looks at the calculations of accountant and realizes there is some fault in the calculations. As the ROA calculated is less as compared to the other industries having nearly same EAT. So he hires a consultancy service ‘Think different consultancy services’ to advice him regarding the correct calculations and other steps to improve ROA. The consultant of the company after looking at the calculations instantly informs the chairman about the correct concept. Solution: He says that ROA calculated using conventional method is an underestimate of profitability as EAT is a reward to shareholders. Actually assets are financed both by shareholders funds and debt-holders funds. So, the numerator should also include reward made both to the owners and lenders. So the numerator will be inclusive of interest paid to debt-holders. (Reference: Page 4.34, Management Accounting by Professor P.K. Jain) So Real ROA= (EAT+ Interest- Tax advantage on interest)*100/ (Total Assets) Based on this formula he calculates ROA as follows: 2014 = (5.2+5.5-1.92)/411.8 = 2.13% 2015 = (4.4+5.0-1.75)/407.6 = 1.88% 2016 = (-0.2+4.9-1.715)/411.5 = 0.72% 2017= (1.8+5.2-1.82)/426.5 = 1.21% The chairman understood the usefulness of the formula suggested by the consultant after seeing the ROA improving considerably for all the years. Consultant further inquired about the assets of the company like Land, plant & machinery as he was aware about the vast amount of land lying unused in the PSUs. He found that more than 100 acres of land was lying unused in various plants of Steel-giant. He also observed that the company like other steel companies was battling a flood of cheap imports from china inundating the company with cheap supplies. So some amount of plant & machinery was also lying unutilised. So he estimated the total unused assets of about 30%. He now calculated the ROA based on Total assets actually used: (Eat + Interest-Tax Advantage)*100/ (Total Assets)*0.7 2014 = 2014 = (5.2+5.5-1.92)/ (411.8)*0.7 = 3.04% 2015 = (4.4+5.0-1.75)/ (407.6)*0.7 = 2.68% 2016 = (-0.2+4.9-1.715)/ (411.5)*0.7 = 1.04% 2017= (1.8+5.2-1.82)/ (426.5)*0.7 = 1.73% This concept helped chairman to understand that ROA is based on the concept of assets actually used to generate profit as the ROA of Steel-giant now became comparable to other steel companies earning same amount of profit. So the Chairman got to know that assets which are not in use are not contributing to EAT so they shouldn’t be used in the calculation. Some recommendations were also given by the consultant: 1. He observed that the other expenses like travelling expenses, scrap recovery expenses, maintenance expenses, etc. formed a major part of the company’s expenses. Since the company is already facing issues of less margins so he insisted on reducing these miscellaneous expenses. 2. Company has a huge amount of land lying as waste. This land is currently unproductive and company at this position can’t afford this. If the company doesn’t have any plans of expansion in future it can rent this land to generate income. Another option is to sell the land to fund its operations but that will only be a short term boost. So the company should quickly come up with plans to either diversify its business or rent the unused land. 3. Lastly it should not allow the machinery to be unutilised for long periods of time. Because the cost of machinery is a fixed cost, it will continue to be incurred even if it is not put to use. So company should ensure maximum utilization of its installed capacity, so that proper utilization of assets can be done. Problem Statement 2 This problem is related to two companies PM & IT. Both the companies are generating same ROA form two years which is close to 9%. Both of them have approached the same consultancy service ‘Think different consultancy services’ to know about the reasons of low ROA and also the ways to improve it. Important financial data of PM for 2 years is given below: Important data 2017 2016 Sale 80.8 80.6 Earnings after tax 6.5 6.2 Average Total Assets 70.2 69.2 Important financial data of IT for 2 years is given below: 2017 2016 Sale 140.2 138.5 Earnings after tax 1.5 1.4 Average Total Assets 15.5 15.1 Solution: The consultant is fully aware about the Du-Pont analysis and how he can use it to find about the real reasons of less ROA. So, first of all he performs analysis on data of PM: Net Profit margin = EAT/ Sales 2016 = 6.2/80.6= 7.69 2017 = 6.5/80.8 = 8.04 Investment Turnover = Sales/ Average Total Assets 2016 = 80.6/69.2 = 1.16 2017 = 80.8/70.2 = 1.15 By Du-Pont analysis ROA= Net Profit Margin*Investment Turnover 2016 = 7.69*1.16 = 8.96 2017 = 8.04*1.15 = 9.26 After doing this analysis the consultant gave them recommendations: 1. He explained the company ‘PM’ that they were just concentrating on increasing their net profit margin which is only one factor in calculating ROA. They also need to concentrate on other important factor which is Investment turnover. 2. They can improve their turnover by increasing their sales by employing effective marketing strategies. They can have better utilisation of assets to improve their Investment turnover. As it is clearly visible from the data they are using large amount of assets to generate sales. Analysis on data of IT: Net Profit margin = EAT/ Sales 2016 = 1.4/138.5 = 1.01 2017 = 1.5/140.2 = 1.07 Investment Turnover = Sales/ Average Total Assets 2016 = 138.5/15.1 = 9.17 2017 = 140.2/15.5 = 9.04 By Du-Pont analysis ROA= Net Profit Margin*Investment Turnover 2016 = 1.01*9.17 = 9.27 2017 = 1.07*9.04 = 9.68 After doing this analysis the consultant gave them recommendations: 1. Unlike company PM they were concentrating only on Investment Turnover. But there net profit margins are very low. 2. To address the issue of low net profit margins the consultant advised them to economise their costs or reduce their expenses. This will lead to increase in EAT and hence the net profit margin. So by using Du-Pont analysis consultant could clearly bring to the fore the areas which required more attention from management. By using both these problem statements I wanted to drive home the point that ROA is very important factor in assessing the firm’s profitability. It is more important for companies having huge chunk of unused assets. It highlights the importance of proper utilization of assets, because if unused assets are brought to use the companies can also address the issues of rising NPAs.
Analysis of Company's Annual report
1. Company: Tata Steel Limited Stock exchanges on which it is listed: (a) London Stock Exchange : TTST (b) Bombay Stock Exchange: TATASTEEL (c) National Stock Exchange: TATASTEEL Key Elements required 2017 2016 2015 Total Equity raised 51,934.01 48,912.38 52,464.31 Net debt raised 21,966.77 25,447.37 27,895.47 Debt to equity ratio 0.44 0.50 0.53 Dividends paid 776.97 776.97 776.97 · All the data mentioned is in INR crore Analysis (I) As seen from above company is raising more capital from Equity rather than debt over the years. This shows that shareholders hold a large amount of share in the company. The company is using the capital to increase its assets. (II) The dividend paid by the company has remained same over the last three years. Source: Tata Steel annual Report. http://www.tatasteel.com/media/3669/integrated-report-and-annual-accounts-2016-17.pdf 2. Mahindra & Mahindra Limited Bombay Stock Exchange: M&M National Stock Exchange: M&M London Stock Exchange: MHID Source: Money Control.com Key Elements 2017 2016 2015 2014 2013 Total Equity 25,669.56 21,696.40 19,244.30 16,780.40 14,648.08 Net debt raised 2,737.43 1,843.55 2,620.38 3,745.16 3,227.07 Debt to equity 0.11 0.1 0.14 0.22 0.22 Dividends paid 720.92 745.31 745.31 862.25 798.17 · All the data mentioned is in INR crore Analysis: 1. It can be clearly seen that company is relying on more and more equity as debt to equity ratio is decreasing consistently. 2. The company’s dividend policy has been in general consistent. 3. Housing Development finance corporation Bombay Stock Exchange: HDFC National Stock Exchange: HDFC London Stock Exchange: HDFCBX Key Elements 2017 2016 2015 2014 2013 Total Equity 89,462.35 72,677.77 62,009.42 43,478.63 36,214.14 Net debt raised 717,668.53 599,442.66 496,009.20 406,776.47 329,253.58 Debt to equity 8.022 8.25 7.99 9.355 9.09 Dividends paid 0.00 2,401.78 2,005.20 1,643.35 1,309.08 · All the data mentioned is in INR crore, Source: Money Control.com Analysis: 1. As we can see it raises more funds through Debt than Equity. 2. It didn’t pay dividends to its shareholders in the current financial year. IV INFOSYS Bombay Stock Exchange: INFY, National Stock Exchange: INFY Key Elements 2017 2016 2015 2014 2013 Total Equity 68,017.00 57,157.00 48,068.00 42,092.00 36,059.00 Net debt raised 0 0 0 0 0 Dividends paid 5,915.00 5,570.00 5,111.00 3,618.00 2,412.00 All the data mentioned is in INR crore , Source: Money Control.com · Analysis: 1. It can be seen that Infosys doesn’t raise capital through debt 2. It pays large amounts of dividends to its shareholders. V Ashok Leyland Bombay Stock Exchange: ASHOKLEY, National Stock Exchange: ASHOKLEY Key Elements 2017 2016 2015 2014 2013 Total Equity 6,126.07 4,492.33 4,096.89 3,273.96 3,158.46 Net debt raised 1,344.96 1,984.38 2,591.34 3,883.91 3,504.82 Debt to equity 0.22 0.45 0.63 1 1 Dividends paid 325.40 270.36 128.06 0.00 159.64 All the data mentioned is in INR crore , Source: Money Control.com Analysis: 1. It raises funds equally through Debt and Equity. 2. It is paying generally a consistent amount of dividend except in the year 2014. Combined Analysis: 1. DEBT& EQUITY: Except HDFC all other companies raises funds through Equity. Another exception is INFOSYS which doesn’t raise any DEBT. 2. DIVIDENDS: Generally all the companies are paying consistent dividend except Ashok Leyland & HDFC which didn’t pay for one year each.
The Internet of Things (IoT) is the latest fad in the technology sector. IoT is the concept of connecting electronic devices to the internet. This includes everything from mobile phones, washing machines, HVAC systems, lighting and almost anything else that one can think of. IoT is about connecting companies, people, technology, and devices in real time. IoT attempts to solve many daily life problems, manufacturing limitations, agricultural issues via automation. It builds over core concepts of sensing and actuation, monitoring and analysing real world parameters and using wireless or wired connectivity for remote control and automation. Next big step in the emerging technology is application of artificial intelligence. IoT has applications in areas of manufacturing, transportation, insurance, agriculture, smart homes and buildings, electronics, utilities, and defence. Bosch, GE, Google, Amazon, Hitachi, IBM, Samsung, Cisco, Fitbit, and AT&T are few of the big names which are investing and expanding in IoT research, development and prototyping. Some of these companies are working towards developing and providing IoT infrastructure for use by other companies in developing IoT products and services for consumers. of smart homes. Google’s acquisition Nest is selling thermostats and fire detectors which can learn patters and interact with their users. be used to monitor and control energy distribution, embedded networks and cyber security products. with traditional and cloud databases. essageSight, for machine to machine data transfer, giving normal objects mobile like capabilities. data analysis of IoT data. frastructure, has created SmartThings that includes connected motion sensors and plugs, which are linked to a central hub and can be monitored through a mobile app. There are many real life consumer relatable solutions that IoT can offer. Many of these have been implemented and being expanded slowly. – Risk assessment based on actual usage data is a strategic component of many insurance companies. For instance, wireless sensors and devices installed in vehicles can gather driving behaviours like speed, hard braking, clutch driving, sudden turning etc. and thus provide insurance companies with tons of data points which were inaccessible before. car maintenance data of its users in order to keep them from damaging the vehicle and enforcing traffic regulations. ased retail outlet in Seattle, set retail industry a-buzz with its powerful demonstration of the technology. Customers can just walk in to the store; pick up the desired grocery from the shelves and leave. Carefully designed sensors, cameras, AI and ML algorithms, along with the interconnected system of user’s phone and payment account with do the rest of the job of determining the cart, its value and billing. spaces. Sensors can determine patterns of usage and switch the systems on and off accordingly. Moreover, lighting and HVAC systems can be controlled and scheduled remotely. s in residents’ mobile apps. IoT security systems can also facilitate in monitoring and securing premises from break-ins or other hazards. Any irregularities are immediately alarmed to the relevant physicians and appropriate medical help can be provided. There are multiple speculations about viability and sustenance of IoT in future. Many are pondering about its business viability and long term utility. There are doubts whether IoT can offer any quantifiable value aside from entertainment or limited convenience. But, current trends and above cited examples show quite positive and promising results. It seems that IoT isn’t just a superfluous fad and is here to stay for long.
Analytics in Financial Industry Data is the basic requirement for any decision. The amount and velocity of data is increasing every day. It is available in on the premises as well as on the cloud. Now to extract the useful insights from the huge data and to capitalize on the opportunities that are available analytics is playing a dynamic and vital role. Data and analytics guide every interaction, drive every process and ensure optimal outcomes. Data analytics is the buzzword today because along with traditionally being used in decision making, it is also used at the places where it doesn’t existed before. Predictive analytics is used these days to predict the behaviour of the customer to augment the customer’s internal & external experience. It not only recommends the best action to drive the process but also automatically triggers it. It can coordinate lots of processes and decisions thus acting as the brain of the organization. One major example in which data analytics was used at a big stage is the 2012 US presidential elections. Mr. Obama employed 100 data experts to extract information from a huge database using predictive models to gain a competitive edge. So an analytics driven campaign helped him in winning the elections. In the report published by International Data Corporation it was mentioned that the spending on business analytics services will be $89.6 billion in 2018 which was $51.6 billion in 2014. This represents compound annual growth rate (CAGR) of 14.7. Use of analytics in financial services industries Economic slowdowns, increasing demands from customers and regulatory pressures are the challenges that the financial industry is facing today. Also the stress on digital economy has lead to explosive data growth. Due to increasing social media usage the consumer awareness about the financial products is increasing today. All these factors have forced the financial services industries to use data analytics to extract not just the useful but also actionable insights from the data. Analytics has brought a major change in the financial services industries in areas such as risk & compliance, marketing, consumer and commercial banking, etc. So three broad functional areas across which financial industries have adopted analytics are operations, risk and marketing. 1. Operations: The basic use of analytics here is to reduce the costs. This is done using supply chain, workforce and IT operations analytics. 2. Risk: Analytics help to manage risks. This is done by fraud prediction, risk assessment, regulatory compliance such as BASEL, CRAR etc and loss forecasting. 3. Marketing: Analytics is done to grow the business. It involves market segmentation & sizing, market mix optimization and customer satisfaction. In risk mitigation analytics help in making data driven decisions to help mitigate risk. Natural language processing (NLP) is a technology that can analyze text and then digest the meaning for it. It is now being used in the area of fraud detection as it can read employee’s mails and also process and then digest new regulations. NLP can also access that which of the banking areas or products can be at the risk of non compliance. Natural language generation (NLG) technologies are also being used to generate AML suspicion activity reports. Analytics can also be used to perform risk based pricing and scorecards because risk models which follow the regulatory requirements can be made using tools. So analytics help financial services industries to detect, prevent and mitigate risks in real time. Customer analytics in financial services industry will involve: 1. Social media analytics: This will help in enhancing the online experiences for customers. Also new trend emerging in social media analytics is tracking the sentiment of customers on social media platform which can in turn help in developing strategies for different products. 2. Customer lifecycle analysis: The most important application of analytics is customer segmentation and targeting. Acquisition and churn analytics is also done to estimate customer lifecycle value (CLV). 3. Campaign Management: Analytics can help to discover the opportunities to cross sell and up sell. The marketing campaign’s effectiveness can be judged based on return on investment (ROI). Analytics can also help in providing customer with real time customized and personalized product offerings. Almost every organization in financial services industry is using data analytics these days to help them optimize their processes and serve their customers in a better way. Royal Bank of Scotland (RBS) uses a big data analytics software SAS to improve its customer service. The software helps them to analyze and visualize large amounts of data thus the customers complaints are handles in a better way. The software tells them the errors made by their staff. They are thus aware about what causes complaints and how they can be resolved. So the key aspect of improving customer experience is taken care of by SAS. RBS is targeting £100 million investment in developing analytics technology. The other use of analytics they are targeting is to send automated text messages to inform the customer that their cash is safe if they have left it accidentally after withdrawing from ATM. They are using the technologies such as Cassandra and partnering with data driven start-ups like Pegasystems. JP Morgan uses data analytics in various ways. The investment banking giant uses the Hadoop tool to leverage big data for analytics. They have massive amounts of data due to a huge customer base. So Hadoop is used to process this data that include mails, social media posts and other information that cannot be analyzed by conventional means. The Hadoop can store bulk of data from various banking products. Along with the above mentioned uses Hadoop helps JP Morgan to detect patterns, risks and find any opportunity available to make money. Another platform used by JP Morgan is Sqrrl’s to help analyze and integrate cyber datasets securely. Many other analytics tools like Equities Analytics, Correlation Analyzer and Data Query are also used by JP Morgan. Pitfalls that organizations face when using data for decision-making A survey was consisted by Insight IQ in which 5000 employees of 22 global companies were evaluated. The motive was to find employees who have strong analytics skills and are best equipped to make decisions based on data analytics. It was found that only 50% of managers and 38 % of employees fell into this group. So this survey clearly highlights the shortage of skill set among employees when comes to decision- making using data analytics. Another problem is that usually reliable information exists but it is very difficult to find. It is like you have a library but no card catalogue. According to a survey done by Harvard Business Review only 44% workers say that they are aware where to look for the information they need. Companies should also try and increase the data literacy of their employees and also incorporate information to decision making by providing those employees right tools. According to Mckinsey director Tim McGuire companies face three major challenges while using data analytics. First is to decide that which data needs to be used, next is having the right capabilities to handle analytics and third is to transform operation by using the insights gained. Lastly still there are many organizations which do not trust the data that they have. Conclusion: So we can clearly see that data & analytics are at the centre of each organization. Especially the financial services industries are employing lot of analytics in their processes. Analytics is helping these organizations to serve their customers in a better way and also giving such insights that were not possible with the conventional ways. According to research report from Gartner the data analytics will take the centre stage when huge data will be generated by embedded systems and as a result of which vast amount of structured and unstructured data will be needed to be analyzed. Organizations will have to filter the vast amount of data coming from internet of things and social media. They need to then make sure that right information is delivered to right person at right moment. So, analytics is slowly becoming deeply embedded everywhere. Still there are the pitfalls that organizations face while using data for decision making. So there is a need to design an end-to-end architecture to fulfil the requirements of growing businesses. Organizations need to develop analytics skill in the employees instead of waiting for someone else to extract useful insights from the data. Analytics should go viral. This can be done by using a pragmatic approach to foster the development of analytics competency at the point of data ingestion. Cloud should be incorporated in the end-to-end architecture. Also the development of both professional as well as technical skills should be focussed upon to make en-to-end architecture a success. So, in the words of Tim McGuire, a McKinsey director analytics will define the difference between the losers and winners going forward.