On the International Day of Charity, marked annually on 5 September, United Nations Secretary-General Ban Ki-moon called on people everywhere to volunteer and act charitably in the face of human suffering.“At a time when the need for humanitarian assistance has never been higher and when there are more refugees and displaced people than at any time since the end of the Second World War, charities play an increasingly vital role in meeting human need,” the UN chief said in a message.The International Day coincided with the anniversary of the death of Mother Teresa, who was awarded the Nobel Peace Prize in 1979 for her work to overcome poverty. The UN chief recalled that upon receiving the prize, she famously gave the money that came with it to some of the poorest people in India.He said such expressions of solidarity help us in our shared quest to live together in harmony and build a peaceful and sustainable future for all.“United Nations development and humanitarian agencies also rely on donations from the public as well as the generosity of governments to continue their lifesaving work in response to development challenges, natural disasters, armed conflicts and other emergencies,” Mr. Ban added.He also highlighted that the resources, knowledge and ingenuity of philanthropic and volunteer organizations will be invaluable partners in implementing the new 2030 Agenda for Sustainable Development, which will be adopted by UN Member States at the end of month.Ban Ki-moon said this is in line with the Addis Ababa Action Agenda, a series of bold measures adopted by UN Member States in the Ethiopian capital this past July to overhaul global finance practices and generate investments for tackling a range of economic, social and environmental challenges.
De-risking through diversification seems to be the way for business leaders with media baron Subhash Chandra and pharma tycoon Dilip Shanghvi vying for oil and natural gas blocks in the country.According to a report in Business Standard, these two business leaders took part in the auction process of small discovered oil fields conducted by the Narendra Modi government on Monday.Shanghvi through his firm Sun Petrochemicals submitted bids for seven fields, which include five in Gujarat and two in Mumbai offshore. The company’s website says that it has a manufacturing facility in Nagothane in Maharashtra producing acetylene black used in battery manufacturing and other niche applications.It has also diversified into the upstream hydrocarbon business through Sun Oil and Natural Gas (SONG) division dealing with exploration and production.Meanwhile, Subhash Chandra has joined the auction through Essel Middle East and has shown interest in two fields in Assam and Gujarat.Based in Dubai, Essel Middle East is involved in the business of mineral mining, oil explorations and acquisition of natural resource assets.Notably, India’s first auction of small discovered fields witnessed subdued response from global bidders on Monday. However, domestic players showed huge interest with a lot of firms vying to put their hands in oil and gas assets.The fields with estimated oil and gas assets of around $625 million received 134 bids from 42 companies for 34 contract areas. Only five foreign bidders participated with most big names staying away from the auction process.The blocks put for auction were given up by Oil and Natural Gas Corp (ONGC) and Oil India Ltd (OIL) due to their small sizes.
The Bombay Stock Exchange (BSE) logo is seen at the BSE building in Mumbai, India, January 25, 2017.Reuters fileBenchmark indices had a tepid opening on Monday as there were no major global or domestic cues. Select stocks such as Tata Consultancy Services (TCS), GAIL (India), Hindustan Unilever Ltd. (HUL) and Bharti Airtel were trading with modest gains at around 9.25 am. The TCS board will also be meeting on Monday to consider share buyback. The biggest highlight of the day is the auction of players for Indian Premier League (IPL) 2017 that began a few minutes ago; watch the live updates here.Read: India’s top 10 companies as of February 17, 2017 [PHOTOS]The BSE Sensex was down 48 points at 28,420, while the NSE Nifty was 7 points lower at 8,820.In related news, the Goods and Services Tax (GST) Council will be meeting for the 11th time on March 4 and 5 to discuss and approve the enabling legislations — CGST, IGST and SGST — after having approved the law to decide compensation payable to states after implementing the GST, hopefully from July 1, 2017.CGST stands for Central GST, IGCST for Integrated GST and SGST is State GST.State Bank of India (SBI) has also informed the BSE that bank employees have given a notice to the bank of the strike on February 27.”State Bank of India has informed BSE that the Bank have been advised by the Indian Banks’ Association (IBA) that members of United Forum of Bank Unions (AIBEA, AIBOC, NCBE, AIBOA, BEFI, INBEF, NOBW, INBOC & NOBO) have served notices of strike on Indian Banks’ Association, informing their decision to go on strike on February 28, 2017 in all the banks on certain issues,” the lender said in a regulatory filing on Sunday.”All India State Bank Officers’ Federation and All India State Bank of India Staff Federation, being part of UFBU will also participate in the said (strike),” it added.SBI shares were trading marginally higher at Rs 269 apiece.BSE shares were almost flat at Rs 969 apiece on the NSE.During the legal drafting of the CGST, SGST and IGST laws, certain contentious issues came to the fore and it was necessary to place all the issues before the Council again to take specific directions. So, the legal committee of the GST Council sought clarifications from us today. “These will be incorporated, and at the March 4-5 meeting in Delhi, these laws will be cleared,” Union Finance Minister Arun Jaitley told reporters after the Udaipur GST meeting last Saturday.
Wake up your weekends with a free flowing Champagne Brunch at Shangri-La’s – Eros Hotel’s new all day dining restaurant Tamra. This newest addition is a gastronomical bouquet, offering authentic South East Asian cuisine as well as Japanese, Indian and European fare from its five interactive cooking theaters. The cooking theatres at Tamra each featuring a different culinary style showcase the restaurant’s ‘world on a platter’ concept as well as stages for our talented Chef’s engaging performances. Also Read – ‘Playing Jojo was emotionally exhausting’The exquisite buffet menu offers you a plethora of choices, featuring an exotic live grill where you can order medium rare Tenderloin cooked in red win and prawns grilled to perfection and served with a creamy sauce. The California Maki rolls, Spicy Tuna Sushi and Ebi Tempura make up an impeccable Sushi platter. The Asian Oriental cooking theater offers braised Pork Belly with Thai Chili sauce and exquisite flavorful dim sums. For the big finish, indulge yourself with the silky sour Lemon Meringue in a crispy Filo pastry or a decadently sinful creamy, airy Blueberry mousse. The Sunday Brunch at Shangri-La’s – Eros Hotel, New Delhi is nothing but decadent.Every Sunday, Tamra organises a special menu for kids inclusive of a do it yourself sundae counter with all the works.
7 min read Today, the importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.Although machine learning dates back to the 1950’s, it’s presently more subject to practical, large-scale applications than it has ever been. Big data, on the other hand, became a thing in 2013, after it was discovered that 90 percent of the world’s data was produced in the previous two years.The spate of data generation, therefore, became a challenge as well as an opportunity. As an opportunity, big data enables businesses to not grope in the dark but make wise real-time decisions by providing them with insights into various market situations and ensuring a better understanding of consumers’ behaviors and preferences.It’s however noteworthy that big data by itself is of little value. To be useful, it has to be operated on by various analytical methods, many of which don’t go beyond providing mere statistical insights.Machine learning comes in handy as it goes further to unveil the hidden potentials of big data by producing and implementing solutions to complex business problems.Here are some four ways by which combining big data with machine learning has helped improve business intelligence, and some takeaways for business owners.Related: 5 Reasons Machine Learning Is the Future of Marketing1. Facilitating Customer SegmentationIt is not uncommon to find distinct groups — each comprising individuals who share a wide range of similarities – within a business’s customer base. In fact, discovering such groups is a crucial step every business should take.Fortunately, machine learning clustering algorithms are perfect for achieving this kind of a segmentation. Many such algorithms are unsupervised in that they don’t require special human direction to operate. Rather, an unsupervised clustering algorithm requires only data for exploration, so as to discover similarities and differences (where they exist), and come up with distinct clusters based on a number of features.In 2009, Orbitz created a machine learning team to facilitate segmentation, among other reasons. Three years later, it discovered a pattern from the data at its disposal: Mac users were willing to spend as much as 30 percent more per night for hotel rooms, when compared to Windows users. This discovery made it (Orbitz) swing into action in a way I’ll touch on, shortly, as it obviously helped to lay the grounds for segmenting the business’s customer base based on the relative propensity to pay for varying hotel types.Your business can also harness the power of machine learning and big data to achieve segmentation. But, first, you need to discover whether segmentation holds any potential benefit for your organization. If you believe it does, then it will become necessary to invest heavily in data analytics, make your business machine learning ready, and then employ a machine learning team. As you’ll soon see, machine learning will not only help to accurately and efficiently make sense of the data at your disposal, but also help to implement core business strategies.Related: Want to Be More Like Amazon? Start By Making Your Startup More Data-Driven.2. Making Targeting Feasible and Effective:Merely knowing that your customer base is composed of different groups doesn’t cut it -– you have to devise means to cater to divergent needs.Orbitz responded to the earlier stated discovery by targeting customers differently: costlier hotels were displayed to Apple users. It’s quite reasonable to suggest that this move was a wise one, for such a strategic targeting must have been highly profitable.On the other hand, it’s sometimes necessary to view one’s customer base as comprising different individuals with various preferences rather than a conglomeration of different groups. This perspective will make it more pragmatic to tailor products to each individual based on his or her specific behavior and perceived preferences. Again, machine learning, under the aegis of big data, facilitates this.Google, for example, uses big data to better understand your preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query you make. This is why your past choices (for example, the sites you’ve visited) end up impacting on some of the results you’re shown.Machine learning and big data are also breaking grounds in targeted advertising. Pixar, for example, targets its audience with different movie advertisements which are based on learned preferences. Netflix also estimates that its algorithms produce $1 billion a year in value from customer retention, thanks to the “Netflix addiction” which is mostly spurred by accurate recommendations fostered by both user and item-based collaborative filtering.In other words, business owners need to understand that targeting consumers differently makes a lot of sense, and that machine learning makes personalization, which is key to providing a better user experience, possible. Say you run an ecommerce business, machine learning can help you personalize your ads so that people see only products that are most likely suited to their needs. This will definitely add an unobstrusive touch to your platform and may improve your bottom line by increasing sales and engendering customer retention. Again, the “Netflix addiction” speaks volumes about the potentials of machine learning-induced targeting.Related: Top 10 Best Chatbot Platform Tools to Build Chatbots for Your Business3. Fostering Predictive Analysis:After gaining insight into consumer behavior from big data, you’ll want to use machine learning to develop generalizations and thus make predictions regarding various business issues.In other words, machine learning models can learn behavior patterns from data and determine how likely it is for a person or a set of people to take certain actions, such as subscribing for a service. This makes it possible to anticipate events and make futuristic decisions.The American Express Company used big data to analyze and predict consumer behavior by learning from historical transactions. Through this, it was able to predict 24 percent of accounts in its Australian market that were about to close within four months. T-mobile also uses big data to predict consumer fluctuations.To make these kind of predictions, you must employ machine learning expertise to help grapple with your business’s data. Classification algorithms are usually used as the foundation for such predictions.Related: Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative4. Providing Foundations for Risk Analysis and Regulation:Big data enables machine learning models to extensively analyze and regulate risks.For fraud detection, American Express applies machine learning to analyze large historical datasets. In fact, the machine learning system is considered to differ from the previously existent fraud detection systems which included only manually created rules, and is better off because it’s likely to improve with more data inputs. It also saves the company millions of dollars, said Bernard Marr. Your business can also make use of machine learning to decrease financial irregularities. Many organizations are, in fact, developing systems to make the process easier. IBM, for example, provides financial institutions with a machine learning system on IBM z/OS in order to aid financial risk management. This system pays particular attention to credit scoring and is targeted at deducing credit worthiness which it uses to gauge risks.Employing machine learning models can go a long way in ensuring anti-money laundering compliance, detecting rouge trading and other trade anomalies, so it’s best to not starve your business of these elements of sanity.Related: A Humanoid Robot Called Sophia Mocked Elon Musk After Being Asked About the Dangers of AIMachine learning and big data are presently gaining the attention they deserve, and there’s no doubting that both depend on each other’s strength. More importantly, both have consistently made major impacts on how we undertake business operations. This article revealed some four ways by which a combination of machine learning and big data, if applied, can be a fillip to business intelligence. It’s therefore left to you to up your game as an entrepreneur. Enroll Now for Free Opinions expressed by Entrepreneur contributors are their own. Free Workshop | August 28: Get Better Engagement and Build Trust With Customers Now January 29, 2018 This hands-on workshop will give you the tools to authentically connect with an increasingly skeptical online audience.