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Among the topics of Bangalore\u2019s top tech event, Nasscom PC tre<\/a>nds in Big Data had a full half a day of panels, pitches and keynotes to better grasp what it\u2019s all about.<\/p>\n In 2008, the number of devices connected to the internet was more than the world\u2019s population<\/strong>. The forecast for the next decades is without appeal: we\u2019re living in a world where machine to machine (M2M) is bringing a huge technological and cultural shift.<\/strong><\/p>\n <\/p>\n The main paradigm set up by the use of massive, big data is certainly the cognitive shift it operates on our own understanding of the world.<\/p>\n In a universe where information was so far scarce, we had as human beings to devise theories, hypothesis, and in most cases to analyse samples of data or population to verify them.<\/p>\n It was a decent way to proceed, although the hunt for causality was highly biased, and always on small clusters of data.<\/p>\n Today, even if the data is too huge in its volumes, too messy in its different formats, there\u2019s an incredible amount of value stemming from the correlations we can detect.<\/strong><\/p>\n All experts agree on point, as stressed in Big Data<\/a>, an easy to read vulgate by Viktor Mayer-Sch\u00f6nberger and Kenneth Cukier, messy data is always better than handpicked and precise data.<\/p>\n The cultural, psychological shift we need to operate is also quite impressive<\/strong>. Some, like Chris Anderson have been trumpeting the \u201cdeath of theory\u201d<\/a>, and popular movies such as Minority Report show well how correlation, and probability, is a form of power we are unprepared to.<\/p>\n <\/p>\n Back in Bangalore, the afternoon dedicated to trends \u00a0in Big Data was introduced by Atul Batra<\/a> from \u00a0Manthan<\/a>, a world leader in Advanced Analytics, \u00a0Business Intelligence, Optimization and Analytics- \u00a0driven Execution solutions for Retailers.<\/p>\n In his speech, Atul offers 5 insights to better use Big \u00a0Data for businesses, in particular in the retail world:<\/p>\n Insight #1: Focus on one vertical only and get intelligence out of that<\/strong>, see what are the pain points, the change in processes, what is still done manually, what are the gaps, then you have a good product proposition. For instance, there are about 50 different parameters you can check on as a retailer. Try to focus on one move before going wild on all the other data.<\/p>\n Insight #2: Close the loop between analysis and decision<\/strong>. Execute on the data, don\u2019t let the loop open, and do numerous tests (still keeping insight #1 in mind: it\u2019s easier to execute on one small field first). Say you have a big analytics platform: how are you going to link it up with marketing execution? And then engage on a personal tone with customers?<\/p>\n Insight #3: Predict and Prescript Business Outcomes<\/strong>. How to mark on 10% of a certain category of products? How does it impact the other categories? Again, you need to test and see how changes in a few variables will impact your global business. If you change prices every week, how do you inform mobile sales forces?<\/p>\n Insight #4: Drive real-time personalisation<\/strong><\/p>\n How do you link up different segments of customers with different set of promotions, in real time?<\/p>\n Insight #5: Leverage platforms<\/strong><\/p>\n Don\u2019t reinvent the wheel, a lot of solutions exist today.<\/p>\n The different speakers then chose to shed the light on a few case studies to understand how Big Data impacts their businesses.<\/p>\n Vijay Raghavendra<\/a>, VP Engineering at Walmart Labs<\/a>, showed how Big Data is being applied to the biggest retailer in the world in a startup mode.<\/p>\n In his case study, Vijay showed how the title of each product on Walmart.com was tailored in a unique way for each separate set of keywords<\/strong> typed in in Google.<\/p>\n Take a basic tote bag with a humorous message. Depending on what query lead to the product, Walmart can guess part of the intent (buying a funny bag, buying a simple bag, buying a bag in organic cotton), and offer a product name based on this intent.<\/p>\n In another case, one of India\u2019s top TV shows, Satyamev Jayate<\/a>, used Big Data to change, on a very short notice, the upcoming episodes.<\/p>\n The show itself is more like a documentary, where sensitive societal are discussed, generating a huge conversation online and on social media<\/strong>. After just 13 episodes, writes GigaOm<\/a>, the stats are huge:<\/p>\n \u00ab\u00a0400 million viewers on Indian television and across the world on YouTube.<\/em><\/p>\n More than 1.2 billion people have connected with Satyamev Jayate across its website, Facebook, Twitter, YouTube and mobile devices.<\/em><\/p>\n More than 8 million people have contributed a total of more than 14 million responses to the show\u2019s content via Facebook, web comments, text-message votes and a telephone hotline. More than 100,000 new people respond each week.\u00a0\u00bb<\/em><\/p>\n Thanks to an Indian consultancy (Persistent Systems<\/a>), the show was able to organise a follow-up on radio with the best messages, as well as influence the government. In one of the episodes, talking about female feticide, the Big Data team showed how 99.8% of the conversation<\/strong> agreed to set up a fast track court to prosecute doctors performing such operations. The Rajasthan high court approved the idea<\/a> just after the show.<\/p>\n These stories and case studies must convince you that a huge cultural shift is happening right now<\/strong>, with a transformative impact for all businesses.<\/p>\n Find out more about big data and its trends here:<\/p>\n Big data in Africa: How an insurance company provides an optimized cover for farmers in Kenya<\/a><\/p>\n The best of ConDatos, the top Open Data event of Latin America<\/a><\/p>\nUnderstanding trends in Big Data: going from causality to probability<\/strong><\/h3>\n
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<\/p>\nTrends in Big Data for business: 5 Insights to make it actionable<\/h3>\n
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<\/p>\nCase studies in trends in Big Data<\/h3>\n
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