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Beth Alexrod,eBay公司人力资源高级副总裁,电子商务巨头说,“通过数据分析有很多的价值创造和增加”, “吸引外面的人才到公司是否能做一个更好的工作,或做预测分析谁可能会离开并且原因是什么,这样你就可以在这个点到达之前提前介入尝试改变,有大量的这样的机会。”









What exactly is going to change in this era of Big Data?

Big Data is possibly the biggest buzzword for the past five years. The technology, still in its nascent stages, is rife with potential and everybody is enthusiastic about the exciting prospects that it presents. But what’s exactly going to change in this era of big data? The answer is “a lot”.

Where does big data come from?

Big data is a common term used to describe data or data sets, both structured and unstructured, that are too large and complicated to manipulate with the standard tools or methods available.

Companies like Amazon, Netflix, and eBay generate “data exhaust”, the by-product of all digital activities such as buying, selling and this data provides great insights about an individual and hence is of immense importance to marketers and business entities. Besides, there is the truckload of data, being generated by smartphones and our social networking activities such likes and comments that tell a lot about our preferences, location, and even vital health stats. Then, there is this onset of the wearable technology, happening at a rapid pace, which fills the last gap in constant connectivity.

With so much of data at disposal, the possibilities are huge. Some predict that Big Data will be as vital to businesses and people as Internet has become. By assessing human behaviour across a huge sample set, it will become easier to predict future trends and businesses would know beforehand as to what customers really want. There is this often cited example of Big Data being used as a perfect sales and business driver, where Target, the mass market retailer came to know of a customer’s pregnancy and so started sending sales flyers offering deals on baby furniture and maternity items, that too before she broke this news to her family. This is only the tip of the iceberg.

An excerpt from World Economic Forum’s whitepaper titled Big Data, Big Impact: New Possibilities for International Development, “

“In the wake of Haiti‟s devastating 2010 earthquake, researchers at the Karolinska Institute and Columbia University demonstrated that mobile data patterns could be used to understand the movement of refugees and the consequent health risks posed by these movements. Researchers from the two organisations obtained data on the outflow of people from Port-au-Prince following the earthquake by tracking the movement of nearly two million SIM cards in the country. They were able to accurately analyse the destination of over 600,000 people displaced from Port-au-Prince, and they made this information available to government and humanitarian organisations dealing with the crisis.”

There are other implications as well in areas like health, education, financial services and agriculture, not to forget corporate decision making and even talent recruitment.

Publishing: An unlikely beneficiary

Publishing is an unlikely industry to have any direct or indirect relation to big data. Before digital reading was invented, publishers couldn’t do much other than analyzing sales by region and demographics, and hear what people or reviewers thought of the content. With digital reading arriving on the scene, it is now possible to know how a customer engages with a book, did he even read it or just sifted through page, how much time did he took to finish the book and last but not the least, how did he find it. By knowing what books readers find engaging, publishers can take informed decisions on which authors and franchises to invest in. Books that had a higher completion rate yet low sales could be promoted with a different strategy and can be presented to a wider audience.

Similarly, engagement metrics can also reveal if an author is worth investing in or not and what size the investment should be. Reading data could not only reveal how many people were finishing the books of that particular author, but also how fast they were finishing it. Engagement data in the form of completion rates alone can take a lot of risk out of the equation.

Talent hunting becoming more meaningful

Beth Alexrod, Senior Vice President of Human Resources for eBay Inc, the e-commerce giant says, “There’s a lot of value to be created and added through data analytics,” “whether it’s doing a better job spotting talent outside to attract to the company, or doing predictive analysis of who is likely to leave and what are the factors, so you can intervene before that point is reached to try to change the trajectory. There’s a ton of opportunity there.”

Just like financiers and marketers use data to fore_cast future earnings and read consumer habits, recruiters can now use people analytics as an important driver of corporate decision-making. The biggest challenge here that stands in way of implementation of Big Data in the search for talent are HR practitioners who lack the necessary data skill sets. The demand for analytical skills in the workforce has increased substantially and it doesn’t help anymore to have just few experts who are able to analyze data.

A big leap in understanding Human Behaviour

There is no doubt about the fact that computers are capturing every single detail of what we are doing through a range of always-connected devices that we use. Just think about the immense power that big data has inspired in businesses, they can finally predict who, where and when customers will buy their products.

– Social media addiction has led us to reveal very fine detail about ourselves. Market research companies use this data by scraping the web to find relations between human sentiments and brands, products & services.

– Data intensive domains like healthcare, finance and e-commerce are a repository of data on individual human behaviour and outcomes.

– Speech analytics has caught on in a big way as call centers record conversations. With improving speech recognition technology, the range of voice-based data and its meaning that can be captured in an intelligent format grows.

However, there is still doubt whether all this data can actually make any sense. Are there enough technologies and methods to derive meaning from all this data and predict the future in business? Let us know your comments.






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