Big Data, Business Intelligence, Data Science – the digital revolution is here, and it’s evolving steadfastly.
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Soon, data analytics is becoming the life-source of IT. The range of technologies is varied, and the way data is expanding, we are fast moving towards a juncture where analysis of vast volumes of data will be done in a jiffy.
Data analytics is evolving. Coupled with Big Data, it is changing the topography of almost everything.
Each year, more and more companies are allocating their time and budget to exploit and understand vast pools of data strewn across them, both inside and outside company files. Of course, Big Data and Analytics is selling like a hot cake today, but what is its story of evolution? How the technology flourished 10 years back?
Analytics is the key to modern business growth. But, developing it as a highly interactive analytical interface is challenging enough to exhaust the time and resources both. As a result, many businesses are shifting their focus to Embedded Analytics (EA) for their operations, workflows and decision-making capabilities. This new breed of analytics, known as Embedded Analytics helps businesses leverage the power of data to process them in the most useful manner.
Continue reading “Embedded Analytics: How it’s Revolutionizing Businesses Today?”
With data analytics flourishing the manner it is, a lot of you hailing from IT background are sincerely thinking about making the remarkable switch from IT to Analytics. The skills you possess are transferable and the data structure fascinates you. You know very well, you will make more money in analytics and your career pathway will seek you great rewards. Yet something is stopping you from going!!!
What it is? Why are you feeling apprehensive to make the bold move that could change your life and career forever?
Continue reading “How to Take the Plunge from IT to Analytics: Explained”
Since the start of this year, new development in the field of technology has been the hottest topic of discussion at several science symposiums. This blog post sheds some light on what can be expected for 2017, based on 2016 evolutions in Data Science and Machine Learning.
Continue reading “Top 2016 Trends Expected to Turn Fruitful in 2017”
With the Fourth Industrial Revolution looming ahead, many would think that we are already in a digital economy era. Well, somewhat it holds true even. There are countless new apps and software programmes that help people hail a cab, make reservations in a hotel or mop floors by using robotic technology. Smart machines have become really smart to do a plethora of highly adept jobs, which would have been a little bit difficult on the part of humans to perform.
“While technology has long been developed to serve specific business needs, we are now in an era where people are central to the design and development of technologies,” stated Bhaskar Ghosh, group chief executive, Accenture Technology Services. In a recent interview with a leading financial magazine, he talked over Accenture’s Technology Vision 2017 and gave snippets about the latest trends and innovations that have become a pre-requisite to achieve success in the more-than-ever digitised economy.
Can the film industry leverage more on data analytics?
Film making as an industry is as dependent on good marketing as it is on good content.
And it is here that data analytics comes to the picture, for not only does it govern marketing strategies of a Studio but in future it might govern the creative half as well.
For a conventional Hollywood blockbuster, an average of $70 Million are spent within 10-12 weeks and data analytics might direct us as to how much cash needs to be spent and where. Nowadays companies such as IBM are experimenting with Deep Sentiment Analysis, which tries to gauge the market sentiment by listening to the constant stream of content being posted by the users in a given area. The data comes from all sorts of sources, both structured and unstructured, which then needs to be cleaned before gaining any actionable insights from it. Nowadays, companies are working towards developing Market Optimisation Models where they can use historical data to create models, which are then fed current data in order to guide marketing budget allocation decisions. Another way studios are using data analytics is to predict market reaction in USA and Europe by analysing moviegoer’s reaction to the initial run of the movie (usually in smaller markets of Asia). They then proceed to rebrand/improve its offering to make it more ‘commercial’ for a given region.
But does this seemingly endless data and ever improving predictive model point towards a future, where Big Data might tell writers what to write, directors how to direct and actors how to act? If the answer is in affirmative, then are we diluting cinema as an artistic medium? Studios, such as Netflix have now extracted about 70,000 unique characteristics from its movie collection, and now they are analysing how the presence/absence of a characteristic has an impact on the movie revenue/rating/viewing. It then uses these findings to develop and fine-tune the shows it will produce in future. This increasingly ‘scientific’ manner of developing movies is taking over at other studios as well, along with experts fearing that this practice might lead to the industry losing its experimental and creative edge.
With proved benefits, including increased revenue and minimal risk, it is imperative for studios to invest into Data Analytics. It has become imperative to design their marketing strategy using this mine of user data to make their offerings economic, popular, efficient and successful.
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“Big data analytics will help bridge India’s tax gap.” Economic Times
“India’s Growing Big Data Future.” NDTV
“The Big News About Big Data.” NASSCOM
“Big data to boost job openings in 2017: Report” Tech Circle
“Big Data for the next green revolution.” Hindu Business Line
Investigative minded people will definitely relate to this story from almost every child’s formative years. The day they get their hands on a magnifying glass, kids would feign being the most famous detective of all times – Sherlock Holmes with a cap they would focus the magnifying glass on an object and try and derive meaning by studying the details closely. This would be their first lesson in data visualization. Later as we learnt about Mr. Holmes through books of Sir Arthur Conan Doyle many of us may have imagined pursuing a career as a full-fledged detective. In his book A Study in Scarlet is the most vivid description of the inclination Mr. Holmes has for the sciences.
Now that we come to think of it a detective has probably evolved in this technologically driven planet into a modern-day data analyst or an experimental scientist. The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then we use this to build a case through deduction and logic and then you reach a conclusion after analysis. Continue reading “Sherlock Holmes Has Been Doing Data Visualization Before Big Data”