Imagine you have stepped off the field, and your team has lost. Obviously you can look at it as a failure, but if you closely introspect, you could look at it as an opportunity to improve. Why? Because of those embedded sensors in your jersey that are tracking your every move, and proper data analysis of that data will help you get better in the next game.
Yes, you heard it right – data plays a key role in the world of sports. Collected data now guides team towards victory. Whether you are a cricket fanatic or a rugby supporter, you will find several instances that would show how big data is influencing this mega industry. Today, the professional sports industry stands at a whooping value of $90 billion – just as other industry domains are utilizing the power of big data and putting it to good use, why should sports industry lag behind? They also are looking for ways to enhance their athlete’s performance and improve organizations’ and fans’ total experience. Continue reading “How Big Data is Influencing the Sports Industry?”
In 2018, companies have decided to invest $3.7 trillion on machine learning and digital transformation so as to embrace a promising return on that sizeable investment for professionals involved in managerial roles. Nevertheless, 31% of the companies using the potent tools of machine learning and data science are not yet tracking their ROI or are in no mood to do so in the near future.
But to be on the side, ROI is very crucial for any business success – if you fail to see the ROI you expect from data science implementation, look into bigger and complex processes at work – and adjust likewise.
Take cues from these 3 ways, explained below:
Implementing data science strategy into C-Suite
According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost nonexistent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite basically gets its name from a series of titles of top level executives who job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data and unveil new trends and opportunities that the company has been attempted to tab for years.
The core responsibility of a CDO is to address a proper data management strategy and then decode it into simple, implementable steps for business operations. Its prime time to integrate data science into bigger processes of business, and soon company heads are realizing this fact and working towards it.
Your time and resources are valuable, don’t waste them
Before formulating any strategy, CDOs need to ensure the pool of professionals working with data have proper access to the desired data tools and support or not. One common problem that persists is that the data science work that takes place within an organization is done on silo, and therefore remains lost or underutilized. This needs to be worked out.
Also, besides giving special attention on transparency, data science software platforms are working towards standardizing data scientists’ efforts by limiting their resources for a given project, thereby ensuing cost savings. In this era of digitization, once you start managing your data science teams efficiently, half the battle is won then and there.
Stay committed to success
Implementing a sophisticated data science model into production process can be a challenging, lengthy and expensive process. Any kind of big, complicated project will take years to get completed but once they do, you expect to see the ROI you desire from data science but the journey might not be all doodle. It will have its own ups and downs, but if you stay committed and deploy the right tools of technology, better outcome is meant to happen.
In a nutshell, boosting of ROI is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of your data science strategy, which will help in predicting success accurately and thus help taking ROI-supported decisions.
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Technology is omnipresent. And when it comes to imparting engineering education, technology is the meat and potatoes. Gone are the days of traditional teaching methods practiced within the walls of a classroom, following a set of particular curriculum. They have become a history. These days, technology-powered smart classes are in – they keep students enticed and hooked into learning. Laptops, smartphones and tablets have made gaining access to knowledge anywhere anytime downright easy. Not only that, access to education has enhanced versatility in the form of videos, audios and images that are available right at our fingertips through smartphones and tablets.
Technology is taking a new shape, each day. None other than ace modern engineers and scientists understands this better, and as a result, they try adopting innovative technologies for better, powerful future harnessing newer opportunities.
Data is the most influential strategic asset for companies in a data-powered economy. Data is used to measure the ability of a business to perform notable tasks and operations, and draw significant insights through complex machine learning algorithms.
Gaining access to data is not a problem; but the real issue lies in having the right kind of data that helps companies remain on edge. A large number of them don’t even realize they are supplied with chunks and chunks of bad data, punched with wrong formatting, plenty of duplicates, having missing fields or irrelevant information.
Data is everywhere. There is no shortage of data – even the neophyte entrepreneurs who have just begun their business operations are sitting on mounds and mounds of data – but this often makes us introspect how can we use data to grow bigger, more productive?
Suppose, you have two resumes of two data scientists in your hands: A and B. Both of them possess similar backgrounds and expertise: qualifications, platforms, languages, frameworks, methodologies, industries and more. Theoretically, they are more like the same person – on paper. Yet there are few things that reflect that A is more successful than B – but how you determine that?
Here we’ve whittled down a set of habits or traits of successful data scientists that make them stand out from the rest of the pack.
According to a recently published report from McKinsey – “Alphabet invested roughly $30 billion in developing AI technologies, and Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year.”
Not only companies, but reports suggest Chinese government is pursuing AI technology relentlessly in an attempt to drive the AI innovation, singlehandedly.
With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.
Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.
Shine bright with Big Data
In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!
Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!
Get better, get evolved
Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”
Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.
It’s all in the layout
Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.
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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.
