Data Analytics:Future and Scope in India

 

Business Analytics have become vital for the growth and development of the companies1 of today. Large investments are being made in big data analytics to make better business decisions from past data. This past data is being generated by different sources such as business people, marketing, education, engineering, medicine, social media, on-line transactions, call centers, sensors, web logs and telecommunications.

Need for the right analysis:

In this day and age, when the economy is challenging and the business landscapes are changing faster than ever, it is critical for organizations to focus on their critical business issues. It is also useful to understand the nature of these challenges. With increasing competition and plenty of options, consumers are always on the look-out for the next-best-thing. Business Analytics plays a very important role here as it uses statistics and tools to decode consumer insights. This is done based on accrued data, and Business Intelligence that garners key insights that can help predict future behavior, in effect, helping businesses run better. The latest developments in Business Analytics’ technology are playing a crucial role in automating the analysis process. It is also enabling both data analysis experts and business users to interpret data more easily and quickly. Business analytics are key differentiators, which provide a competitive edge to companies across industries.

The Indian talent pool:

Experts believe that In1dia is amongst the leaders in the talent markets. Its success with the IT sector for the last two decades, strong entrepreneurship culture and English language skills have helped India to stay ahead of China and Eastern Europe in the domain of Analytics. The scope is huge in India as many more companies from abroad are outsourcing their analytical requirements to India.

Scope of Business Analytics in India:

The scope in the field of business analytics is ever expanding and is helping it becomemainstream as companies of all sizes and analytics skill levels get into the big data game.Exploring business analytics needs the right focus, right technology, right people, right culture and top management commitment. Companies like IBM, Accenture, and Deloitte are using business analytics tools and coming up with decisions that are useful and profitable.

One needs to acquirea particular skill-set to succeed in a business analytics career. Inquisitiveness, interpretation skills, thorough understanding of tools and methods, ability to do in-depth research and quantitative skills are vital to excel in the subject. Sensing the need to create a workforce that understands the field and is trained to tackle the complex issues related to business analytics; Indian institutes,in association with several premiere colleges, are offering regular as well as executive business analytics courses. Apart from the regular colleges, there are several analytics training institutes that are offering their own business analytics courses or are in collaboration with renowned institutes.

Promising future trends:

Experts believe that the following trends will dominate the world of analytics in 2015.

Analytics will play an important role in data security. Analytics are already transforming intrusion detection, differential privacy, digital watermarking and malware countermeasures.

The Internet of Things (IoT) will continue to grow rapidly in 2015. Analytics tools and techniques for dealing with the massive amounts of structured and unstructured data generated by IoT will continue to gain importance.

Companies will voice their need of routinely monetizing their own data for financial gain.

Growth of Cognitive Analytics.

Relevance of ‘Open Source Solutions’ will regain momentum.

Focus on Tax Analytics- This will simplify the process of recovering overpaid transaction taxes and helping to prevent future overpayments.

Boost in demand for Data Scientists- a hunt for people who can balance quantitative analysis skills with an ability to tell the story of their data in compelling, visual ways.

Companies would become over-critical and cautious about Data Accuracy.

Basis the aforementioned elements and points, it would not be wrong to say that the demand in the Business Analytics market would grow at an impressive pace in the upcoming years.

The Google Driverless Car

Driverless cars are here already... sort of

Much of the autonomous technology used in Google's self-driving cars is already found on the road.You may have seen commercials advertising the Volkswagen Polo's automatic braking or the Ford Focus' automatic parallel parking, which both build on the increasingly common use of proximity sensors to aid parking.

Combine these sensors with the automated-steering technology used for parking, throw in the seemingly old-hat technology that is cruise control and you have the loose framework for a self-driving car.

How many sensors does the car have, and what do they do?

Google’s driverless car has eight sensors.

The most noticeable is the rotating roof-top Lidar – a camera that uses an array of 32 or 64 lasers to measure the distance to objects to build up a 3D map at a range of 200m, letting the car "see" hazards.

The car also sports another set of “eyes”, a standard camera that points through the windscreen. This also looks for nearby hazards - such as pedestrians, cyclists and other motorists – and reads road signs and detects traffic lights.

Speaking of other motorists, bumper-mounted radar, which is already used in intelligent cruise control, keeps track of vehicles in front of and behind the car.

Externally, the car has a rear-mounted aerial that receives geolocation information from GPS satellites, and an ultrasonic sensor on one of the rear wheels that monitors the car’s movements.

Internally, the car has altimeters, gyroscopes and a tachometer (a rev counter) to give finer measurements on the car’s position. These combine to give the car the highly accurate data needed to operate safely.

How Google’s driverless car works

No single sensor is responsible for making Google's self-driving car work. GPS data, for example, is not accurate enough to keep the car on the road, let alone in the correct lane. Instead, the driverless car uses data from all eight sensors, interpreted by Google's software, to keep you safe and get you from A to B.

The data that Google's software receives is used to accurately identify other road users and their behaviour patterns, plus commonly used highway signals.

For example, the Google car can successfully identify a bike and understand that if the cyclist extends an arm, they intend to make a manoeuvre. The car then knows to slow down and give the bike enough space to operate safely.

How Google's self-driving cars are tested

Google’s self-driving vehicles – of which it has at least ten – are currently being tested on private tracks and, since 2010, public roads.

The car always has two people inside: a qualified driver with an unblemished record sits in the driver’s seat, to take control of the car by either turning the wheel or pressing the brake, while a Google engineer sits in the passenger seat to monitor the behaviour of the software.

Four US states have passed laws allowing driverless cars on the road, and Google has taken full advantage, testing its car on motorways and suburban streets.

Steve Mahan, a California resident who is blind, was involved in a showcase test drive, which saw the car chauffeur him from his house around town, including a visit to a drive-through restaurant.

However, it’s not quite a case of telling your car where you want to go, sitting back and relaxing.

"Any test begins by sending out a driver in a conventionally driven car to map the route and road conditions," Google software engineer Sebastian Thrun explained in a blog post. "By mapping features such as lane markers and traffic signs, the software in the car becomes familiar with the environment and its characteristics in advance."

Are driverless cars safe?

This is one of the questions that continues to pop up in the driverless car debate: is it safe to hand over control of a vehicle to a robot?

Supporters of self-driving car technologies are quick to point to statistics that highlight how unsafe the roads are at the hands of non-autonomous cars – in 2013, 1,730 people were killed as a result of car accidents in the UK alone, and a further 185,540 people were injured, according to the Office for National Statistics.

The worldwide figures are just as scary, with road deaths claiming 1.2 million lives last year. Google claims that more than 90% of these fatalities were due to human error.

In April, Google announced that its driverless cars had covered over 700,000 miles (1.12 million kilometres) without a recorded accident caused by one of its vehicles - one was hit from behind, but the other driver was at fault.

While this is an incredibly small figure compared with how many miles UK motorists cover in a year – in 2010, car insurance company Admiral suggested the number could be near 267 billion miles – the fact that autonomous Google cars are still accident-free remains encouraging.

Technology

The project team has equipped a number of different types of cars with the self-driving equipment, including the Toyota Prius, Audi TT, and Lexus RX450h, Google has also developed their own custom vehicle, which is assembled by Roush Enterprises and uses equipment from Bosch, ZF Lenksysteme, LG, and Continental.

Google's robotic cars have about $150,000 in equipment including a $70,000 LIDAR system. The range finder mounted on the top is a Velodyne 64-beam laser. This laser allows the vehicle to generate a detailed 3D map of its environment. The car then takes these generated maps and combines them with high-resolution maps of the world, producing different types of data models that allow it to drive itself.

As of June 2014, the system works with a very high definition inch-precision map of the area the vehicle is expected to use, including how high the traffic lights are; in addition to on-board systems, some computation is performed on remote computer farms.

Startups v/s MNC : Truth!

Hey there "Technovores!", nothing about technology today, it's something else, but it has become really a very common word to hear now-a-days- "Startup!". It is something, I really felt like sharing today.

Every wondered what it is like to work in a startup, and how different the experience would be compared to working in a regular multinational corporation? Aside from the rules and regulations that MNCs eat, breathe and sleep with, there are plenty of differences that separates the bold from the established.

From the choice of shoes, office chairs, and facial hair choices to how meetings look like, here are some of the stark differences you would expect at startups and MNCs.

Business Intelligence

BI - Business Intelligence:

The term business intelligence (BI) represents the tools and systems that play a key role in the strategic planning process of the corporation. These systems allow a company to gather, store, access and analyze corporate data to aid in decision-making.

Generally these systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few.

Most companies collect a large amount of data from their business operations. To keep track of that information, a business and would need to use a wide range of software programs, such as Excel, Access and different database applications for various departments throughout their organization. Using multiple software programs makes it difficult to retrieve information in a timely manner and to perform analysis of the data.

Business Intelligence Software:

Business intelligence software is designed with the primary goal of extracting important data from an organization's raw data to reveal insights to help a business make faster and more accurate decisions. The software typically integrates data from across the enterprise and provides end-users with self-service reporting and analysis. BI software uses a number of analytics features including statistics, data and text mining and predictive analytics to reveal patterns and turn information into insights.

Big Data and Business Intelligence:

Big Data is used most extensively today with business intelligence and analytics applications and a number of BI vendors have moved to launch new tools that support Hadoop. For example, SAP offers connectors to Hadoop for SAP BI and Business Objects. According to EnterpriseAppsToday, BI vendor support for big data is typically in at least one of two ways:

- Integration connectors that make it easier to move data from Hadoop into their tools.

- Data visualization tools that make it easier to analyze data from Hadoop.

Business analytics:

Abbreviated as BA, business analytics is the combination of skills, technologies, applications and processes used by organizations to gain insight in to their business based on data and statistics to drive business planning. Business analytics is used to evaluate organization-wide operations, and can be implemented in any department from sales to product development to customer service.

Business analytics solutions typically use use data, statistical and quantitative analysis and fact-based data to measure past performance to guide an organization's business planning.