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Move Your Career towards Big Data Analytics: The Future Looks Bright

Posted on June 30, 2017October 5, 2018 by Dexlab

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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The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data

Posted on June 23, 2017June 27, 2017 by Dexlab

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.

Continue reading “The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data”

Data Science – then and now!

Posted on May 18, 2017May 23, 2020 by Dexlab

Data Science = Statistics + Computer Science
emerges as a designation for stores of big data

The following timeline traces the evolution of the term “Data Science”, along with its use, attempts to define it, and related terms:

“The future of Data Analyses “- by John W.Turkey, 1962

More emphasis was placed on using data to suggest hypotheses to test
Exploratory Data Analysis and Confirmatory Data Analysis works in parallel

“Book on Survey – Contemporary data processing methods “– by Peter Naur, 1974

- Data is a representation of the facts or ideas in a formalized manner
- It is capable of being communicated or manipulated by some process
- The rise of “Datalogy”, the science of data and data processes and its place in education
- Data Science here defined as – the science of dealing with data, once established and the relation of data being delegated to the other fields and sciences.

“The International Association for Statistical Computing (IASC)”- Section of ISI, 1977

The mission is to link traditional statistical methodology, modern computer technology and the knowledge of domain experts in order to convert data into information and knowledge

Gregory Piatetsky-Shapiro, 1989

Arrival of Knowledge Discovery in Databases (KDD) workshop
It became the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) in 1995

“Database Marketing” – cover story by BusinessWeek, 1994

Companies collect mountains of information about you
Then crunch it to predict how likely you are to buy a product
Implement the knowledge to craft a marketing message precisely calibrated to get you to do so
Many companies were too overwhelmed by the sheer quantity of data to do anything useful with the information
However, many companies believe they have no choice but to brave the database-marketing frontier

“Members of the International Federation of Classification Societies (IFCS)”, 1996

Data science is included in the title of the conference (“Data science, classification, and related methods”)

“From Data Mining to Knowledge Discovery in Databases” by – Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth,1996

Historically, the notion of finding useful patterns in data has been given a variety of names,
Some of the names are data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing
KDD [Knowledge Discovery in Databases] refers to the overall process of discovering useful knowledge from data, and
Data mining refers to a particular step in this process
Data mining is the application of specific algorithms for extracting patterns from data
Data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data

H. C. Carver Chair in Statistics at the University of Michigan -Professor C. F. Jeff Wu, 1997

Asked statistics to be renamed as data science, and statisticians to be renamed data scientists

The journal Data Mining and Knowledge Discovery, 1997

“Data mining” designates as – “extracting information from large databases.”

“Mining Data for Nuggets of Knowledge” – Jacob Zahavi quoted – 1997

Conventional statistical methods work well with small data sets
Today’s databases, however, involves millions of rows and scores of columns of data
Scalability is a huge issue in data mining
Another technical challenge is developing models that can do a better job analysing data, detecting non-linear relationships and interaction between elements
Special data mining tools may have to be developed to address web-site decisions

Also read: The Beginners’ Guide to Data Science Jargon

“Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” – by William S. Cleveland, 2001

Plan to enlarge the major areas of technical work of the field of statistics
The benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited
A merger of knowledge bases would produce a powerful force for innovation
The statisticians should look to computing for knowledge today just as data science looked to mathematics in the past
The departments of data science should contain faculty members who devote their careers to advances in computing with data and who form partnership with computer scientists

“Statistical Modeling: The Two Cultures” (PDF) – by Leo Breiman, 2001

Two cultures in the use of statistical modeling to reach conclusions from data
One assumes that the data are generated by a given stochastic data model, while the other uses algorithmic models and treats the data mechanism as unknown
Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics
It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets.
If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools

Launch of Journal of Data Science, 2003

Data Science means almost everything that has something to do with data: Collecting, analyzing, modeling
The most important part is its applications–all sorts of applications

“Competing on Analytics,” a Babson College Working Knowledge Research Center report “- by Thomas H. Davenport, Don Cohen, and Al Jacobson, 2005

The emergence of a new form of competition based on the extensive use of analytics, data, and fact-based decision making
Beside competing on traditional factors, companies starts to employ statistical and quantitative analysis and predictive modeling as primary elements of competition

The National Science Board publishes “Long-lived Digital Data Collections – 2005

Data scientists are – “the information and computer scientists, database and software engineers and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.”
In simple terms, they are the people who work where the research is carried out–or, in the case of data centre personnel, in close collaboration with the creators of the data–and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in data base technology

Also read: Secrets To Clinch Victory in Global Data Science Competitions

Harnessing the Power of Digital Data for Science and Society, 2009

The nation needs to identify and promote the emergence of new disciplines and specialist’s expert in addressing the complex and dynamic challenges of digital preservation, sustained access, reuse and repurposing of data
Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science
These individuals are key to the current and future success of the scientific enterprise
However, these individuals often receive little recognition for their contributions and have limited career paths.

“Google’s Chief Economist, tells the McKinsey Quarterly”- Hal Varian, 2009

Quote – “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”
The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—are going to be the most important skills in the coming decades
Managers need to be able to access and understand the data themselves.

“The Revolution in Astronomy Education: Data Science for the Masses “- Kirk D. Borne, 2009

Understanding the data is crucial for the success of sciences, communities, projects, agencies, businesses, and economies
It is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce)
specialists must learn and apply new data science research techniques
Non-specialists require information literacy skills

“Rise of the Data Scientist”- Nathan Yau, 2009

As quoted, “the next sexy job in the next 10 years would be statisticians.”
By statisticians, he actually meant a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts
Ben Fry argues for an entirely new field, which will combine the skills and talents from disjointed areas of expertise… [Computer science; mathematics, statistics, and data mining; graphic design and human-computer interaction].

Also read: How is data science helping NFL players win Super bowl?!

Troy Sadkowsky, 2009

Created the data scientists group on LinkedIn, complementing his website, datasceintists.com (which later became datascientists.net)

”Data, Data Everywhere“- The Economist Special Report – Kenneth Cukier, 2009

A new kind of professionals has emerged – the data scientists, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data

“What is Data Science?”- Mike Loukides, 2010

Data scientists combine entrepreneurship with patience, along with the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution
They are inherently interdisciplinary
They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions
They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here’s a lot of data, what can you make from it?’

Also read: What Sets Apart Data Science from Big Data and Data Analytics

“A Taxonomy of Data Science” – Hilary Mason and Chris Wiggins – 2010

Data scientist, in roughly chronological order: Obtain, Scrub, Explore, Model, and Interpret
Data science is clearly a blend of the hackers’ arts
Statistics and Machine learning and the expertise in mathematics and the domain of the data for the analysis to be interpretable
Requires creative decisions and open-mindedness in a scientific context

“The Data Science Venn Diagram”- Drew Conway, 2010

Simply enumerating texts and tutorials does not untangle the knots
Data Science Venn Diagram – hacking skills, math and stats knowledge, and substantive expertise

“Why the term ‘data science’ is flawed but useful “- Pete Warden, 2011

The people tend to work beyond the narrow specialties that dominate the corporate and institutional world, handling everything from finding the data, processing it at scale, visualizing it and writing it up as a story
They also seem to start by looking at what the data can tell them, and then pick interesting threads to follow rather than the traditional scientist’s approach of choosing the problem first and then finding data to shed light on it

“Data Science’: What’s in a name?”- David Smith, 2011

Many companies are now hiring ‘data scientists’, and the entire branch of study is run under the name of ‘data science’
Yet some have resisted the change from the more traditional terms like ‘statistician’ or ‘quant’ or ‘data analyst’
However, unabashedly ‘Data Science’ better describes what we actually do, which is a combination of computer hacking, data analysis, and problem solving

“The Art of Data Science” – Matthew J. Graham, 2011

To flourish in the new data-intensive environment of 21st century, we need to evolve new skills
We need to understand what rules [data] obey, how it is symbolized and communicated, and what its relationship to physical space and time is.

“Data Science, Moore’s Law, and Moneyball” – Harlan Harris, 2011

Data Scientist runs the gamut from data collection and munging, through an application of statistics, machine learning and related techniques for interpretation, communication, and visualization of the results
Data Science is defined by its practitioners, as a career path rather than a category of activities
People who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense.

“Building Data Science Teams”- D.J. Patil, 2011

Jeff Hammerbacher shared the experiences of building the data and analytics groups at Facebook and LinkedIn
He realized that as their organizations grew, they need to figure out what to call the people on their teams
‘Business analyst’ seemed too limiting
‘Data analyst’ was a contender, but they felt that title might limit what people could do. After all, many of the people on their teams had deep engineering expertise
‘Research scientist’ was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM
However, they felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams
Instead, the focus of the teams was to work on data applications that would have an immediate and massive impact on the business
The term that seemed to fit best was data scientist: those who use both data and science to create something new

“Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review – Tom Davenport and D.J. Patil, 2012

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After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

Posted on May 15, 2017October 10, 2018 by Dexlab

After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

Two decades ago, if someone asked me to write a computer program that played tic-tac-toe, I would have failed horribly. Now being an accomplished computer programmer, I know the desirable tricks to solve tic-tac-toe with the help of “Minimax Algorithm”, and what it takes is just about an hour to jot down the program. No doubt, my coding skills have evolved over the period of time, but also computer science technology has reached unattainable heights.

Computers paved the ways for a startled innovation. When in 1997, IBM introduced a chess-playing computer, known as Deep Blue, which eventually beat world-renowned Grandmaster Garry Kasparov in a six-game match, people remained in awe for years. Following the trend, in 2016, Google’s London-based AI Company, DeepMind launched AlphaGo – and it mastered over the ancient board game Go. Computers have outplayed the best human players in the games of chess, draughts and backgammon, now it’s time for Go.

Also read: Infographic: How Big Data Analytics Can Help To Boost Company Sales?

The technology goes on thriving, beating humans at games. In late May, AlphaGo is all set to take on its human rival Ke Jie, the best player in the world during the Future of Go Summit in Wuzhen, China. Games, which solely relied on human intelligence, wit, intuition, discern is now excelled by the AI, which is powered by improved engineering and computer superiority.

Also read: Top Databases of 2017 to Watch Out For

Don’t you think this is great! Where AI is driving our cars, looking for ways to cure deadly cancer and helping us in everyday work, winning at Go takes AI a step ahead. It not only makes the games more fun and exciting, but endlessly enjoyable.

The strategy explained

In the eastern part of the world, notably in China, Japan and South Korea, Go is extremely popular and many celebrities indulge in it. The game developers showed interest for long in the complexity of this game. However, the rules are simple – the main objective is to secure the maximum territories by placing and capturing black and white stones on a 19×19 grid.

Also read: Shadowing a Data Architect for a Day!

Chess is less complicated than Go; in the latter, the chances of recognising wins and losses is relatively tougher, as stones possess equal values, and ensures understated impacts throughout the board. To play Go, AlphaGo program implemented deep learning in neural networks – a brain-stimulated program. The connections formed here runs in-between layers of simulated neurons, further strengthened by examples and experiences. Firstly, it analysed 30 million positions from expert games, while gaining abstract information about the state of play from the board data, just like other programmes that classify images from pixels. After all this, finally it played against itself over 50 computers to improve its performance, with each iteration and this came to be known as reinforcement learning.

The round of applause

“AlphaGo plays in a human way”, says Fan – DeepMind’s program AlphaGo beat Fan Hui, the European Go champion. He further added, “If no one told me, maybe I would think the player was a little strange, but a very strong player, a real person.” “The program seems to have developed a conservative (rather than aggressive) style”, adds Toby Manning, a veteran Go player and a referee.

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Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

Posted on May 8, 2017June 18, 2020 by Dexlab

You have employees? And they bring smartphones to work? Is everything right? Or wrong?

Period.

The moment an employee carries a personal mobile device, be it a smartphone or a tablet, to work, a merger of personal and professional is bound to happen. And this could definitely give a rough time to the employer. If not handled properly.

Also read: What Sets Apart Data Science from Big Data and Data Analytics

Of late, there has been a lot of furore, thanks to our effervescent, ever-efficient media about messaging apps. But the headlines took e negative bend when a London- based banker was fired and fined by FCA for exposing crucial confidential data through WhatsApp. Though he defended himself by stating that he simply wanted to MAKE AN IMPRESSION on his friend, he was booked under cybercrime sections.

Over the past few decades, the communication forms have undergone a magnanimous evolution. Once a mail-driven society is now a bustling centre of myriad high-on-function communication apps, the apps includes personal, social and enterprise-oriented apps. However, with new technologies materializes new challenges. The best way to manage such personal apps is by ensuring safe and secure mode of communication, instead of banning them completely. Embrace the BYOD culture but with due protective measures.

Also read: How To Stop Big Data Projects From Failing?

Let’s talk about Data Mining

Mobile Device Management (MDM) is the key

MDM is the best way to ensure productivity from the employees, while administering their mobile devices. It allows the employees to access data and meaningful information without posing any threat to company data. By implementing MDM, companies can keep a tab on corporate data segregation, corporate policies, secure emails and confidential documents, and integrate and manage mobile devices. Sometimes, a company can go a step higher by restricting users from using WhatsApp on their company provided device, and in its place give them some secure and safe team messaging solution.

Launch a secure team messaging app

For safekeeping of confidential company data, make sure you provide your employees an efficient messaging app. Choose an app that ensures better control over the information that is to be accessed or shared by the users.

The app should be used by the team admin to keep an eye on the team’s activities and the content that they are sharing. They are the ones responsible to control who can or cannot join the team, along with blocking external domains.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

It is advisable to select a tool that provides its users advanced controls, from basic channel level. Flock is developed on these mechanisms and empowers the channel admin to delete any content, and add/remove members from the team. These ways are good to go in restricting the leakage of confidential data through company professionals.

Awareness and compliance helps

Make your employees, your strength and not weakness. They are the best defence against any attempt of breaching crucial data. So, ensure compliance by conducting frequent safety awareness audits and workshops. Also, make sure that not every employee has access to sensitive company data, as it enhances the risks of becoming a victim of cybercrime.

Still wondering, what have you done to secure your company’s confidential data?

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Drawing a Bigger Picture: FAQs about Data Analytics

Posted on April 24, 2017December 7, 2018 by Dexlab

When the whole world is going crazy about business analytics, you might be sitting in a corner and wondering what does it all mean? With so many explanations, notions run a gamut of options.

It’s TIME to be acquainted with all the imperceptible jargons of data science; let’s get things moving with these elementary FAQs.

What is data analytics?

Data analytics is all about understanding the data and implementing the derived knowledge to direct actions. It is a technical way to transform raw data into meaningful information, which makes integral decision-making easier and effective. To perform data analytics, a handful number of statistical tools and software is used and et voila, you are right on your way to success!

How will analytics help businesses grow?

The rippling effects of data analytics are evident, from the moment you introduce it in your business network. And stop rattling! The effects are largely on the positive side, letting your business unravel opportunities, which it ignored before owing to lack of accurate analytical lens. By parsing latest trends, conventions and relationships within data, analytics help predict the future tendencies of the market.

Moreover, it throws light on these following questions:

What is going on and what will happen next?
Why is it happening?
What strategy would be the best to implement?

Also read: Tigers will be safe in the hands of Big Data Analytics

How do analytics projects look like?

A conventional analytics strategy is segregated into the following 4 steps:

Research – Analysts need to identify and get through the heart of the matter to help business address issues that it is facing now or will encounter in the future.

Plan – What type of data is used? What are the sources from where the data is to be secured? How the data is prepared for implementation? What are the methods used to analyse data? Professional analysts will assess the above-mentioned questions and find relevant solutions.

Execute – This is an important step, where analysts explores and analyses data from different perspectives.

Evaluate – In this stage, analysts evaluate the strategies and execute them.

How predictive modelling is implemented through business domains?

In business analytics, there are chiefly two models, descriptive and predictive. Descriptive models explain what has already happened and what is happening now, while Predictive models decipher what would happen along with stating the underlying reason.

Also read: Data Analytics for the Big Screen

One can now solve issues related to marketing, finance, human resource, operations and any other business operations without a hitch with predictive analytics modelling. By integrating past with present data, this strategy aims to anticipate the future before it arrives.

When should I deploy analytics in business?

An Intrinsic Revelation – Analytics is not a one-time event; it is a continuous process once undertaken. No one can say when will be the right time to introduce data analytics in your business. However, most of the businesses resort to analytics in their not-up-par days, when they face problems and lags behind in devising any possible solution.

So, now that you understand the data analytics sphere and the significance attached, take up business analytics training in Delhi. From a career perspective, the field of data science is burgeoning. DexLab Analytics is a premier data science training institute, headquartered in Gurgaon. Check out our services and get one for yourself!

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Harnessing Big Data for Water Management

Posted on March 24, 2017February 25, 2019 by Dexlab

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22^nd March, it is apt to assess the development being made in conserving this diminishing resource.

Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities.

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres

Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability

New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

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Concocting Data with GIS

Posted on March 22, 2017May 18, 2018 by Dexlab

In supreme and sophisticated geospatial realm, data have been predominant. Or, should I say it is the matured fosterling of Geographic Information Systems (GIS). Choose, whatever suits you; subject to whom you work for or what you need to work on. The meat and potatoes? To excel on location analytics, concentrate only on the best most current data.

In today’s world, data is valuable. It is vital and veritable. It is indispensable in Geographic Information Systems (GIS).

To second that, today’s tech-efficient society is anchored on location-based data, than ever, especially with the rise in Twitter, Google, Facebook and other social media apps, which collects and stores data from their highly-valued users to sell them off to money-grubbing advertisers. Though secretly. On the other hand, cell phones go a step ahead in broadcasting your current location data 24/7. Otherwise, how would your friends know that you are safe when a severe earthquake rattled your neighbouring city! (Thanks to location settings)

Feisty Predicaments

However, the real challenge lies in data identification and consumption. Countless number of users gets baffled when it comes to finding data, and if found, how to consume it to set off their business determinations. To solve this, many imminent think tanks of tech industry came out with direct and decisive solutions. Some of them were loaded with an abundance of data, i.e. digestible and disintegrated. By disintegration, they meant that the data was categorized into: points of interest, roads, boundaries and demographics, for easy comprehensibility. Furthermore, industry data bundles concerning telecommunications, retail and insurance fields were added to make the coverage global and profitable. To top it off, quality content and sprawling file formats boosted the results and mechanisms, both.

Conflux of GIS and BI

Location technology – Does this ring a bell? Yes? Then you would be familiar with GIS but others, particularly new Business Intelligence users and consumers must have just started taking baby steps on basic mapping. For BI, maps are the backdrop against which business analysts project their business data, stats and analytical information. Analysing the data to understand the insights of consumers is crucial, directly affecting the business decisions and revenues thereby. For example, heat maps, used to see the concentration of installations, customers and IoT devices provides an unparalleled accurateness of spatial relationships, which is impossible to obtain from the spreadsheets.

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One of the integral location analytics issues is to help in identifying the high-risk zones at the time of natural disasters, like tornadoes, earthquakes, floods, hurricanes or mudslides. For example, in the US, the East Coast is vulnerable to a lot of hurricanes and floods, whereas earthquakes and mudslides snap the West Coast time to time. Assessment of these location problems is intrinsically important for mortgage underwriters, insurance agents and public safety departments. And best data along with effective geo-coding is the solution to all the inconveniences.

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Data Analytics for the Big Screen

Posted on March 20, 2017May 18, 2018 by Dexlab

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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