Cloud Computing

Cloud computing continues to transform the way organization are doing business, proving to be a transformative innovation for many enterprises. Considering how far the cloud has come in recent years spurs questions of what the future will look like and what types of changes we can expect. Many are speculating about the pace of cloud adoption and what services and capabilities will become available in the future.

Some believe recent reports of online surveillance and data breaches at popular cloud applications resulting from hacking could impede the growth rate of cloud adoption.  But we believe recent events will lead to further innovations that will bolster security and corporate control and this will allow more companies to confidently move important processes online, ensuring the cloud continues its path of fundamentally transforming corporate IT. Broadly, the future for cloud computing will include clearly defined and standards-based security solutions and technology that will enable enterprises to retain full control of their sensitive information assets while continuing to move more business functions online (thereby reducing IT and other costs).  This year’s The Future of Cloud Computing survey by North Bridge, gave some great insights into what might be coming for the cloud and I’ve added a couple of additional ideas below.

Increase of Public Cloud Vs. Private Cloud Applications

At the enterprise level, the use of public cloud applications will continue and increase across IaaS, Saas and PaaS. Private cloud will continue to be the preferred approach where feasible, but at the enterprise app layer (applications like CRM, Human Capital Resource Management and IT Service Management) public cloud SaaS apps will reign.  As more companies enter the cloud application provider space, they will work to gain critical competitive advantages over the rest of the pack and enterprises will benefit from the associated innovations providers produce.

These innovations will allow enterprises to more fully employ public clouds and unlock the true potential they have for their organizations.  So it is clear that we’ll see large companies increase adoption of both private clouds and a series of critical enterprise-grade public cloud options, making the hybrid approach the most popular model.

Improved Security and Reliability of Cloud Computing

While more companies are benefiting from the cloud and while the big cloud application providers have very secure data centers to secure data at rest, some companies have experienced well publicized security and reliability issues – including failed migration of data to cloud applications. In the coming years, cloud application providers will proactively tout the improved security and reliability measures they are putting in place.  In fact, you’ll see them visibly differentiating on security and compliance.  Cloud processes and techniques for securing data in motion will be dramatically improved.  A key part of this will be ensuring that a variety of protections and risk mitigation techniques are available to enterprise customers that will require a multi-faceted approach to controlling their data stewardship and application use. Giving enterprises the ability to control data assets, throughout their entire lifecycle, in motion at at rest, will allow cloud providers and their ISV partners to address legal and legislative blockers to cloud adoption.

Auditing and monitoring will also be improved and more predictive and alerting capabilities will be built directly into the cloud services.  We’ll see a rise of cloud security brokerage capabilities designed to safeguard cloud use and empower IT and Security organizations within the enterprise. Being able to anticipate issues and proactively address them with the appropriate remediation techniques will permit secure, uninterrupted use of the world’s most powerful and pervasive cloud services.

Future Of SAP

SAP : SAP is the world’s leading provider of business software which specialises in industry specific Enterprise Resource Planning (ERP) solutions. 

How ERP vendors see the future, not just of technology but of business, should be a top of mind question for all software users (not just current buyers). The future direction of these products and vendors is really telling as to how they see their firms positively impacting your firm. Will they get it? Will they be fast in re-tooling existing product lines or building new product lines? Have they lost their innovation edge and intellectual courage/curiosity?

The ERP market is bifurcating. There will be those vendors that see BIG, BIG, BIG change coming to businesses and are getting their heads around it as these changes will doubtlessly render, over time, most of the ERP solutions on the market obsolete. The vendors that continue with blinders on will perish (or die an even uglier death trying to play catch up). It’s time, folks, to start that dead pool for ERP vendors.

The big changes that businesses are facing are centered around: extraordinarily rapid, curvilinear innovation and changes impacting regulation, competition, finance, etc. The speed of business is not just increasing; it is growing at a skyrocketing pace while the ability of ERP solutions to change is approaching an asymptotic path. The gap between the speed of business and the speed of ERP is expanding not contracting at many firms.

Mobile technologies are becoming the de facto systems entry point for millions of ERP users. Desktops are in decline and more and more workers are bringing their own communication devices to work. The modern worker is mobile, often works from home, may be a contractor (not an employee) and may never have a cubicle with a desktop computer. They don’t want their parent’s work environment or work systems. They work on their terms with their technology. If you’re an ERP vendor and you don’t design first for the portable workforce and the devices they use (e.g., cell phones and tablets) (and subsequently for desktop devices), then you’re behind the curve. More interestingly, ERP vendors are competing with small software companies (think 1-2 people) that are developing apps directly for these cell and tablet users. These developers don’t force their users to purchase a million dollar database and spend millions more with an integrator to connect their apps to an old ERP solution. The big question for ERP firms is “Can you develop mobile apps at the same pace and price points of the people creating apps for iPhones, Androids, etc.?

SAP did a good job today of identifying their trinity. They laid out the change phenomena (via keynotes) and their co-CEOs spoke to how in-core (HANA), mobile and social innovations will be part of their vision for 2015. It’s clear that co-CEO Jim Snabe not only gets the changes impacting businesses, he knows how several of their technologies will address many of these changes.

Telling Tales: Niche of a Data Scientist!

“We are, as a species, addicted to story”
-John Gottschall
Author of The Storytelling Animal: How Stories Make Us Human

A Data Scientist Knows the Fact that Stories Sell!

Since time immemorial, storytelling has been an integral constituent of our cultures, and henceforth, of our being. We retain stories more than we understand and remember facts and figures. An ambitious hero progressing expeditiously towards his goal will always leave a lasting impact on the listeners, as compared to a dull and drab story about a layman wandering aimlessly without any significant goal in hand. A linear story with a protagonist, a quest motif, a resolution, preferably a positive outcome, is always cherished, remembered, and followed.
An American proverb says, “Tell me the facts and I’ll learn. Tell me the truth and I’ll believe. But tell me a story and it will live in heart forever.”

Facts are Dull, Stories are Interesting!

Does it mean that Data Scientists are aware of our natural inclination and inborn affinity with stories? Probably yes! Analytics data, usually tagged as dull and boring, fails to seep into our minds to create a long-lasting impact. For a Data Scientist, the art of weaving facts and figures into a soulful narrative is a must-have skill. The facts transformed into a narrative will take all the controls – it will communicate data analytics to non-analytical people, narrative along with visual analytics will make analytical data look impressive, it will persuade people into meaningful actions, it will generate goal-oriented activities, and last but not the least, it will motivate people in achieving their final goal.

Wisdom Loaded Stories Work Wonders!

Even the folk tales of all the cultures across the globe have some morals to teach. Essentially didactic in nature, stories teach us, motivate us, and even guide us to find the right directions or ways of functioning. This kind of wisdom is expected out of a Data Scientist, who should know how to impart knowledge, accumulated through diverse experiences. Presumably, a Data Scientist possesses great analytical abilities, but he should couple his abilities with level-headed maturity and considerable insights, in order to tell a great story that communicates, persuades, and works wonders.

Will Negative Stories leave a Negative Impact?

It is true that stories can be both negative and positive. For a Data Scientist, the ultimate goal of storytelling remains the communication of analytical data. To this end, positive stories are powerful, and negative stories can be even more powerful! Where positive stories tell about what went right, negative narratives can tell people about “what to avoid” or “what went horribly wrong” such as which course of action proved disastrous for an organization, which elements altered the smooth functioning of processes, how ambiguous policies led to failures, and so on. Such a story can then become an elaborate piece of information which not only tells one about the ultimate goals, the desired end, the process to be followed, but also the other significant details about the anticipated loopholes and impending dangers. Surely, a smart way to communicate!

Myriad ways of Telling Tales

Someone has rightly said, “Storytelling is the mother of all ‘pull’ marketing strategies. It encourages dialogue, engagement and interaction among equals – an exchange of meaning between people.”  As a matter of fact, the first and the foremost story is “the story before the story” – a story that springs from an idea. There will be no investments in data science projects, if there is no convincing story woven strategically and aesthetically around an idea or a concept. Every data science project begins with no data in hand. There are only ideas, and a story about the idea. The idea and the allied story lead to the actual implementation and data collections, followed by extensive data analytics, and finally paving way for another set ofdata-driven stories.

These data-driven stories may have at their hearts data from past and present. Analytical stories that center around events, patterns, and other aspects from the past, are usually termed as Reporting stories. While on the other hand,stories may also originate from the surveys done primarily to have an insight into the latest trends in varied sectors such as finance, healthcare, anthropology, Human Resources, Business, and so on. These stories are descriptive in their nature, and throw light on the present scenario. However, bothanalytical stories from the past, and descriptive stories from the present pave way for Predictive Analysis, in which a Data Scientist, based on some assumptions and probability, predicts the future activities or patterns.

Not limited to this, Data-driven stories have multiple manifestations. “What-Stories” and “Why-Stories” are equally important because these stories entail detailed analysis of the concerned event and of the underlying causes. For instance, an objective reporting about a sudden rise in the online shopping, can be termed as a “What-Story” and the detailed analysis of why this happened, would provide crux to the “Why-Story.” Causation, in this way, is central to data analysis. 

Data Scientists analyse volumes of data to find out the cause and effect relationship among multiple variables. They also seek if there is any correlation in the variables- if rise in one variable led to the rise in the other, or vice-versa.

Data-driven tales are central to Data Analytics, in the same way, as these are to the profile of a Data Scientist. A Data Scientist has to have the storytelling ability – the ability which will make his words interesting to listen to, and meaningful enough to think over. The profile of a Data Scientist is considered to be the most “sought-after” profile, who knows storytelling abilities may have added to the charm. Not only storytelling, but also other attributes such as knowledge and wisdom play key role in the career of a Data Scientist. Such a skill set comes after getting trained in the niche skill.

ETLhive organises comprehensive lectures on Data Science, during which the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on varied concepts and skills associated with Data Science. At ETLhive, you will go extensive training with hands on experiences in Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. Get trained at ETLhive and get hired for the hottest job of the century – a Data Scientist!

R is Our Mighty Programming Language

R – The name comes from the initials of its developers Ross Ihaka and Robert Gentleman, who created R programming language for statistical analysis, graphics representation, and reporting. With the passage of time, R has diversified and entered innumerable sectors, with many people declaring it “hot” and many adjudging it as “getting even hotter”.If you are gauging the success of R programming, you need to have a look at the list of companies that use it for handling variety of issues which they face on daily basis. Revolution Analytics creates a list of companies that use R programming as a fundamental tool for data management and data analytics. However, to understand the expanding horizon for R programming, and to know how mighty R Programming has become, have a look at the following data that discussesabout various sectors wherein R Programming is valued incessantly.

Banking Sector

According to data collected by Revolution Analytics, Banks and Financial sector depend heavily on R Programming for various functions such as that of Credit Risk Analysis and Reporting. The names that can be associated with R Programming are Bank of America, and ANZ, one of the leading banks in Australia

Non-Profit Organisations:

Non-profit organisations such as Benetech and Human Rights Data Analysis Group (HRDAG) use R programming for answering geopolitical questions and for analysing human rights data respectively (Revolution Analytics).

Real Estate:

Real-estate agencies depend on R programming and Data Science for predictive analysis. They perform data analysis on the collected data in order to predict sales and purchase, and to formulate and finalise prices of the property

Media and Newspapers:

Media and Newspapers rely on R Programming for the tasks it can perform. Many newspapers such as The New York Times depend on R Programming for Data Visualisation. Similarly, newspapers and media import data for weather forecasting from weather forecasting agencies, which in turn are heavily dependent on R programming for predicting weather forecasts wherein R programming is as efficient as generating graphics for flood/drought/or other famine possibilities

Social Networking Sites:

Social Networking sites such as Twitter and Facebook make use of R programming for multiple functions. Data Scientists working in the Twitter Analytics Domain try to extract meaningful data out of millions of tweets and after analysing the emotional and sentimental quotient hidden within tweets, they try to find out some common observations for the benefit of the concerned entities or organisations.

Aerospace and Flight Aviation Industry:

Aviation industry is one such industry where R Programming is considered as one of the essential “must-haves” since it helps in predicting the flight status, delays, scheduled time, and actual inflight time.

Stock Market Exchange:

R programming is equally reliable in Stock Market Exchange. It has emerged as a brilliant programming language that ensures smart Business Intelligence in terms of prediction, analysis, and the formulation of policies in the process.

While going through the above mentioned sectors and their dependency on R programming, one finds an appropriate answer to the question that asks “What is the future of R Programming?” The answer to this question is “Future is here and now”. Learn R Programming, build a strong foundation for a remarkable career in Data Science, become an efficient Data Scientist – the mightiest, with the hottest job in your pocket!

You may definitely have a remarkable career in Data Science if you are able to get hands-on training in it. ETLhive organises comprehensive lectures on Data Science, during which the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on varied concepts and skills associated with Data Science. At ETLhive, you will go extensive training with hands on experiences in Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. Get trained at ETLhive and get hired for the hottest job of the century – a Data Scientist!

Data Scientist – Is it the Hottest Job in Demand?

Data Scientist – Roles and Responsibilities..

Tweeting is trending! It has emerged as one of the latest manifestations of the human need to socialise, to acknowledge and be acknowledged. But have you ever wondered while tweeting about what importance does your tweet hold? It may be negative or positive carrying multiple meanings, influencing the sales of a company, or its reputation, but for sure, the data collected in the form of your tweets is largely unstructured or semi-structured in its nature. It is in here that a Data Scientist comes in!

A Data Scientist, working in the Twitter Analytics Domain, extracts meaningful data out of thousands of tweets, performs sentiment analysis on the streamlined data, and predicts the patterns of behaviour, interactions, and associations between people which may directly or indirectly influence an organisation and its prosperity. As a matter of fact, the role of a Data Scientist is to analyse, manage, and streamline complicated data sets, and to devise certain tools that enhance information flow to the respective organisations for their business benefits. The task is largely predictive and analytical in its nature.

Multiple Roles in the Data Science Domain.. 

Data Scientist – The Hottest and the Highest paid job across the Globe with an Average Salary of $110,000.

A Data Scientist, with his expertise in Mathematics, Statistics, and Programming, performs to ease out the intricacies behind any data, in order to come up with simplified business strategies which the BIG business houses may not be aware of. A Data Scientist applies the knowledge of statistics, algorithms, and mathematics to find out various predictive models to solve a business problem. In doing so, a Data Scientist analyses and predicts the possibilities that may occur in the near future. World-wide business houses, such as Amazon, hire Data Scientists whose roles are to create predictive models based on the ratings and likes of products, and to make recommendations accordingly.

Data Engineer – Trending with an average salary of $90,000

Data Engineers are usually software professionals having in-depth understanding of programming languages, warehousing solutions such as SQL and NoSQL, and frameworks such as Spark and Scala, and Hadoop. Their area of expertise is coding and programming. They co-ordinate with the data scientists in order to process, manage, and clean up the data sets. They process the predictive models designed by the data scientists and implement those in code. With a steep rise in e-commerce industry, there occurs a high-demand of Data Engineers.

Data Analysts – Future Calling… An average salary of $65,000

With a base skill set comprising of Business Knowledge and Statistics, a Data Analyst makes data accessible in the form of charts and reports. Business Analysts are Data Analysts who try to understand the given data and try to figure out the best business policies for their companies. Being stationed at the entry-level in the domain of Data Science, Data Analysts have promising careers ahead with handsome salaries in their pockets.

Data Science and its Future?

“By 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent” – A Study by McKinsey

As per global research, Data Scientists are in great demand, owing to the widened business domain of the e-commerce industry. Their requirement is equally high in other industries such as aviation and aerospace industry, and stock market exchange where they work to analyse and predict flights, and to study the ebb and flow of the stock markets. With its increased demand and highest paid jobs, Data Science is emerging as one of the most exciting domains for career. It is indeed the “hottest career” to dream of and to follow!

One can certainly pursue one’s dream job, for which one requires to undergo hands-on training in Data Science. ETLhive organises extensive training lectures on various concepts of Data Science such as Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. During the course the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on all such concepts and skills associated with Data Science. Get trained at ETLhive and get hired for the hottest job of the century, and become a glamorous and knowledgeable Data Scientist!

 SEPTEMBER 29, 2016

What is Machine Learning

Machine learning is a function of artificial intelligence (AI) Which provides computers the intelligence to automatically learn and develop skills from experience without being explicitly programmed. Machine learning aims at the improvement of computer programs which can access data and use it learn for themselves.

Future of Machine Learning

Most applications will include machine learning.

In only 3-5 years, machine learning will play an important role and become part of almost every software application. Engineers will even insert this efficiency directly into our systems. Think of how good your TV streaming service knows that what to suggest to the user. Expect this level of personalization to become ubiquitous and improve the customer experience everywhere.

Machine learning as a service will become more common.

As machine learning will become more and more valuable and the technology will bloom, almost every enterprise will start using the cloud to deliver machine learning as a service (MLaaS).

This will usher a bigger range of organizations to take advantages of machine learning without making large hardware capital or training their own algorithms.

Systems will get really good at talking like humans using Machine learning.

Before the machine learning, systems were facing a very hard time to understand even simple human language. Machine learning helps computers understand the context and meaning of sentences much better through natural language processing (NLP). As the technology improves, solutions such as IBM Watson Assistant will learn to communicate seamlessly without using code.

Algorithms will constantly retrain.

Currently, most machine learning systems train only once. On that initial training, the systems will then locate any new data or problems in the system. Over time, the training information often becomes dated or imperfect. In a few years, many machine learning systems will be connected to the internet and constantly retrain on the most relevant information on the internet.

Specialized hardware will deliver performance breakthroughs.

Traditional CPUs only had finite success running machine learning systems. GPUs, however, have an advantage in running these algorithms because they have a large number of simple cores. Artificial intelligence (AI) experts are also using field-programmable gate arrays (FPGAs) for machine learning. At times, FPGAs can even outperform GPUs.

As this technology advances, more businesses will embrace the AI revolution.

Conclusion:

Machine Learning is an emerging technology which has widespread benefits. To learn more about this technology and about how to leverage it for your job or business, contact us at etlhive.com

Machine Learning

What is Machine Learning

Machine learning is a function of artificial intelligence (AI) Which provides computers the intelligence to automatically learn and develop skills from experience without being explicitly programmed. Machine learning aims at the improvement of computer programs which can access data and use it learn for themselves.

Future of Machine Learning

Most applications will include machine learning.

In only 3-5 years, machine learning will play an important role and become part of almost every software application. 

Engineers will even insert this efficiency directly into our systems. 

Think of how good your TV streaming service knows that what to suggest to the user. 

Expect this level of personalization to become ubiquitous and improve the customer experience everywhere.

Machine learning as a service will become more common.
 

As machine learning will become more and more valuable and the technology will bloom, almost every enterprise will start using the cloud to deliver machine learning as a service (MLaaS). 

This will usher a bigger range of organizations to take advantages of machine learning without making large hardware capital or training their own algorithms.

Systems will get really good at talking like humans using Machine learning.

Before the machine learning, systems were facing a very hard time to understand even simple human language. 

Machine learning helps computers understand the context and meaning of sentences much better through natural language processing (NLP). 

As the technology improves, solutions such as IBM Watson Assistant will learn to communicate seamlessly without using code.

Algorithms will constantly retrain.

Currently, most machine learning systems train only once.

 On that initial training, the systems will then locate any new data or problems in the system. 

Over time, the training information often becomes dated or imperfect. 

In a few years, many machine learning systems will be connected to the internet and constantly retrain on the most relevant information on the internet.

Specialized hardware will deliver performance breakthroughs.

Traditional CPUs only had finite success running machine learning systems. 

GPUs, however, have an advantage in running these algorithms because they have a large number of simple cores. 

Artificial intelligence (AI) experts are also using field-programmable gate arrays (FPGAs) for machine learning. 

At times, FPGAs can even outperform GPUs.

As this technology advances, more businesses will embrace the AI revolution. 

Conclusion: Machine Learning is an emerging technology which has widespread benefits. To learn more about this technology and about how to leverage it for your job or business, contact us at etlhive.com