Dexlab, Author at DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

Role of Self Service Analytics in Businesses

Posted on April 5, 2017May 23, 2020 by Dexlab

Self Service Analytics is proving useful for business users, who are working on business data without necessarily having a background in technology and statistics. It is essentially bridging the gap between trained data analysts and normal business users.

Following are the characteristics of Self Service Analytics:

Business Users Independence:

Self Service Analytics reduces dependency on IT and Data warehousing teams, thereby reducing the turnaround time for a request made by a business user.

It does so by continuously collating and loading real time data into a singular stream without disparity, which is easily accessible through browsers. Thus, it helps business users in taking decisions on Real-Time basis.

This feature benefits organizations because vital decisions made within time can be more profitable as compared to the traditional way of analysing data, which may not be a good idea in respect to the urgency constraint.

Easier and Reduced Cost of Operations:

Often, the company’s data are fragmented and widespread across various divisions. This increases the headache of channelling the data meaningfully and in a wholesome manner.

Further to this, preparing reports using this data becomes a cumbersome job for the IT department or the department, which is serving such request. Hence, it may lead to increased cost of time or decreased quality of efficiency at which the operations have to run. However, many a times, these reports fail to give an overview of the operations in an organisation.

Self-service BI integrates data from different systems and delivers a “Single Version of Truth”. Accessing this data and running computations on it requires only a browser for access and eliminates the need to install, maintain and administer large-footprint software clients on each user’s workstation.

If Self Service Analytics is hosted on SaaS, it will further reduce the cost of machinery and maintenance associated with it. The provision for usage can be increased or decreased in no time according to the usage pattern. This really means that Self Service Analytics helps you adapt with time and Pay-Per-Use model, which is a leading trend in most of the industries.

Resolving the conflict over accuracy:

Typically, a business user using Excel would have a local copy of data and run computations on it. He can merge and transform it by using various formulas and finally derive a conclusion.

This is dangerous because in live operations, data keeps changing and data integrity is at stake by working on local copies. Thus, accuracy in decision-making becomes a game of luck.

In Self Service BI, the data from the source is extracted, transformed and loaded into a unique data model, which goes with all operations. In this case, data integrity is assured. In addition, all business users have the same source of data, removing the risk that working with different local copies have.

Therefore, from the above stated facts, we can conclude that Self Service Analytics is a need for today’s businesses.

However, there are a few risks involved in Self Service Business Analytics:

Loose corporate governance and make data available to business users directly may be taken advantage of in an undue manner.
Business users may not be properly trained or skilled to make decisions.
Relying heavily on any tool without some real life experience and insight into the background of that data can result into an impaired decision-making.

If all the above-mentioned risks are mitigated and proper corporate governance structure is in place, Self Service Analytics can be very beneficial for the success of any organization.

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Learn to Surf on the Three Waves of Artificial Intelligence

Posted on April 4, 2017September 12, 2020 by Dexlab

How is the future going to be like? Will human workforce be completely redundant? Will machine learning supersede human intelligence? There have been myriad forecasts about the illuminating future of the AI: that it will be capable of analyzing human emotions, evaluating social nuances, heading medical treatments and surgeries, all that shrouds the best of human resources. But what about now? What is the present scenario like? Fortunately, DARPA is here to unleash a stream of answers to various questions, asked as well as unasked.

DARPA is one of the most intriguing agencies in the US. The stalwarts at DARPA succour outrageous projects – concepts that are completely absurd and far from the accepted paradigms. GPS, legged robots, self-assembling work tools, prediction markets and early internet are some of its incredible creations. And now, they are putting their rear into gear to focus on AI.

DARPA segregates between three distinguishing waves of AI; each is boisterous with its own abilities and challenges. Out of the three, if you ask me which one is more galvanizing, I will point my finger to the third one. However, to decode the third wave of AI, which is the most exhilarating out of the three, you need to understand the first two.

First Wave of AI: Logical Rules and Bespoke Knowledge

Here, tech specialists formulated algorithms and software programs based on the knowledge and information they imbibed, and tried to feed them with logical commands that were decrypted throughout the years. Most of the software that we are using today, like Windows Operating System, traffic system and even our smartphone apps emanated from first wave AI.

Clear and logical rules formed the crux of first wave AI structure. They were successful in introducing simple logical rules for intricate problems, but what they lacked was incapability of learning and ways to deal with the uncertainties.

Second wave of AI: Statistical Learning Systems

In 2004, DARPA initiated its first Grand Challenge – where fifteen autonomous vehicles took part in the competition and they had to complete a 150-mile track in the Mojave Desert. The vehicles were formulated on first wave AI and immediately it revealed the limitations of AI.

Not a single vehicle could finish the entire course; it was an absolute failure.

DARPA learnt its lessons, well. Just one year later, DARPA again organized Grand Challenge 2005 and this time, five groups completed the entire track. But, how? A year ago, they couldn’t.

The groups that could complete the race used the second wave of AI: statistical learning. In this wave, the engineers ignored the exact rules of first wave; instead, they focused on developing statistical models, which they trained eventually on numerous samples to make them highly efficient and accurate.

For better understanding and higher adaptability, statistical learning systems are fetching. If they are properly trained, they can work and adapt themselves to different situations. The brainchild of second wave AI is the notion of artificial neural networks. Besides, the second wave AI is dwarfing humans at speech transcription, face recognition, identifying objects and animals in images, controlling autonomous cars and aerial drones. However, the success of these complex programs leaves the AI pundits clueless. Nobody knows how these systems are working so well. But, we are not complaining!

Third Wave of AI: Redefining First Wave Logical Rules

In the last and final wave, AI system will take the charge of constructing models, themselves. Precisely, they will now redefine the logical rules, which will sculpt the entire decision-making process. The third wave is efficient in relying on several different kinds of statistical models to draw a bigger picture of the world. They are capable of training themselves better.

They are able to derive information from various kinds of sources in order to come at a nuanced and logical conclusion. The systems are so effective that they can actually extract data from our smart homes, cars, cities, even from our wearable devices and deduce our health status. Moreover, they will also be able to program themselves and help in developing abstract thinking.

However, the only challenge that is up on the front is, “there’s a whole lot of work to be done to be able to build these systems”, as told by the director of DARPA’s Information Innovation Office.

The implementation of the third wave is surely going to be a major step for the entire mankind. However, all of this will take some time. Probably it will eventually taste success in the next twenty years. But, when we will appraise the potentials of the AI systems, the concept of the third wave won’t sound improbable.

At the end of the discussion, I am pondering would there be any fourth wave of AI and if yes, then what it would be like? These questions should however be left on time to analyse and then to research. Till then, may you be focused on the third wave.

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How Machine Learning Training Course and AI Made Lives Easier

Posted on April 3, 2017March 5, 2020 by Dexlab

Technological superiority, the rise of the machines and an eventual apocalypse are often highlighted in sci-fi Hollywood movies. The unfavorable impacts of machine learning and excessive dependence on artificial intelligence have always been the hot topic for several Hollywood blockbusters, since years. And people who watch such movies develop a perception that more the technical advancement, higher is the chances that it will ignite a war against humans.

However, in reality, away from the world of Hollywood and motion pictures, Machine Learning and Artificial Intelligence is creating a sensation! If we look past the hype of Hollywood movies, we will understand that the Rise of Machines is certainly not the end of the world or the harbinger of apocalypse but a window of opportunity to achieve technical convenience.

How Things Got Simpler Using Machine Learning Training Course

Though individual are reaping benefits from AI, but it is the business world that is deriving most of its benefits. You will find AI everywhere- from gaming parlors to the humongous amount of data piled in workstation computers. Extensive research is being carried out in this field and scientists and tech gurus are spending huge amount of time in making this improved technology reach the masses. Also, Google and Facebook have placed their high hopes on AI and have also started implementing it in their products and services. Soon, we will see how easily Machine Learning and AI will stream from one product to another.

Who Are The Best Users of Machine Learning?

Machine learning cannot be implemented by every SaaS. Then who can be the active users of machine learning? As stated by a spokesperson of a reputable AI company, the implementation of Machine Learning is suitable for companies that have massive amounts of historical data stored. To train a puppy, you need a handful of treats, similarly to tackle an algorithm you need a vast amount of human corrected error-free data.

Secondly, to get the taste of success the companies, who are thinking of implementing AI, need a proper business case. You need a proper plan before you start operating. Always question yourself, whether your machine learning algorithm will be able to reduce your costs, while offering better value. If yes, then it is a green signal for you!

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Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment

Posted on April 1, 2017November 11, 2019 by Dexlab

Pipes in R

Pipe, written as “%>%“ is basically an efficient operator, supplied by magrittr R package. The pipe operator is notably famous due to its wide range of use in dplyr and by the proficient dplyr users. The usage of pipe operator allows one to write “sin(5)” as “5 %>% sin“, which is inspired by F#‘s pipe-forward operator “|>” and is further characterised by: Continue reading “Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment”

Conducting Intensive Workshops – A Holistic, Exhaustive and Multidimensional Approach to Learning

Posted on March 30, 2017March 30, 2017 by Dexlab

“Knowledge was scattered treasure; education organized it into art, commerce and science.”

― Amit Kalantri – a magician, mentalist and an author

St. Stephen’s College, Delhi presents the magnanimous Academic Conclave 2017 – an initiative to endorse intellectual exuberance of the college and to strengthen interdisciplinary education across myriad fields of study. Often, the term ‘Academics’ is misinterpreted as ‘boring’ but once you attend this stellar event, you will definitely get a sneak peek of a perfect amalgamation of enthusiasm and comprehensive knowledge offered to the up-and-coming scholars of India. The intent is to establish a common accessible platform for incubation of ideas, interaction of thoughts and infestation of intellectuality and what can be better than host interactive workshop sessions! Besides lectures and keynote addresses, workshops are being conducted to encourage an easy interaction between the students and stalwarts of specific domains.

Continue reading “Conducting Intensive Workshops – A Holistic, Exhaustive and Multidimensional Approach to Learning”

Shadowing a Data Architect for a Day!

Posted on March 29, 2017May 21, 2018 by Dexlab

A data architect is a noteworthy role in the present analytics industry. One can naturally evolve from a data analyst or a database designer to a data architect after gathering sufficient experience in the field. The prominence of this role showcases the emergence of the online websites and other internet avenues which require the integration of data from several unrelated data sources.

These data sources can be anything from:

External sources, like market feeds (for e.g. Bloomberg) or other News Agencies (like, Reuters)
Or they could be internal sources like exiting systems that collect data, for instance HR operations that gather employee data

Here is a depiction of a day in the life of a successful data architect:

Data analyst certification from a reputable analytics-training institute can help to speed up your process of evolution from being a data analyst to becoming a successful data architect!

Shadowing a Data Architect for a Day! from Infographics

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Darker Clouds Covering the Cloud

Posted on March 28, 2017April 13, 2018 by Dexlab

New age technologies are dominating the present business environment. Mobility, cloud computing, social media and analytics have been affecting the different realms of business at an ever-increasing rate. Though most of the impacts are favourable, yet it will be reckless to ignore the severity of the negative ones.

Amidst all, cloud computing grabbed the utmost attention. The benefits of cloud computing are myriad – better productivity, lower costs and quicker time to market. A surging number of employees are using cloud applications to talk about various work-related subject matters. Nevertheless, data security is still a leading concern.

Traditional threats are no more potent. Most organisations have devised manipulating ways to safeguard themselves against those predictable threats, newer threats call for better IT security to realise high profile business priorities. A well-researched study by VMware, a pioneer in cloud infrastructure and digital workspace technology revealed that though businesses – small, medium and large will be more than keen to implement cloud computing to secure better future goals and efficiency, information security thriving on the cloud will have a profound impact on enterprises in the next 3-5 years.

The Cloud Security

Another study by eminent research firm Kantar IMRB highlighted that though organisations are taking steps towards a modern workspace environment, they are more interested about having a safe and secured digital environment, thanks to a rising number of cyber threats and thefts. If you follow the figures, in the next 3-5 years, more than 86% of enterprises are going to enhance their IT Budget and 80% of organisations will be eager to expend more time, skill and money on cloud technology.

In respect to the above context, Arun Parameswaran, managing director of VMware India said, “With nearly 25% of all IT workloads being managed on the cloud today, and the number expected to double by 2021, it is evident that the traditional on-premises IT environment is undergoing a profound change.” He further added, “Today, CIOs play an extremely essential role in their organisations’ IT, and it is of utmost importance to have enterprise data available always—anytime and anywhere while tightly secured.”

Enhanced productivity and better profitability will always remain a prime priority, but now as per the recent studies, IT security has also become a chief concern in the list of business priorities. However, despite heavy investments in IT, CIOs of well-established companies are unhappy because the budget is either not structured properly or inadequate. The studies also reveal that the government and BFSI respondents think that the budget for IT security is quite low, and it should be increased at least by 25% by next year.

Cloud is the best thing since sliced bread. Companies are relying more on cloud to store sensitive data. Cloud is the future; so companies should look up to ways to balance the risks with explicit advantages that this evolving technology brings in.

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How To Visualize Multivariate Relationships in Large Datasets in R Programming:

Posted on March 27, 2017November 7, 2019 by Dexlab

In this post, we will discuss how to use the package nmle in R programming, which includes the dataset MathArchieve. To install the package and load it into your R programming environment, use the code mentioned below:

Continue reading “How To Visualize Multivariate Relationships in Large Datasets in R Programming:”

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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Author: Dexlab