Viacom the owner of household brands such as Comedy Central, Nickelodeon and MTV, is one of the largest media companies in the world, delivering more than 170 cable, broadcast and online networks in around 160 countries.
Monitoring of the digital networks, which are used to pump their content into millions of homes, gives them access to a huge amount of data, on how both their systems and their audiences behave.
To plug tax loopholes, the income tax (IT) department will use Big Data analytics to track tax evaders by collecting financial information about them, such as – common address, mobile number and e-mail to establish relationships between their multiple PANs. The department with support from various private firms will analyse the voluminous big data available post-demonetisation for checking transactional relationships between PAN holders.
Continue reading “Tax department leans on Big Data analytics to mark out multiple PAN holders”
When Moore’s Law meets with modern day Business Intelligence, what happens? Disruption and then wider adoption!
With costs of implementing BI tools lowering, more and more enterprises are keen on jumping on-board the homebrewed variety of custom BI solution to help drive their business. The result of these efforts is that these days several organizations are pursuing data driven intelligent decision-making, at a cost, which is almost fractional compared to yesteryear’s Business Intelligence budgets.
A proper Big Data certification allows individuals to make the best of available smart BI solutions available out there!
But the question remains, as to are all these companies actually making better decisions?
Surely, most enterprises are now reaping the benefits of having a larger range of BI solutions available to them. Nevertheless, there is still a bigger room for error in the picture, which many firms tend to ignore.
If done right, BI solutions can deliver an ROI of USD 10.66 for the cost of every dollar spent on implementing them. But, as per a survey conducted by Gartner, the results are not so glorious for most firms. More than 70 percent of all BI implementations do not stand up to meet the business goals that were anticipated of them.
Due to the evolution and lowering BI solution prices, the demand for data analytics certification courses have grown by several manifolds.
Is there a secret formula to BI solution driven success? Well, starting with asking the right questions is always a good place to begin:
Do you know what the lifeblood is for BI? Why, data of course, data is what Business Intelligence strives upon. All firms do have a rudimentary strategy to collect and analyze data, however, they tend to overlook the data sources. The key here to note is – truly reliable data sources are the main difference between the success and failure of your Business Intelligence efforts.
These data sources do exist; all you have to do is choose right. In addition, the best thing about them is a lot of them are almost free of charge. Using the good ones will transform the way you look at your market, the business pipeline and the way you perceive your audience.
These are your firm’s single source data repositories. Warehouses store all the data you collect from various sources, and provide the same for when needed, on prompt for reporting and analysis. However, self-service BI tools can be a bit of hit-or-miss at times, where consistently handling data is a worry.
The key is to discover a data warehouse solution, which can efficiently store, curate and retrieve data for analysis on prompt.
Companies that are looking to use their own Business Intelligence infrastructures must identify the analytics architecture that best suits their necessities. However, unwieldy datasets in combination with a lack of processing maturity can dull the effort even before one decides to start!
For incorporating enterprise-scale Business Intelligence solutions, it is necessary to have it work effortlessly with the different other information formats, processes and systems, which have already been established previously in the internal work pipeline.
So, the key here is to ask the question – will the necessary integration cost more in terms of resources and effort that you can afford?
The most persistent challenge in BI is to wrangle data, majority of users cannot understand any of it beyond a simplified visualization. Decision-makers may be fooled with the help of powerful visualization tools. However, the truth is that making it pretty alone will not get the job done right.
So, forget pretty, and ask the all important question of whether the reporting mechanism is useful in interpreting otherwise unintelligible data or not.
If your BI solutions, directly impinges on relevant regulations (and so it will, when the time comes). Then the solutions should aid the compliance and not hinder it. A good BI solution should provide a means to trace and audit data and its sources wherever, needed.
The field of data science is evolving in expertise. And even professionals involved in the field tend to vary in their capabilities and opinions about the same. So, the important thing is to consider the importance of data in your company, and that one has all the appropriate responses to the posed questions above.
You can learn to ask the right questions with comprehensive tableau BI training courses. For more information on tableau course details feel free to contact the experts at DexLab Analytics.
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Many still believe that Big Data is a grossly misunderstood, mega trending buzzword in the tech field even today. However, there is still no denying of the fact that the recent development of AI and machine learning push is related on the synthesis and labelling of huge amounts of training data. A latest trend report by the advisory firm Ovum predicted that the Big Data market which currently is valued to be USD 1.7 billion, will further rise to be USD 9.4 billion by 2020.
Then what do the insiders in the data analytics market see it happening in the upcoming year ahead? We at DexLab Analytics, the premiere Big Data Hadoop institute in Delhi spoke to several leaders in this field to discover.
Here is what we found to be the five most important trends that will shape the future of machine learning, AI and data analytics in 2017 from the industry experts:
The predictions strongly emphasize the need for more talent and skilled personnel in this vast field of data analytics, thus, a growing demand for Big Data training and Big Data courses will be witnessed worldwide.
Continue reading “Five Major Big Data Trends That Will Shape AI this New Year”
It will be a simple understatement to say that Hadoop took the Big Data market up by storm this past years from 2012-2016. This time-period in the history of data witnessed a wave of mergers, acquisitions and high valuation rounds of finances. It will not be a simple exaggeration to state that today Hadoop is the only cost sensible and scalable open-source alternative option against the other commercially available Big Data Management tools and packages.
Recently it has not only emerged as the de-facto for all industry standard business intelligence (BI), and has become an integral part of almost all commercially available Big Data solutions.
Until 2015, it had become quite clear that Hadoop did fail to deliver in terms of revenues. From 2012 to 2015, the growth and development of Hadoop systems have been financed by venture capitalists mostly. It also made some funds through acquisition money and R&D project budgets.
But it is no doubt that Hadoop talent is sparse and also does not come in cheap. Hadoop smarts a steep learning curve that most cannot manage to climb. Yet, still more and more enterprises are finding themselves be attracted towards the gravitational pull of this massive open-source system, of Hadoop. It is mostly due to the functionality that it offers. Several interesting trends have emerged in the Hadoop market within the last 2 years like:
While still having a few obvious setbacks, it is of no doubt that, Hadoop is here to stay for the long haul. Moreover, there is rapid growth to be expected in the near future.
Image Source: aws.amazon.com
As per market, forecasts the Hadoop market is expected to grow at CAGR (compounded annual growth rate) of 58% thereby surpassing USD 16 billion by 2020.
The major players in the Hadoop industry are as follows: Teradata Corporation, Rainstor, Cloudera, Inc. and Hortonworks Inc., Fujitsu Ltd., Hitachi Data Systems, Datameer, Inc., Cisco Systems, Inc., Hewlett-Packard, Zettaset, Inc., IBM, Dell, Inc., Amazon Web Services, Datastax, Inc., MapR Technologies, Inc., etc.
Several opportunities are emerging for Hadoop market with the changing global environment where Big Data is affecting the IT businesses in the following two ways:
Image Source: tdwi.org
The forecast for Hadoop market for the years 2017-2022 can be summarised as follows:
Image Source: tdwi.org
This chain link graph shows that each component in an industry is closely linked to data analytics and management and plays an equally important role in generating business opportunities and better revenue streams.
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Today is a time when omnipresent has a whole new definition. We no longer think about the almighty, omnipotent and omnipresent God when we speak about being everywhere. Nowadays we mostly mean data when we hear the term “present everywhere”. The amount of digital data that populates the earth today is growing at a tremendous rate, doubling over every two years and transforming the way we live.
As per IBM, an astounding amount of 2.5 Billion gigabytes of data is generated every day since the year 2012. Another revelation made by an article published in the Forbes magazine stated that data is growing faster than ever before today, and by the year 2020 almost 1.7 megabytes of new information will be created every second by every human being on this earth. And that is why it is imperative to know the fundamental basics of this field as clearly this is where our future lies.
In this article, we will know the main differentiating factors between data science, Big Data analysis and data analytics. We will discuss in detail about the points such as what they are, where they are used, and the skills one needs to be a professional in these fields, and finally the prospect of salary in each case.
Data science involves dealing with unstructured and structured data. It is a field that consists of everything that relates to cleansing of data, preparation and analysis. It can be defined as the combination of mathematics, analytics, statistics, programming, capture of data and problem solving. And all of that in the most ingenious ways with an amazing ability to look at things from a unique perspective. They professionals involved with this field should be proficient in data preparation, cleansing, and alignment of data.
To put it simply, this is the umbrella of techniques which is used to extract insights and information from the data.
As the name suggests, Big Data is nothing but a mammoth amount of data. This is so huge that it cannot be processed effectively with the existing traditional applications. The processing of Big Data starts with working with raw data that is not very well aggregated and is almost impossible to store in the memory of only one single computer.
It is now a popular buzzword filling up the job portals with vacancies. And is used to denote basically a large number of data, both structured and unstructured. It inundates a business on a daily basis. It is a prime source of information that can be used to take better decisions and proper strategic business moves.
As per Gartner, Big Data can be defined as high velocity, high volume and high variety information assets which demand cost efficient, innovative forms of information processing that enable improved insight, better decision making, and a procedural automation.
Thus a Big Data certification, can help you bag the best paying jobs in the market.
Data Analytics is the science of assessing raw data with the purpose of drawing actionable insights from the same.
It basically involves application of algorithms in a mechanical and systematic process to gather information. For instance, it may involve a task like running through a large number of data sets to look for comprehensible correlations between one another.
The main focus for data analytics is concentrated on interference, which is the procedure for deriving conclusions which are mainly based on what the researchers already are aware of.
Now that you know what the path to career success, looks like stop waiting and get a R Analytics Certification today.
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Here in this post we will discuss with inspiration from the views of insider experts about how Big Data teams and IT personnel can make sense from the right kinds of data which will ultimately allow executives to make smarter business choices and drive results for their business.
The amount of data that has been created in the last two years is much more than the amount that has been created in the entire previous history of our human kind. This has led to an explosion of data analyst training institutes popping up every now and then and welcoming students from diverse backgrounds. Continue reading “How To Stop Big Data Projects From Failing?”
Do you know how much data is currently produced globally every year?
As per the reports published by IBM, the figures are 2.5 QB (Quintillion Bytes). The numeric representation of the same looks as: 2,500,000,000,000,000,000. And we thought that our mobile devices with 64GB memory space are capable of storing huge data.
As technology is expanding at the speed next to light, more companies are planning to invest in Big Data platforms for getting the best out of it. Gartner Inc. had conducted a research recently among 437 global organisations across different industries and figured out that more than 75% of them are looking forward to the benefits they can derive from Big Data. The purpose for using Big Data varied to some instance across these organisations, however most of the companies were found to use data analytics for enhancing their customer service segments. Recently, security breach has hit the headline more often than global warming and that has been a factor of worry for many data driven companies. Thus, they are opting for Big Data tools in order to strengthen their online security. Continue reading “Why Getting a Big Data Certification Will Benefit Your Small Business”
Algorithms are now like the air we breathe; it has become an inevitable part of our daily lives and is also included in all types of businesses. Experts like Gartner has called this age as the algorithm business which is the key driving force that is overthrowing the traditional ways in which we do our business and manage operations.
In fact the algorithm boom with uber diversification has reached a new high, so much so that now each function in a business has its own algorithm and one can buy their own from the algorithm marketplace. This was developed by algorithm developers at Algorithmia to save the precious time and money of business operators and other fellow developers and offers a plethora of more than 800 algorithms in the fields of machine learning, audio and visual processing and computer vision.
But we as data enthusiasts in the same field with an undying love for algorithm would like to suggest that not all the algorithms from the Algorithmia marketplace may be suitable for your needs. Business needs are highly subjective and environment based. And things as dynamic as algorithms can produce different types of results even in the slightly different situations. Also the use of algorithms depends on a number of factors on how they can be applied and what results one can expect from their application. The variables on which the application of algorithms depends are as follows: type and volume of the data sets, the function the algorithm will be applied for and the industry in which the algorithm will be applied.
Hence, not always reaching for the easy option of buying a readymade algorithm off the shelf and simply tweaking it to fit into your model may not always be the most cost-effective or time saving way to go. So, it is highly recommended for data scientists to educate themselves well on the most important algorithms that must be known by them, as well as the back of their hands. A data scientist must also know how each algorithm is developed and also which purpose calls for which algorithm to be applied.
So, our experts associated with DexLab Analytics developed an infographic to let big data analysts know the 12 most essential algorithms that must still be included in the repertoire of a skilled data scientist. To know more about data science courses drop DexLab Analytics and find your true data-based calling.
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