Let’s go back to the time when we all had no idea whatsoever about social media or the internet of things. This was the time when the concept of a personal computer, did not really exist. All these desktops were supposed to do, was to store information, to help in some calculations as a part of other related activities. This was also the time when normal storage devices, were not really normal. They used to be sold at very exorbitant prices and would set you back by more than just a few thousand rupees. Anyone who has an idea about this situation, may be pleasantly surprised of how cheap these storage devices have become lately. If you happen to be an individual with very less requirement for storage space, you might as well not buy a storage device for all intents and purposes. Meaning, why buy a hard disk, when you have free space available to you to store your things.
Today there are so many applications online, for instance Google or the famous Drop Box, these applications are providing users with up to 15 gigabytes of free storage space, to store everything they want to. With the advent of mobile phones with great storage capacities, the need to buy a pen drive has almost become negligible. Now one would wonder, how exactly would it benefit these companies to lower their storage costs. One thing is definitely sure, these companies benefit by having all of this data, in order to enhance their very service offerings. In more technical terms, it allows these companies to generate big data and thus go ahead and use it for their profit. Today, with the great advancement in every single field, due to the massive explosion of developing technology, has also led to a tremendous generation of data. In the field of Data Science, the jargon they use for this process is a person’s digital footprint. Think of it as a carbon footprint, but devoid of the negative connotations. This digital footprint of every individual consists of the data they generate, by their various dealings online, which in turn are used by companies to enhance their chances of success.
Have you every stopped and wondered, how when you open any page on your browser, you have ads of the exact things, that you were looking for about two days before. This is exactly the power of Big Data. The whole idea here is trying to map someone’s digital identity get all the information regarding the person’s likes, dislikes and then present that person with every kind of enhancement possible, from targeted recommendations, to even someday finding out if we could make clones of that person, based on their social media activities. This concept of big data is gradually, yet very effectively changing the way the world works, making it smarter, more efficient and faster. There are many theories that in the future, we would be even able to develop robots, create our own personas in the virtual world, have the most heightened artificial intelligence technologies and basically be able to harness our data a rich surroundings to their optimum potential.
Data Analytics, Big Data, Data Scientist, these are no longer big terms from a far away profession, these words or rather roles are becoming catalysts, impacting the growth of our businesses and enhancing the overall experience we get in doing our daily tasks.
Our online presence is not a matter of choice anymore; we often find ourselves using online portals to shop, connect with a doctor, research, basically from going on a vacation to preparing for motherhood, marriages, and dating, to banking, and even school and college admissions, all of these are done online, we even use social networking to express ourselves, through tweets, posts etc…,
Excessive usage of the internet creates online activity logs that contain humongous amounts of data.
Now imagine the camera’s mounted almost on every corner of the street and satellite based observations like the google map and google earth, they also collect data in large numbers on how people conduct themselves.
This data that is generated is being collected in large numbers around the clock, in real time and historic, this data further needs to be extracted, however, it is easier said than done, data is huge and extraction and explanation of the same cannot be done effortlessly. Most of the data collected is unstructured and not authentic, so you need to be wise to catch the correct characteristics at the right time.
People who can perform this extraction in a functional manner and make sense of it are called, Data Scientist or Data Analysts. The competencies that help them in this task are, sound knowledge of Mathematics, Computer Science, and Statistics.
The job of a data scientist is not only extracting data and analysing it, but to clean the data in such a manner that they can also predict and forecast trends for an assigned business, based on certain hypothesis or conditions. And that is the uniqueness they get to their job, the ability to accurately pre-process data and predict and forecast, sets one data analyst apart from the other.
A career in big data has become a dream choice for most job seekers these days, there is a lot that an organisation can achieve with the right application of data science. Some companies have identified this, and are either training their internal staff on the skills required to perform the job, while others are not yet too open to hire a full time resource. Although that day is not too far when the position of a data analyst will become imperative in every organisation.
If you are planning to enter the data science industry to make a great career in big data, then you need to adapt and acquire certain competencies and expertise in data analytics related tools, in addition to the above mentioned prerequisites. For example, programming languages, like R, and Python, SAS, a working knowledge of Machine Learning, and Predictive analysis. Also a sound knowledge in the industry you plan to work for, e.g., healthcare, or IT, Education etc.., will be an added advantage.
There is a huge gap between the demand and available resources in the field of data science, hence making a career shift in this direction would be wise and also lucrative, recent researchers have suggested that a data scientist earns more than experienced engineers. Clearly, this is a field with huge potential.
Do take up certifications, that will further assist you to springboard yourself in the field of data science.
A friend who runs a social media platform related to food proclaimed on Facebook, yesterday, ‘Data is the new oil!’ People get on his platform and talk about what they like and don’t like and where they eat, lakhs of conversations that essentially boil down to little bits of data which get analyzed to reveal what people want to eat, hate to eat and refuse to pay for; a veritable gold mine of information, or rather an oil mine, which is what an investor in his company apparently told him. Something he was quite happy to share with us on Facebook because nothing today is worth anything until a few thousand people know about it. We digress. The point I was trying to make was the biggest trend of 2017 is going to be the monetization of this data, the bits of conversation you and I have to entertain ourselves when we really should be working. This post should have been written yesterday if it were not for my heated debate about which city serves the best butter chicken. This information will then be monetized and sold to a restaurant company to help them decide on whether to serve or open another butter chicken restaurant in Mumbai. See how this works?
Forbes reckons the Data Analytics industry will soon surpass $200 billion by 2020 led by revenue growth from information-based products. Data monetization” will become a major source of revenues, as the world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015, according to IDC. It’s all well and good to have access to 100,000 conversations, but breaking them down into bits of bytes that create value is another challenge altogether. But monetizing it won’t be so easy because while firms focus on creating data, they are yet to actually create data driven cultures and technology is not the impediment. The issues lie in getting management buy –in, the refusal of organizations to align them to a culture where data creation is important. I mean, data input was the punishment that was given to an intern for god sakes, now we have to revere it?
But let’s say you do manage to get your act together, your next step is to understand the numerous competing technologies and languages. 2016 saw rapid growth in cloud data analytics, which has companies confused – cloud or premises analytics solutions. With cloud, no matter what anyone says about security, laymen like me always wonder about security. Laymen equal fast-growing CEOs who weren’t born speaking SAS. When someone says cloud, I imagine my discussion about those chips I had last evening floating in the sky somewhere, waiting for someone to pick it up and then post it on twitter with my picture/ handle next to it under the heading – Tuesday’s Glutton.
And who’s going to oversee all this data inputting? Why Mr. Chief Data Officer of-course, who comes with a nifty readymade acronym, CDO. The CDOs who will lead the organization by creating a culture conducive to the creation and storing of data. In my opinion, the roles of CDOs and CRO’ (Chief Risk Officers) will eventually merge because what is data other than the study of past historical data to predict the future, to protect against risk of losing revenue, losing clients and ensuring compliance. We talk about risk here because the Banking industry is expected to lead the investment in Data Analytics hiring and software in 2017.Ofcourse they are. They don’t want to find themselves caught on the back foot, again, do they?
What does all this mean for you? How does it affect your career? This entire recording and storing is nice enough but who’s going to help use the information? We need both Data Scientists who can analyse the data and Data Translators, domain experts with deep knowledge of the business to translate the analysis to people like you and me who want things explained to us like we are five-year-olds. Anyone who can do both is literally sitting on a large gold mine. No wonder they tell you that data analytics is the sexiest job of the 21st century.
There is a lot of confusion in the data science Job, as it is relatively new profession. We have got a lot of queries about Data Scientist salary and there career path. In this blog will talk about the how data scientist came into a picture and what is the starting salary for this job.
Statistics state that history’s most unbalanced demand and supply ratio is seen today in the Big Data Industry. It is known that in the U.S.A there would soon be a shortage of around 140,000-190,000 professionals, with the required skill set for data analytics. With a tsunami like amount of information being generated by firms on a daily basis, it becomes difficult to for them to make sense of it.
This is where the Data Scientist or the Data Analyst comes into the picture. These are individuals equipped with a certain skill set, who can take all this information or more popularly known as data and make sense of it. They work with great volumes of data sets, study them and generate various insights which help the company prosper.
As this is a fairly new thing, there are a lot of areas which are clearly out of focus. There has been no clear distinction between the two terms ‘Data Scientist’ and ‘Data Analyst’ and people still haven’t had any clear cut idea about what is meant by either Hadoop or SAS Programming and so on.
As this field needs a specific skill set like statistics, an eye for drawing out the patterns, being great at analysis and exceptional at programming knowledge; makes the number of professionals apt for this job very limited. The fact that there has been a rising demand in the firms for Data Scientists, states that the career prospects in this field have grown exponentially.
Glassdoor placed it in the first position on the 1st, as a Best Jobs in America list. According to IBM, demand for this role will soar 28% by 2020.
It is believed that the field of Data Analytics would be further divided into three different categories. These would be for professionals who would be good at coding and creating languages to sort the data, people possessing exemplary statistical skills and those who have an eye for drawing traits and patterns from the same.
With the Data Analytics Industry becoming dynamic by the day, the prospects for someone looking to make it their career are really high. The average salary of a Data Scientist starting into this industry can range from 3lakh-4lakh and can go onto 12lakh- 20lakh per annum.
There are a lot of courses offered in Data Analytics today, whereby any aspirant can get trained in various data analytics tools like R Programming, Python, SAS Programming, Big Data Hadoop and many others.
At, Imarticus Learning we offer various short term and long term courses in Data Analytics and the tools therein.
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Data is omnipresent, it is available everywhere. We cannot deny the fact that data is changing the world in ways we cannot fathom. We are at a time, where we are witnessing innovation in the way we are collecting and interpreting this data. Big data analysis is a groundbreaking step and it is only becoming bigger by the day. We are slowly realising that the use of data in almost all aspects of our lives, is actually making our lives simpler, at the same time it is presenting an opportunity for us, by helping us shape the world that we live in for the good.
There are many mainstream uses of data, which can be collected from various sources, and on interpretation can produce huge values. For example, e-commerce sites, that increase revenue with the inputs from customer feedback and shopping pattern.
Eventually, every aspect of our lives will be impacted by the advent of big data. It is not all buzz without action. There are some significant areas where big data is already making a difference, in fact in some areas it is doing so in a camouflaged approach.
Analytics is working in wonderful ways, and it has found some pretty interesting applications.
A quick read below will reveal some unobserved ways in which big data is touching our lives :
1. Enhanced Sport Performance
Most select sports are now using the benefits of big data analytics, video analytics is used to map the performance in football or baseball, sensor technology is also used in the sports equipment such as basket, golf clubs which relay feedback over smartphones, via cloud servers, on how to improve performance in the game. Health bands help track activities of sport persons outside of the sporting environment to keep a check on their nutrition and sleep also social media usage and conversations to understand the psychological wellbeing.
2. Elevating Machine Performance
Big data tools are used to control autonomous cars, for example, Toyota’s Prius is equipped with GPS, powerful computers, and sensors to safely drive on road without human intervention. Computer performance can also be enhanced by using big data tools.
3. Augmenting and Refining Cities and Countries
Many cities use big data to improve aspects of its functioning. For example, traffic situations could be better controlled, by getting factual data, by weather understanding and through social media. Some cities ae planning to use big data to upgrade themselves as smart cities, where the transport, infrastructure and utility processes are all interconnected. In other words, a train would wait for a delayed plane, or where traffic signals change their functioning on predictions to minimize traffic jams.
4. Use of Sentiments in Elections
Big data analytics can impact the way we choose our leaders, or perhaps the way the leader ensure we choose them by appealing to our sentiments and needs, voter models are designed to identify specific voters who could make a difference in elections and specifically target messages to those voters. The Obama campaign in 2012 presidential elections took this to another level into their stride.
5. Better Healthcare
Big data, with its computing ability, can assist us to decode entire DNA strings in moments. This will allow us to achieve better results and maybe understand or better still predict disease patterns. It can almost help us identify epidemics and outbreaks by linking data from medical records along with social media analytics, and imagine all this will be in actual time. Just by reading messaging like ‘not feeling good today” or “in Bed no energy” on social media.
As time extends, there will be many applications and tools of big data that will have a variety of possibilities. While it can be debated how this data is invading our privacy, it is best to look at the bright side and acknowledge how this data will make our lives easy.
By now it is mostly common knowledge, that Big Data is essentially a large volume of information collected through myriad sources, in various formats, it is also understood that this big data has a key to all future plans and strategies that the company needs to adopt if it truly wants to succeed.
True, big data is information gathered from the internet enabled services, social media, and other similar sources. It can be typically characterised by 4 V’s…
- Volume – it is getting vast as compared to the traditional sources through which data used to be captured
- Variety – data comes from various sources, machine generated and people generated
- Velocity – the speed at which this data is being generated, it is phenomenal and never stops
- Veracity – basically the quality of data, as one has little control of the volume.
The evolution of the technology has helped organisations apply the findings, not only while strategizing but in almost every aspect of the functioning of an organisation. For internal and external benefits. Merely capturing data is not beneficial, but to understand what insights you get from that data is paramount in decision making. In the ever-evolving business environment, having historical insights is not enough, but to get accurate future predictions, using data analysis and predictive modelling and visualisation techniques is also colossally essential, mostly while developing strategies.
Big data, eliminate intuition such that all imperative decisions can be made through a structured approach, and with a data-driven insight.
To put it simply it is a broad three-step process performed in a loop. (a) Manage Data- extract relevant data (b) Perform analytics on the data – gain insights and use algorithm’s (c) Make Decisions.
Big Data can further Benefit organisations in the below mentioned 5 areas
- Comprehend market Conditions – through big data, organisations can predict what future customer behaviour will be, purchasing patterns, choices, product preferences. This will leverage the company, and help contest competitors.
- Know your Customer Better – through big data analysis, companies come to know the general thought process and feedback in advance and make course corrections. Companies can reduce complaints and act on it before it becomes big. There are big data tools that predict negative emotions, prompt action can be taken to mitigate the same by organisations.
- Control Online Reputation – Sentimental analysis can be done through Big Data Tools; thus a company can check on what is being said by whom online and manage their online image efficiently and effectively.
- Cost Saving – firstly, there might be an initial cost of application of big data tools, but in the long run, the benefits will outweigh the cost. Secondly, with the application of real-time big data tools, the IT staff will be less burdened, so these resources could be used elsewhere, and lastly, the application of big data technology will make storing of data easier and more accurate.
- Availability of Data – Through Big Data tools, relevant data can be available, in an accurate and structured format, in real time.
The value of Big Data Insights is priceless. One needs to have the patience and discipline while application of the same. Ask the right question to gain accurate insights. With the quality of data, the possibilities are great, for businesses to flourish.
Data engineering and data scientist are job titles which might be new to us in recent times, however, these roles have been around for a while.
Traditionally, anyone who would analyse data would be called a Data Analyst, and the person responsible for creating platforms to support the analysis is a Business Developer.
In the world of IT, the data scientist gets more visibility and praise, as they are the ones, extracting vital intelligence from big data and help organisations take critical decisions with regards to their business swiftly. But it is important to note that the data scientist does not work in isolation, they are not capable of generating valuable information independently, and they need the constant support of Data Engineers. The engineers are the ones designing and maintaining software and platforms that operate the big data pipeline. They set the stage and keep it running.
A Data Scientist is someone who is an excellent statistician, with above average software engineering skills. Should be primarily inquisitive, have the skills of data visualisation and storytelling along with programming skills. His tasks would essentially be to identify the question and finding answers through data, finding a correlation between dissimilar data, to be able to tell the findings, hence storytelling ability, and lastly should be hands on with tools like Julia, Python Programming, data visualisation tools like Qlik view or Tableau.
The description of Data Engineers and Data Scientist can be quite obscure, there is an overlap. While these roles still maintain to be distinct data science job roles, they require different skills and experience. Some data scientist can do data engineering, while some data engineers can do data analysis and visualisation as well.
The emergence of big data has opened space for new titles and roles to come into existence. Over the past couple of years’ businesses have applied all means to get individuals who have the skills to turn data into gold.
A lot has changed in the way businesses function, earlier a lot of companies were functioning in the physical world, nowadays most businesses function on the digital platform. When a company is mostly functioning online, there is a huge accumulation of data. Data about who is visiting your website, if they are choosing your competitor’s website as opposed to yours, what could be the reason, you also get data about the statistics of the competitor’s target audience, So the possibility of the data accumulation is too big and very fast. The data are screaming information and is noisy beyond comprehension.
In order to find a way in this data, one needs to sort this in two ways,
Firstly, to create a database to process the data and to store it and the second would be the need of people to comprehend the data and know how to ask a relevant question and research the data in a method that the concerned business can take informed pointers from it. This stored data needs people who know statistics who know how to write code, in order to get insightful information.
Data Scientist and Data Engineers are these people; they are the need of the hour. To know how to process data using various platforms, and more importantly, we need them to be around, These people also know how making sense of the information, how to analyse it. They don’t only plot graphs from data collected from a spreadsheet but also create statistical models that over a period of time affects the business and products with effective ways to increase the revenue.
The data available could be stand but smart and appropriately skilled people are the ones who help find that needle in the haystack.
Welcome to the age of instant gratification. The demand for the instant seems to be seeping in every corner of our society. The types of food we eat (the high range of ready to eat meals or the typical fast food chains, instant reviews to the food we eat) you see it all around you, let that be in the retail market (speedy deliveries), or the entertainment business (you have latest movies with instant virtual streaming available), travel (apps that help you book cabs, no waiting for them anymore, apps to make hotel and restaurant reservation).
Most of our lives are hyper-connected. Living in the now and thus subconsciously continually giving in to the era of instant gratification and the interim loss of patience.
Now let’s understand the personalities of Trading and Investment.
Mr trading is very competitive, most often taking risks while meeting the desired results. To some, he may come across as aggressive or impatient, but it mostly stems from the ambitious aspiration he has set for himself. Mr Trading’s primary focus is on quantity, as he believes eventually quality will be achieved through chance. He is very outgoing and is usually revered by most people he meets. His friends describe him as a workaholic, as he has an obsessive need to take risky decisions in the nick of time and hence is always on the run. There is a strong sense of control that he constantly demands.
To be specific he gets his drive to be committed to his line of work from the constant adrenaline rush that he gets on cracking the right deals and overcoming risk if any in the process. He is usually under a lot of stress but loves the mystic and the adrenaline rush that his job gets.
Mr Investment (Arch Nemesis of Mr Trading)
The steady worker, who derives his motivation from the end result. When faced with competition focuses less on winning or losing, but does the right thing to eventually get the desired result, and in some sense enjoys the process. He is well networked and also well respected within by peers. Is known to be informative and analytical. A hard worker who is organised and calculative hence is gradually and eventually accurate in getting the set target.
So you see Investing and Trading are two very different methods in attempting to make profits in the financial markets.
Investing is a method of gradually building wealth over an extended period of time through the buying and holding of stocks, Mutual funds, Bonds and other investment instruments. Investors often enhance their profits through reinvesting or compounding any profits and dividends into additional shares of stock. Investments are long term, ranging from a couple of years or even decades, in turn, taking advantage of perks like interests. Markets may fluctuate but investors usually ride out the lows with the expectations that the losses will be covered with rebound.
Trading is, on the other hand, a more frequent buying and selling of stock, commodities with the goal of outperforming the buy and hold investing method. Profits are made by buying at a lower price and selling at a higher price during short intervals. With a myopic investment goal to make money.
Everyone likes making money and usually, that is the driving factor, the “high” from making more out of less, it’s not only a natural high but an adrenaline rush, and like all addictions, one time is never going to be enough. It is a short time commitment to big results or even big losses and the risk makes it attractive.
While Investing is a more proactive method to make your money work and make more money for you. In investing you buy an asset like shares and the straightforward idea is to sell it at a future date when the value appreciates. It’s common knowledge that good investment is a sound way of growing wealth, it’s a long term commitment.
However, it is interesting to know as true Arch Nemesis, both investment and trading cannot completely exist without one another. You see without trader’s, investments will not have any liquidity to buy and sell stock, and without investors, traders will not have any origin or any source to buy and sell from, hence no one can be grander to another.
The only place where the scales lean are the personalities that get attracted to the fast and the furious, and in that Trading emerges as the prototypical winner.
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You cannot achieve tomorrow’s results using yesterday’s methods, and this line is the need to understand and accept the impact of BIG DATA and growing demands of business.
Big data means access to big information, leading to abilities in doing things you could not do before.
Case in point – a small exercise…
Before you read ahead, check the posture you are sitting in, now look around and check the posture of others sitting beside you, observe… what do you see? Someone is slouching. Someone has their legs crossed, elbows resting on the armchair, palms pressed against their chins, a constant moving of the limbs. Not everyone sitting around you has the same posture. What if we put many sensors on the chairs around you to help record and create an index, which is unique to you, which says person X sits in these particular postures during specific intervals of the day?
In addition, say what if this data was sold to car manufacturers as an anti-theft design to help recognise that the person sitting behind the wheel is not the same and say until he keys in a password the car even though entered would not start.
Now imagine what if every single car in the world had this technology, think of the benefits of aggregating this data. Maybe we would be able to predict which postures while driving would lead to an accident say in something as close as the next 7 seconds do, and alert the driver to change position or take a break from driving.
Now my dear reader THAT is the power of BIG DATA. It can help record, collect, understand, predict, and prevent events from a random collection of information.
So if that is so useful where lies the problem? Why is the world not already a better place?
Because there is a gap between opportunity and demand in skilled professionals to help comprehend and present valuable insights into specific relevant trends, big or small from the available data.
Big data is all around us and there is a calling need to preserve all the generated data for the fear of missing something that could be important. Hence, comes the need for big data Analytics, Big data is crucial to do better business, to take accurate decisions and in always being a step ahead of your competitors. Therefore, if you are a professional in the Analytics domain there is a sea of opportunity waiting for you to dive in.
Big data Analytics is Unfathomable and depending on the environment one can choose from
- Prescriptive Analytics
- Descriptive Analytics
- Predictive Analytics
Big Data Analytics market is predicted to surpass $125 billion in between 2015 – 2020, which in turn in some sense means handsome pay brackets for the skilled individual.
- Salary Aspect
To improve the performance of the organisation, most companies have either already implemented or are in the process of implementing Big Data Analytics, as they already have the data at their disposal.
- All Organisations adopting some form of Data Analytics = Growing Market
There is a big gap in skilled professionals who are able to convert the data available. The ability to see small trends from the pool of big information. Which ultimately advances the organisation in the right direction. There are two types of talent deficits. Data consultants – who have the ability to not only, understand but also use the data at hand in the appropriate way and the other is Data Scientists who can perform analytics.
- Skill Deficit
Big data Analytics is not restricted to any specific Domain; it can be and in recent times is being used in Healthcare, Automobiles, Manufacturing and more, creating a massive global demand.
- Global demand across industries
Analytics becomes a competitive resource for organisations, according to certain studies Analytics has already become the most important asset in current times. Because we are emerging from the undeveloped analytics trends to more advanced forms. It is undeniable that Analytics plays a vital role in decision making and taking strategic initiatives for the business.
- Importance of Analytics for Better Decision Making in Organisation
So in deduction to the above, Analytics no matter how advanced is human dependent. These are exciting times for skilled people who can comprehend data and give valuable insights from the business point of view. A trained individual with the right Analytics insight can master an ocean of big data and become an indispensable asset to the organisation becoming a springboard to the business and their career.
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Any successful innovation is a result of a good measure of disruption. Over this, you must add a very generous helping of talent and a lot of creativity to go into the mixture and finally a splash of intuition and you’re good to go.
While this may be the probable recipe for any innovation, but does it also happen to be the recipe for a successful innovation? That seems to be a different story altogether, because usually the one thing that any successful innovation depends on, is whether it meets or exceeds the assigned business goals.
It also finally boils down to the inclusion of big data into the mix. It is this ingredient that would help you ensure that your innovation is very successful. This is how you will figure out the coveted je ne sais quoi for your success. So one must remember to always get out their big data analytics tools and crunch some data in order to get amazing results.
Innovativeness, responsiveness, and resilience happen to be the trifecta of deriving business agility. These happen to be the core business drivers, which when put together, you have a great picture of how businesses deal with change.
Analysing the information that you have gathered or that is at your disposal, will help any organization deal much better with change as a result of a thorough optimization process. All a professional is required to do is gather all the data that is available on anything that they are doing, crunch all the numbers and go on to make recommendations on what all changes need to be made in order to ensure the betterment of the process.
While data analytics may be supremely efficient in making human processes more efficient, it has also experienced one flaw. That flaw as surprising as it may sound is humans. This would be more clear as the processes become complex. For example, when the data grows, it inadvertently means that the need for analysing the same also grows. But the downside here is that people, as a rule, have a limited attention span. So when it comes to analysing information, this attention span can prove detrimental in the processing of the information. So in a way no matter how good and exemplary your data analytics tool is in giving off results, it is redundant if there happens to be no one to read them or even understand them.
This is the reason why in order to eliminate the weakest link, there are many organizations which are trying to establish totally automated feedback loops. For instance take a firm which is responsible for managing giant amounts of data, from airports to factories and data centres. While the traditional approach to handling any kind of glitches here would be, drawing up of a number of reports, based on mathematical formulas and adjust the maintenance schedule accordingly. On the other hand, if there are mechanisms that would know beforehand when the glitches would occur and correct them way before happening, this would be a better arrangement.
This is why Data Analytics is becoming the most sought after profession, with many data aspirants trying to get professionally trained from Imarticus Learning.
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