Designing Big Data architecture is no mean feat; rather it is a very challenging task, considering the variety, volume and velocity of data in today’s world. Coupled with the speed of technological innovations and drawing out competitive strategies, the job profile of a Big Data architect demands him to take the bull by the horns.
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‘Big Data’, and then there is ‘Data Science’. These terms are found everywhere, but there is a constant issue lingering with their effectiveness. How effective is data science? Is Big Data an overhyped concept stealing the thunder?
Summing this up, Tim Harford stated in a leading financial magazine –“Big Data has arrived, but big insights have not.” Well, to be precise, Data Science nor Big Data are to be blamed for this, whereas the truth is there exists a lot of data around, but in different places. The aggregation of data is difficult and time-consuming.
Statistically, Data science may be the next-big-thing, but it is yet to become mainstream. Though prognosticators predict 50% of organizations are going to use Data Science in 2017, more practical visionaries put the numbers closer to 15%. Big Data is hard, but it is Data Science that is even harder. Gartner reports, “Only 15% organizations are able to channelize Data Science to production.” – The reason being the gap existing between Data Science expectations and reality.
Big Data is relied upon so extensively that companies have started to expect more than it can actually deliver. Additionally, analytics-generated insights are easier to be replicated – of late, we studied a financial services company where we found a model based on Big Data technology only to learn later that the developers had already developed similar models for several other banks. It means, duplication is to be expected largely.
However, Big Data is the key to Data Science success. For years, the market remained exhilarated about Big Data. Yet, years after big data infused into Hadoop, Spark, etc., Data Science is nowhere near a 50% adoption rate. To get the best out of this revered technology, organizations need vast pools of data and not the latest algorithms. But the biggest reason for Big Data failure is that most of the companies cannot muster in the information they have, properly. They don’t know how to manage it, evaluate it in the exact ways that amplify their understanding, and bring in changes according to newer insights developed. Companies never automatically develop these competencies; they first need to know how to use the data in the correct manner in their mainframe systems, much the way he statisticians’ master arithmetic before they start on with algebra. So, unless and until a company learns to derive out the best from its data and analysis, Data Science has no role to play.
Even if companies manage to get past the above mentioned hurdles, they fail miserably in finding skillful data scientists, who are the right guys for the job in question. Veritable data scientists are rare to find these days. Several universities are found offering Data Science programs for the learners, but instead of focusing on the theoretical approach, Data Science is a more practical discipline. Classroom training is not what you should be looking for. Seek for a premier Data analyst training institute and grab the fundamentals of Data Science. DexLab Analytics is here with its amazing analyst courses in Delhi. Get enrolled today to outshine your peers and leave an imprint in the bigger Big Data community for long.
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Of late, we have been scrounging through plenty of news about healthcare challenges and gruelling choices confronted by hospital authorities, administrators, researchers, pharmaceutical in-charges and clinicians. Coupled with that, consumers are battling increased costs without corresponding enhancement in health security or in the authenticity of clinical consequences.
However, just as every dark cloud has a silver lining – the healthcare industry is now at the threshold of a major transformation using the stroke of luck Big Data and Hadoop.
In this blog, we are going to brash about 4 above the rest big data trends in 2018, and trust me they are mind blowing:
A supreme objective of modern healthcare facilities is to offer value-based and patient-centric service with the use of veritable health information technology in order to:
By leveraging information technology and concentrating on healthcare systems on patient results, a spectrum of doctors, health insurance, care and hospitals need to correspond with each other to customize care that is price effective, efficient in quality, transparent in delivery and billing and based on patient satisfaction level.
If reports are right, over $120 billion has been spent on healthcare IOT over 4 years, ONLY. Most of the data derived by the healthcare IOT is unstructured, thus helming ways for the use of Hadoop and advanced big data analytics relying on Hadoop framework.
Advanced monitoring devices interacting with other patient devices could possibly reduce the chances of direct doctor’s intervention, and might substitute it with a phone call from the nurse. Moreover, other smart devices installed can detect if medicines have been consumed regularly at home from smart dispensers. In the event of failure, the device will instantly initiate a call to help patients take medications, properly. From this, you can understand the costs will fall drastically, while improving the patient care.
After suffering from spiralling healthcare costs for years, big data is a solace to our finances. By initiating predictive modelling structured on the Hadoop big data platform, identification of erroneous claims in a systematic and repeatable is possible, resulting in a generation of 2200% return on advanced big data technology.
Now, the healthcare organisations can inspect and evaluate patient records and billing anomalies and identify frauds. It has been made possible by going back in time to analyse unstructured datasets and implement machine learning algorithms to detect the inconsistencies.
Predictive Modelling is being used worldwide, by deriving data from EHRs (Electronic Health Records) to reduce mortality rates from diseases like congestive heart failure and sepsis. As you all know, Congestive Heart Failure (CHF) is one of the costliest health problems and needs huge healthcare spending. So, the earlier it is diagnosed the better it is, without getting into the expensive complications.
Predictive analytics combined with machine learning on large sample sizes, containing more patients’ data can expose all the nuances and sequences that couldn’t be uncovered previously.
Concisely, the more healthcare organizations adopt Hadoop and advanced big data technology, the more profound will be the data dissemination across teams and partners, which will further boost patients’ easy cure and reduction in costs.
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You have employees? And they bring smartphones to work? Is everything right? Or wrong?
Period.
The moment an employee carries a personal mobile device, be it a smartphone or a tablet, to work, a merger of personal and professional is bound to happen. And this could definitely give a rough time to the employer. If not handled properly.
Of late, there has been a lot of furore, thanks to our effervescent, ever-efficient media about messaging apps. But the headlines took e negative bend when a London- based banker was fired and fined by FCA for exposing crucial confidential data through WhatsApp. Though he defended himself by stating that he simply wanted to MAKE AN IMPRESSION on his friend, he was booked under cybercrime sections.
Over the past few decades, the communication forms have undergone a magnanimous evolution. Once a mail-driven society is now a bustling centre of myriad high-on-function communication apps, the apps includes personal, social and enterprise-oriented apps. However, with new technologies materializes new challenges. The best way to manage such personal apps is by ensuring safe and secure mode of communication, instead of banning them completely. Embrace the BYOD culture but with due protective measures.
MDM is the best way to ensure productivity from the employees, while administering their mobile devices. It allows the employees to access data and meaningful information without posing any threat to company data. By implementing MDM, companies can keep a tab on corporate data segregation, corporate policies, secure emails and confidential documents, and integrate and manage mobile devices. Sometimes, a company can go a step higher by restricting users from using WhatsApp on their company provided device, and in its place give them some secure and safe team messaging solution.
For safekeeping of confidential company data, make sure you provide your employees an efficient messaging app. Choose an app that ensures better control over the information that is to be accessed or shared by the users.
The app should be used by the team admin to keep an eye on the team’s activities and the content that they are sharing. They are the ones responsible to control who can or cannot join the team, along with blocking external domains.
It is advisable to select a tool that provides its users advanced controls, from basic channel level. Flock is developed on these mechanisms and empowers the channel admin to delete any content, and add/remove members from the team. These ways are good to go in restricting the leakage of confidential data through company professionals.
Make your employees, your strength and not weakness. They are the best defence against any attempt of breaching crucial data. So, ensure compliance by conducting frequent safety awareness audits and workshops. Also, make sure that not every employee has access to sensitive company data, as it enhances the risks of becoming a victim of cybercrime.
Still wondering, what have you done to secure your company’s confidential data?
For more tips and advices, keep updated via DexLab Analytics. The prime Big Data training institute feels honoured to offer a wide spectrum of intensive courses on Data Science Online training in Gurgaon for aspiring students and industry professionals.
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Big data is the big word, NOW. Data sets are becoming more and more large and complex, making it extremely troublesome to coordinate activities using on-hand database management tools.
The flourishing growth in IT industry has triggered numerous complimentary conditions. One of the conditions is the emergence of Big Data. This two-word seven-letter catch phrase deals with a humongous amount of data, which is of prime importance in the eyes of the company in question. And the resultant effect leads to another branch of science, which is Data Analytics.
A/B Testing is a powerful assessment tool to determine which version of an app or a webpage helps an individual or his business meet future goals effectively and positively. The decision is not abrupt; it is taken after carefully comparing various versions to reveal out the best of the lot.
A/B Testing forms an integral part in web development and big data industry. It ensures that the alterations happening on a webpage or any page component are data-driven and not opinion-based.
This comprises of a set of techniques to find out interesting relationships, i.e. ‘association rules’ amidst variables in massive databases. The methods include an assortment of algorithms to initiate and test possible rules.
The following flowchart, a market basket analysis is being focused. Here, a retailer ascertains which products are high in demand and eventually use this data for successful marketing.
Statistical Classification is implemented to:
It is a method of recognizing categories, in which the new observation falls into. It needs a training set of appropriately identified observations, aka historical data.
Well, this is a complex multi-level process involving correlation, association, combination of information and data from one and many sources, to attain a superior position, determine estimates and finish timely assessments of projects. By combining data from multiple sensors, data integration and fusion helps in improving overall accuracy and direct more specific inferences, which would have otherwise been impossible from a single sensor alone.
Identify patterns and strike relationships, with Data Mining. It is nothing but the collective data extraction techniques to be performed on a large chunk of data. Some of the common data mining parameters are Association, Classification, Clustering, Sequence Analysis and Forecasting.
Generally, applications involve mining customer data to deduce segments and understand market basket analyses. It helps understanding the purchase behaviour of customers.
Non-linear predictive models are mostly used for pattern recognition and optimization. Some of the applications ask for supervised learning, whereas some invites unsupervised learning.
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Most people decipher – Hadoop and Big Data are the two sides of the same coin. Adding the fascinating word to your resume leads to better opportunities and higher pay structure. But what the future holds for Hadoop? Is it dismal or encouraging?
Continue reading “Hadoop 2017: The Survivor and Not the Casualty”
By mobilizing the volume and wealth of information in an organization, Big Data leads to improved customer perceptiveness, competitive advantage and operational efficiency. In the current data-centric era, big data is the buzzword. Nevertheless, how many of you actually know what it entails?
In this blog, we have compiled few FAQs, which will instantly shed some light about the basics of Big Data and its implementation.
Substantially complex, big data involve hundreds and thousands of terabytes or exabytes of data (starts with 1 and has 18 zeros after it, or 1 million terabytes) per single data set. If explained in simple words, big data is a collection of data sets, which comes from a variety of sources, like customer data, Internet of Things and social media. If compiled and analyzed in the right manner, it helps in understanding the nature of the lifestyle and purchasing habits of people and customers better.
The answer to this question is a bit challenging. Depending on the infrastructure of the market, the threshold limit of big data is determined. In most of the cases, the lower boundary of big data is limited to 1 to 3 terabytes.
However, using big data technologies for small databases can prove to be effective. Netezza brings about 200 built-in computer programs, like Python and Revolution R, which gained immense appreciation for being applicable to small databases.
Intuition is consequential, as ever. Staring at the humongous amount of data compels us to start from somewhere. As there is so much data, intuition is important, like never before. If you ask me, big data hasn’t yet replaced intuition, in fact the latter somehow complements the former. Both of them share a continuum relationship, instead of binary.
Transactional data, social data and machine data- are the chief sources of big data. Top-notch retailers like Amazon and Dominos boasting of more 1 million customer transactions per day results in to the generation of petabytes of transactional big data. Social media data comprise of 230 million tweets on Twitter per day, more than 60 hours of video uploaded every minute on YouTube and 2.7 billion Likes and Comments on Facebook appearing every day. Lastly, machine data can be boiled down to various modes, including the information generated by industrial equipment, web logs tracking behavioural data and real-time data emanating from sensors.
Adopt interactive data visualization tools and take your business to new heights. These tools are rewarding, say thanks to Big Data! Big conglomerates, like Google, Netflix, Amazon, Apple, Facebook and Twitter embraced the tools to visualize data. And this goes beyond the basic usage of graphs, excel charts and pivot tables.
Well, yes, very much so. Big Data is leading the future and is going to stay HERE AND NOW. It is right on its way to fundamentally transform the ways in which companies function and regard their competitors, customers and overall business.
Are you thinking to kick-start your career with Big Data Hadoop courses, or have any other queries? Speak to our consultants at DexLab Analytics. Our Big Data Hadoop institute in Delhi will cater to your every data science needs.
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The saying goes – ‘necessity is the mother of all inventions’ and with the advent of globalization we have witnessed this aphorism in its sincerest form. A new wave of competition and profit generation owing to the advent of the internet, within the labyrinth of our society has led to the creation of Data at a scale previously unthinkable. To capture the essence of this huge amount of data, a new term Big Data, was coined which meant extremely large data sets, which are to be analyzed to reveal patterns that lie within.
Today, technology has become the backbone of the society and data is its vertebrae. The technological boom began and became common around year 2000; this is when data monetization became apparent.
To simplify, data monetization is the act of generating revenue by exchanging, processing and analysis of data. Processing and analyzing means extraction of value from a particular set of data; eventually this value is to be interpreted to make decisions.
The need for data analysis is apparent since the digital universe is expected to grow 50-fold in terms of data by 2020, yet today only about 1% of the data is analyzed.
To capitalize on data monetization, we can employ the following approaches:
Already 70% of the large institutions purchase external data and monetization of the information asset is still in its infant stage. According to a study performed by Gartner, Data Monetization will be performed by 30% of the companies or more and in a survey conducted by IBM, data monetization was found to be among top 5 priorities of an organization.
The above clearly implies the upward trajectory growth in the near future in this industry, and with the application of the above-mentioned approaches, an effective strategy can be implemented by any organization hoping to be a part of the Data Monetization phenomenon.
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Considering the complex competitive global environment, the world business today is witnessing a paradigm shift from mere data storage to data mining and other subsequent activities.
Thus, from a managerial perspective it is of prime importance to develop a psyche, which can interpret the collection of data. This psyche cannot be theoretically learnt from books, as it requires a knack to make data talk. Data is no more evaluated independently. Today, a cross-domain relationship between data exists, which on analysis depicts patterns, responsible enough to do wonders for the organization.
The question is how can we connect the dots? Following the recent trends, developers are grabbing every opportunity to break a huge chunk of data into meaningful relevant information. From the standpoint of technical professionals, along with an analytical mindset, they need to get hands on experience on the technological perspective to understand the real significance of data evaluation.
Read Also : DexLab Analytics – Training the Future to be Big Data Analytics Fluent
The data not only aligns with the internal activity of the business but also is an integral part for consumer servicing. There is an intense need to study the needs of consumer and every decision he makes, which broadens the outlook of a business on how he/she is using their product. What are the expectations of the customer from an existing product? What more my customer needs? The answers to these questions cannot always be mapped quantitatively but a qualitative approach towards data is one of the key aspects of data analytics.
In this digital era, slightest technological ripples are going to reshuffle the whole industry scenario. And, that is why the omnipresence of data will aid businesses in setting new benchmarks in consumer and market findings. Growing pace of social media would open a Pandora’s Box for companies, who have their right audience in this particular domain.
The emergence of IOT, which primarily thrives on data, will cause disruption in the current business orientation. The data producing sensor architecture directly connected to the company can help the business to be fast and robust, which is the need of an hour. In addition, this analytics might influence mid-size distribution largely.
Simple example of this model: Sensors attached to tyres could sense data, and alert a tyre manufacturer about the usage of a consumer, which will help in servicing their customer at the right moment.
Thus, on an individualistic note there is need to develop a data analytical mindset and include data-ception in perception.
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