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Predictive analysis: A need

Predictive analysis is the need of the hour for all the businesses. Ample data is available and can be used by the companies for making analysis right for customer satisfaction trends. Data collection is becoming cost effective and can be used by any medium scale enterprise also. The analysts use them to draw meaningful results to help their companies grow. The techniques in predictive analysis include: clustering, decision trees and linear regression. The applications where predictive analysis can be used are:
• Content marketing
• E-commerce
• Social media marketing
Thus, predictive analysis is a useful tool that can help the business to provide better products and services. 

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Inward looking Employee Innovation

To keep track with the increased competition in today’s business environments, business leaders look outward to find potential innovation. Facing competition from start-ups, established businesses are inclined to pursue mergers and acquisitions. Such outward looking attitude might cause leaders to overlook ideas of innovation available internally. With the help of digital collaboration tools, allowing for staff participation in innovation encourages teamwork and camaraderie. Inward looking innovation involves the following few steps:

1) Formalizing the program: Setting up innovation programs on whims leads to major failures and hence, such programs should be formalized with guidelines. Larger companies set up their own incubation centres where their teams operate as start-ups whereas smaller companies enable teams to embark on pet projects.

2) Breaking down data silos: Data silos decelerate growth and innovation programs should break down such silos. Participants should be allowed to tap on a variety of skills and perspectives by using collaboration tools like Trello, Slack and Conceptboard.

3) Track Progress:  There should be a time element in the program lest projects won’t progress. Making events out of program milestones is a good way to inject excitement.

4) Provide executive support: Leadership plays a critical role in any innovation program with the duty of keeping their efforts aligned with the business goals. The momentum should not break down. Proper planning is indispensable for efficient time and workload management. 

Such efforts bring concepts closer to reality and not only seek to generate bright ideas. 

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Social Media Analytics behind a successful Business

In the digital age, social media is the most effective tool that helps improving and promoting any type of business content, get customer feedback and improve the overall reach of the company. This has led to the emergence of social media analytics which involves optimizing analytics and social median data into usable information to interpret how exactly the business content is going with the community of followers. 

A clear definition of the company goals lies at the root of such social media analytics with business objectives varying from prioritizing customer service to establishing a name in a niche or industry. Once a goal is defined, social media analytics could be used to frame strategies for accomplishment of such desired goals. Without a goal in the background, the system might go haywire bringing no effective results.

Being saturated with millions of click bait type contents, generic content does not work for the social media and the marketing world. Social media has restricted amount of characters hence, drawing of these characters should be maximized which helps to see the type of content that resounds best with the followers. Analyzing the increasing leads attained through content makes way for an inflated sales basket for the business. Average content does not acquire any place and being concise and thought-provoking in a post yields better results.

The success of a business lies in the timing of a post. Posting about a particular content at a specific time can increase sales. Also publishing a post on high traffic days can bring in commendable results for the business concerned. 

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Creative AI

Creativity and Innovation are what makes us truly human. Starting with visually representing the stunning features of animal species on paper to making intricate structures on road we can do it all. However such creative and innovative skill set are also present in some AIs who with human like capabilities and behaviours and under the guidance of the programmers have produced original paintings, songs and digital artwork. While some make their own dance moves, others rhyme like Kayne. Few examples of today’s artistic AIs include:

Dance-Dance Revolution- An AI inside a white fabric dome structure, records and processes dance moves performed by people and then incorporates those dance moves inside a “Virtual Dancer” after ‘remembering’ from human dancers.

Blooming Dinos- Creation of “botanical dinosaurs” images composed entirely of flowers and plants by AI.

The stuff of nightmares- By using deep learning, a system of data structures that form connections similar to that of neurons firing in the human brain, an AI project aptly named "Nightmare Machine” makes pictures frightening.

Master Class- An AI named “Vincent” assists humans to produce digital creations of canvasses of some of the most celebrated painters of the 19th and 20th centuries.

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Prevention in Data Sciences

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

  1. Assuming your data is ready to use and all you need
  2. Not exploring your data set before starting work
  3. Not using control group to test your new data model in action
  4. Starting with targets rather than hypotheses
  5. Automating without monitoring the final outcome

To study mistakes like these read


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Innovations Finds Hood Under Predictive Analysis!

What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

To know more about how predictive analysis can help in innovation read


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A Look into Future – Introduction to Predictive Analysis

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

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Garbage In is Garbage Out in Data Sciences!

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit:

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Artificial Intelligence: A boon or a bane for employment

Destroying traditional jobs but creating new ones, technical innovations have changed the course of work over the years. The Industrial Revolution of the 18th century marked the transition to new manufacturing processes, effectively increasing the output levels and discovering the modern industrial marvels. With AI improving the standard of living, the current and future generations are likely to witness taxing employment pattern changes.

AI would change the future of work by bringing about the following changes:

1)      Create new jobs: Tasks requiring the least of the human cognitive mind would be dealt with the application of modern AI powered robotics allowing individuals to devote their time to community services, volunteering etc.

2)      Bring Automation: A research carried out by Carl Benedikt Frey and Michael Osborne of Oxford University in 2013 reported that approximately 47% of jobs would be automated in the next few decades with non-routine jobs and tasks requiring  high cognitive and good social skills having the lowest probability of being automated compared to a greater probability involved in automation of manual jobs and routine jobs like data entry, production logistics etc.

3)      Increase the gap between the owner and the worker: AI is likely to widen the gap between high skilled and low skilled workers and also increase the persistent inequality between the owner and the workers by laying off workers that would inflate the profit margin of the owners as robots and chat-bots would not demand overtime allowances.

Gartner, the global research and advisory firm, reported that AI is creating more jobs than it is destroying by bringing about a net increase of nearly 2 million jobs by 2025. The core objective of AI should be to make human workers more efficient without laying them off. AI coupled with human intelligence is all set to revolutionize the economy we inhabit.

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Life saving Artificial intelligence

Artificial Intelligence, big data and machine learning have been ruling the industry in recent times. Starting from Amazon to Google, indulgence in predictive modelling is indispensible. When it comes to the human body, well, artificial intelligence plays a pivotal role in saving lives. Rampant use of AI is involved in CT scans in cases of stroke or brain injuries. Radiologists have a backlog of cases which might delay the detection of the criticality involved in a particular case. To the rescue comes AI, which by streamlining the CT scan interpretation workflow by triage process and automation of the initial screening process, radically reduces the time lapse in detection and diagnosis of time sensitive cases. To detect abnormalities demanding urgent attention such as intracranial haemorrhage, cranial fractures, midline shifts etc, has provided automated deep learning algorithms to assist physicians. The algorithms’ accuracy is equal to that of a physician and classification algorithms are used in radiology itself. TITAN X of NVIDIA, cuDNN and GeForce GTX 1080 GPUs were used that achieved almost 95% accuracy rate as compared to that of 97% by radiologists. Such AI algorithms tend to become a life saver in a world where there is an acute shortage of specialized radiologists.

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Analytics in Construction Business: Scope and uses.

Business Intelligence and Business Analytics are being used interchangeably nowadays in almost every field of businesses worldwide involving, in particular, leveraging the data of a company in order to evolve and grow. In addition to other sectors, predictive analytics greatly benefit the construction business categorizing information with relevance and accuracy. Predictive Analytics assists in the following ways:

1)      Leveraging work packages: Predictive Analytics helps in task breakdown matching the right people for the right job, scans past project documents, including the Work Breakdown Structure (WBS), and assess the fallacies in project execution. With such technical know-how, businesses can scientifically cut on resourcing costs without compromising on potential.

2)      Prescribe, Predict and Describe: Descriptive Analytics creates a database containing the failures and their severity which is followed by predictive analytics analyzing their recurrence. Finally, prescriptive analytics explores options that can prevent such fallacies in future work.

3)      Scanning risks: The construction space advocates vociferously for the health and safety of the crew and this purpose could be served by predictive analysis in conjunction with prescriptive analytics. Pinpointing disaster zones to nth degree accuracy and using pedometer analytics to measure the distance the crew covers, predictive analysis places heavy-duty equipments at various access points improving visibility in low lying areas and also alerts about the resources that demand servicing. It ensures both the project’s progress and the business’s adherence to the crew’s safety standards.

4)      Lowered production costs: Manual monitoring methods are laborious and entail a cost on the business’s profits. GE’s Kimberlite Survey reported that businesses using predictive approach using retrofit sensors and cloud computing experienced approximately 40% less unplanned downtime.

5)      Immersive Insight: Predictive analysis converts dormant data into actionable analysis and prevents any information from lying unanalyzed.

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Big Data Analytics in the Indian Economy

Data has become a central part of the economy and applications in analytics have been proliferating fast from private to the public sector. Data collection and analysis are at the root of critical economic decision-making which makes the socio-economic issues easy to interpret and comprehend, thus playing a pivotal role in economic battles. Big data analytics help the government in infusing transparency into the system, combat fraudulence and deliver public services effectively and efficiently.

The year 2017 will always be known to have triggered off this big data journey with Demonetization and GST being the two notable data-driven policies injected into the system coupled with the shift in focus to the macroeconomic issues like Aadhar data collection which gained an edge to bring economic reforms. Using big data analytics, the following few untapped areas can positively impact the government:

1)      Tax and Welfare: The ‘Project Insight’ rolled out by the Indian govt. used data mining techniques to counter tax evasion in 2017. It also helped in tracking down deregistered firms and gathered information about black money potholes in existence.

2)      National Security:  The uncertainties faced by national security officers with regards to the unpredictable security situation can be overcome by the use of analytics thus enabling them to combat crime attacks easily.

3)      Healthcare: Healthcare system in India has the opportunity to leverage big data analytics on the data emanating from biometric, patient records and thus provide actionable insights with greater prediction power contributing to effective public health.

4)      Education:  Ranking second in terms of student enrollment,  the titanic amount of student data can be analyzed to predict statistical figures and would help in efficient budget allocation.

In addition to these, analytics has also entered the farming sector where the concept of geo-tagging the entire agriculture infrastructure was implemented. Although the entire process is still in its infancy, the outcomes that big data analytics present to the Indian economy are much more effective. Though big data analytics have not been used in policymaking yet, the budget allocation hints at a widespread adoption of artificial intelligence and big data analytics in the Indian economy.

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Big Data in Aviation Industry

Fifteen years ago it was only fair for the airlines companies to keep record of the ingoing and outgoing flights only. But now let’s imagine a sudden storm hitting the East Coast today. This would imply that several flights will be delayed. Hence keeping up to their standards and adding a value to the every penny the passengers have paid would involve several entailing jobs like determining the flights to be connected with the airline, baggage transfer time, the number of transferring passengers, the flights that are coming from and so on. It naturally means a proliferating amount of big data sifting and shifting from a constellation of different sources. A Boeing 787 alone creates a half a terabyte of data every day. Big data in aviation are useful in many cases such as:

·         Fuel Efficiency- Fuel is the second highest expense for airlines and estimating power has developed to a point where airlines can congregate and process the enormous amounts of data they need to analyze on a per-trip basis. It is hoped that data mining will produce actionable intelligence around decisions such as adding or subtracting flights to routes, setting fuel loads for each aircraft, and selling additional passenger tickets. 

·         Smart Maintenance- The big data, including mechanic write-ups, shop findings and in-flight measurements, helps the airline company to plan equipment maintenance with minimal disturbance to flights.

·         Airline Safety- A data collection and analysis program named Data4Safety has been recently launched by The European Aviation Safety Agency (EASA)  to detect risks using a amalgamation of safety reports, in-flight telemetry data, air traffic surveillance information, weather data and so on.

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Predictive Automotive Analysis

The use of predictive analysis is widespread including in connecting car industry which provides both opportunities and challenges for the automotive industry. By using Telematics and infotainment systems, connected cars increasingly stream data into the cloud. Each connected vehicle is expected to generate more than 25 gigabytes per hour as the dizzying array of smart IoT sensors are coming into the picture. Predictive analysis in automotive industries is enabling connected cars to stay more on roads rather than in shops. Some of the ways in which predictive automotive data analysis is driving the growth of connected car industry are listed as follows:

·         Predictive Maintenance-Predictive data analysis can spot maintenance issues before they occur by leveraging data from warranty repairs with existing vehicle sensor data. This is done by pulling in data from virtually every vehicle, comparing the information with warranty repair trends and finding meaningful correlation which is otherwise impossible to be discovered by humans.

·         Predictive Collision Prevention-By utilizing big and fast data, latest sensors and car-to-car connectivity, predictive analytics technology may completely eliminate the possibility of accidents in the future.

·         Connected Car Cyber Security- Predictive analytics is efficient in securing connected cars in the sense that it is able to identify patterns of attackers’ behavior. It not only looks for an intruder to repeat the same behavior as pervious attackers but also searches for a combination of behaviors that are inconsistent with what would be expected of an authorized user.

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The two hands of the businesses: big data science and analytics

Big data and analytics have been the biggest contributor in the recent years. By the end of 2020, the big data volume is going to reach 44 trillion gigabytes. Data analytics provides many innovative solutions for insurance, FMCG, retail and financial services. AI and machine learning helps in predictive analysis and helps in making accurate predictions for the growth of the business. Big data science and analytics have advantages of speed and compiling big volumes.

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On demand start-ups: failure or success? On demand start-ups: failure or success?

Technology has helped develop such apps which provides services at the door right from cabs to groceries. Provision of other services like plumber, electrician and other maintenance services still are unavailable on apps. Providing these services is not a good option because if we see the calculation then the overall costs is more than the money paid by the customer. To break-even the customer should use their services at least 10 times to average the price to 44 Rupees. There are other factors too like loyalty of customers and discounts which is definitely not sustainable for the company. This market is unorganised sector in India and has a lot opportunities. Customers complained about quality of services and prices and hence this needs to be rectified.

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Top 5 languages to learn for ML

The power of machine learning is growing exponentially. Almost no industry domain is remaining untouched with the wonders and powers of machine learning.  Machine learning is just an application of artificial intelligence whose algorithms helps to analyze the historic experience without being explicitly programmed to predict the future affairs. Before jumping into the world of machine learning, it’s important to know which languages are being used to analyze the data and predict the future. Here are those 5 languages which are being using for machine learning: 

1. Python

2. R Programming


4. Prolog

5. javaScript

why and how are these languages being used for machine learning? For detailed information,



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Data management over cloud

Although cloud storage has benefited the businesses in intelligence enterprises but still it shouldn’t be trusted blindly. Cloud storage techniques have helped digital data storage and the changes are remarkable.

The 2 challenges that are connected with cloud storage, cloud lock-in and management complexity are:

• Cloud lock-in: Failure to transfer data from one cloud storage facility to other.

• Management complexity: The inability to perform proper management of available storage environments.

Following are the solutions to these problems that may be seen as unsolvable.

1. Gateway device: It behaves as an agent between the on-premise storage and the cloud storage.

2. Hybrid cloud: With the help of hybrid cloud, cloud storage acts as an extension to on-premise data storage.

3. Multicolored controller: It permits the data to be seen at the same time.

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Economic productivity and effects of Artificial Intelligence

The economic productivity of the country should rise up if there are improvements in technology and artificial intelligence but this is not the case. When the industry employs AI then this lead to more leisure on people’s part and making them useless. According to a survey it has been found that there is a rise in inequality in wages and hence AI needs to be implemented. There should be greater productivity gains but it is dipping. The reason could be slow commercialization. Though it displaces labour but it creates jobs in other sectors. Automation does not replace jobs but it does a part of the jobs.

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How revolution in cloud computing pushes surprising business growth?

Nowadays businesses have opened new gates for certain innovations using cloud computing technology with a proper and selective approach. Most of the IT executives are now entirely focused on how to make cloud a way to achieve their business goals and their entire focus is on cost optimization instead of cost management. In terms of transformation, the cloud has been a central to many organizations. Big data technology has allowed storing and recapturing of the vast amount of information. Regardless of any sector, most of the organizations have transferred all their data to the cloud. But still, most of the executive’s are reluctant to adopt cloud due to security concerns.

Businesses can achieve maximum growth only by accessing, controlling and analyzing all flaws present in the cloud network. That is why multi cloud is more preferred.

Due to digital transformation most of the companies have enforced cloud services to become more profitable.

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