Improving Logistics & Transportation Performance with Big Data

| November 6, 2017

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DynPro, Inc

Trust Experienced, Proven IT Professionals with Your Business and IT Challenges! Founded in 1996, DynPro is a voice of experience in world-class IT Solutions. Because we are firmly rooted in technology, our expert IT professionals deliver tailored IT solutions to solve your challenges. Our solution experience ranges from Software Design & Development, Managed Services, IT Consulting and IT Staffing.

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A learning guide to accelerate data analysis with SPSS Statistics

Article | May 20, 2021

IBM SPSS Statistics provides a powerful suite of data analytics tools which allows you to quickly analyze your data with a simple point-and-click interface and enables you to extract critical insights with ease. During these times of rapid change that demand agility, it is imperative to embrace data driven decision-making to improve business outcomes. Organizations of all kinds have relied on IBM SPSS Statistics for decades to help solve a wide range of business and research problems.

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Will Quantum Computers Make Supercomputers Obsolete in the Field of High Performance Computing?

Article | May 12, 2021

If you want an explicit answer without having to know the extra details, then here it is: Yes, there is a possibility that quantum computers can replace supercomputers in the field of high performance computing, under certain conditions. Now, if you want to know how and why this scenario is a possibility and what those conditions are, I’d encourage you to peruse the rest of this article. To start, we will run through some very simple definitions. Definitions If you work in the IT sector, you probably would have heard of the terms ‘high performance computing’, ‘supercomputers’ and ‘quantum computers’ many times. These words are thrown around quite often nowadays, especially in the area of data science and artificial intelligence. Perhaps you would have deduced their meanings from their context of use, but you may not have gotten the opportunity to explicitly sit down and do the required research on what they are and why they are used. Therefore, it is a good idea to go through their definitions, so that you have a better understanding of each concept. High Performance Computing: It is the process of carrying out complex calculations and computations on data at a very high speed. It is much faster than regular computing. Supercomputer: It is a type of computer that is used to efficiently perform powerful and quick computations. Quantum Computing: It is a type of computer that makes use of quantum mechanics’ concepts like entanglement and superposition, in order to carry out powerful computations. Now that you’ve gotten the gist of these concepts, let’s dive in a little more to get a wider scope of how they are implemented throughout the world. Background High performance computing is a thriving area in the sector of information technology, and rightly so, due to the rapid surge in the amount of data that is produced, stored, and processed every second. Over the last few decades, data has become increasingly significant to large corporations, small businesses, and individuals, as a result of its tremendous potential in their growth and profit. By properly analysing data, it is possible to make beneficial predictions and determine optimal strategies. The challenge is that there are huge amounts of data being generated every day. If traditional computers are used to manage and compute all of this data, the outcome would take an irrationally long time to be produced. Massive amounts of resources like time, computational power, and expenses would also be required in order to effectuate such computations. Supercomputers were therefore introduced into the field of technology to tackle this issue. These computers facilitate the computation of huge quantities of data at much higher speeds than a regular computer. They are a great investment for businesses that require data to be processed often and in large amounts at a time. The main advantage of supercomputers is that they can do what regular computers need to do, but much more quickly and efficiently. They have an overall high level of performance. Till date, they have been applied in the following domains: • Nuclear Weapon Design • Cryptography • Medical Diagnosis • Weather Forecasting • Online Gaming • Study of Subatomic Particles • Tackling the COVID-19 Pandemic Quantum computers, on the other hand, use a completely different principle when functioning. Unlike regular computers that use bits as the smallest units of data, quantum computers generate and manipulate ‘qubits’ or ‘quantum bits’, which are subatomic particles like electrons or photons. These qubits have two interesting quantum properties which allow them to powerfully compute data – • Superposition: Qubits, like regular computer bits, can be in a state of 1 or 0. However, they also have the ability to be in both states of 1 and 0 simultaneously. This combined state allows quantum computers to calculate a large number of possible outcomes, all at once. When the final outcome is determined, the qubits fall back into a state of either 1 or 0. This property iscalled superposition. • Entanglement: Pairs of qubits can exist in such a way that two members of a pair of qubits exist in a single quantum state. In such a situation, changing the state of one of the qubits can instantly change the state of the other qubit. This property is called entanglement. Their most promising applications so far include: • Cybersecurity • Cryptography • Drug Designing • Financial Modelling • Weather Forecasting • Artificial Intelligence • Workforce Management Despite their distinct features, both supercomputers and quantum computers are immensely capable of providing users with strong computing facilities. The question is, how do we know which type of system would be the best for high performance computing? A Comparison High performance computing requires robust machines that can deal with large amounts of data - This involves the collection, storage, manipulation, computation, and exchange of data in order to derive insights that are beneficial to the user. Supercomputers have successfully been used so far for such operations. When the concept of a quantum computer first came about, it caused quite a revolution within the scientific community. People recognised its innumerable and widespread abilities, and began working on ways to convert this theoretical innovation into a realistic breakthrough. What makes a quantum computer so different from a supercomputer? Let’s have a look at Table 1.1 below. From the table, we can draw the following conclusions about supercomputers and quantum computers - 1. Supercomputers have been around for a longer duration of time, and are therefore more advanced. Quantum computers are relatively new and still require a great depth of research to sufficiently comprehend their working and develop a sustainable system. 2. Supercomputers are easier to provide inputs to, while quantum computers need a different input mechanism. 3. Supercomputers are fast, but quantum computers are much faster. 4. Supercomputers and quantum computers have some similar applications. 5. Quantum computers can be perceived as extremely powerful and highly advanced supercomputers. Thus, we find that while supercomputers surpass quantum computers in terms of development and span of existence, quantum computers are comparatively much better in terms of capability and performance. The Verdict We have seen what supercomputers and quantum computers are, and how they can be applied in real-world scenarios, particularly in the field of high performance computing. We have also gone through their differences and made significant observations in this regard. We find that although supercomputers have been working great so far, and they continue to provide substantial provisions to researchers, organisations, and individuals who require intense computational power for the quick processing of enormous amounts of data, quantum computers have the potential to perform much better and provide faster and much more adequate results. Thus, quantum computers can potentially make supercomputers obsolete, especially in the field of high performance computing, if and only if researchers are able to come up with a way to make the development, deployment, and maintenance of these computers scalable, feasible, and optimal for consumers.

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Predictive analytics vs AI Why the difference matters

Article | February 10, 2020

There are few movie scenes I can recall from my childhood, but I vividly remember seeing the 1968 Stanley Kubrick sci-fi movie 2001 A Space Odyssey in 1970 with my older cousin. What stays with me to this day is the scene where astronaut Dave asks HAL, the homicidal computer based on artificial intelligence (AI), to open the pod bay doors. HAL's eerie reply: I'm sorry, Dave. I'm afraid I can't do that.In that moment, the concept of man vs. machine was created, predicated on the idea that machines created by man and using AI could (eventually) defy orders, position themselves in the vanguard, and overthrow humankind. Fast forward to today. Within the information governance space, there are two terms that have been used quite frequently in recent years analytics and AI. Often they are used interchangeably and are practically synonymous.

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5 Predictive Data Analytics Applications

Article | May 31, 2021

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more. Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years. Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.” In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting. Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below: • With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights • Better computers • An abundance of easy to use software • Need of competitive differentiation due to tougher economic conditions As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting. Benefits of Predictive Analytics Competitive edge over other businesses The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers. Business growth Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs. Customer satisfaction Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones. Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers. Personalized services Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers. Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.   Application of Predictive Analytics Customer targeting Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does. The major constituents that create these customer groups are: • Socio-demographic factors: age, gender, education, and marital status • Engagement factors: recent interaction, frequency, spending habits, etc. • Past campaign response: contact response, type, day, month, etc. The customer-specific targeting for the company is highly advantageous. They can: • Better communicate with the customers • Save money on marketing • Increase profits Customer churn prevention Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning. Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout. Factors that can influence the churn are as follows: • Customer variables • Service use • Engagement • Technicalities • Competitor variables Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products. Risk management Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated. With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not. Risk assessment analyzes the following data types: • Socio-demographic factors • Product details • Customer behavior • Risk metrics Forecast sales Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting. Predictive models help in anticipating a customer’s reaction to the factors that affect sales. Following factors can be used in sales forecasting: • Calendar data • Weather data • Company data • Social data • Demand data Sales forecasting allows revenue prediction and optimal resource allocation. Healthcare Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management. Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients. Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly. Frequently Asked Questions What is predictive analytics? Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes. How to do predictive analytics? • Define business objectives • Collect relevant data available from resources • Improve on collected data by data cleaning methods • Choose a model or build your own to test data • Evaluate and validate the predictive model to ensure How does predictive analytics work for business? Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations. What tools are used for predictive analytics? Some tools used for predictive analytics are: • SAS Advanced Analytics • Oracle DataScience • IBM SPSS Statistics • SAP Predictive Analytics • Q Research { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics uses historical data to predict future events. The historical data is used to build a mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes." } },{ "@type": "Question", "name": "How to do predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Define business objectives Collect relevant data available from resources Improve on collected data by data cleaning methods Choose a model or build your own to test data Evaluate and validate the predictive model to ensure " } },{ "@type": "Question", "name": "How does predictive analytics work for business?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations." } },{ "@type": "Question", "name": "What tools are used for predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Some tools used for predictive analytics are: SAS Advanced Analytics Oracle DataScience IBM SPSS Statistics SAP Predictive Analytics Q Research" } }] }

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Spotlight

DynPro, Inc

Trust Experienced, Proven IT Professionals with Your Business and IT Challenges! Founded in 1996, DynPro is a voice of experience in world-class IT Solutions. Because we are firmly rooted in technology, our expert IT professionals deliver tailored IT solutions to solve your challenges. Our solution experience ranges from Software Design & Development, Managed Services, IT Consulting and IT Staffing.

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