Friday 7 December 2018

5 Interesting Things About Refurbished Phones You Must Know

These days, high-end smartphones are getting more and more expensive, unless you wait for Black Friday sales or promotions. Even the latest iPhone XS Max is priced over £1400, which is quite a lot of money. This is the reason that so many smartphone users are opting for refurbished mobile phones to save some of their hard earned money.

As refurbished mobile phones are actually quite common these days, in this article let’s discuss some of the top interesting facts about refurbished phones. And don’t forget to read the 3rd fact. It will entice you to buy a refurbished phone right now. But first of all, let us understand the definition of a refurbished phone.

What is a Refurbished Phone?

Refurbished phones, commonly known as refurb phones are those phones which, after their functionality defect, are returned back to the manufacturer. The manufacturer then repairs these phones and send them back to the market for re-selling. You might also see refurbished phones labelled as ‘pre-owned or reconditioned’ phones. Most of the times there is nothing wrong with refurbished phones even before they get their repairing. The owner might send back the device to buy a new one.

When it comes to a new phone vs refurbished phone, there is not much difference except that the refurbished phones are carefully and lightly used before coming in your hands. In simple words, a refurbished phone is just like a new phone, though technically it is not a ‘brand new’ phone. That is why people like to buy refurbished phones because they are basically new. And you can get a refurbished phone at a discounted price as well.

Interesting Things About Refurbished Phones

Here are some of the interesting things about a Refurbished phone. After knowing these things, we are sure that you will never hesitate while buying a refurbished phone.

  1. Almost New Phones at a Lower Price

Saving money is one of the utmost desires of a common human being. And Refurbished phones fulfil this desire, as you can get an almost like new phone at a huge discounted price. These refurbished phones work like new smartphones. The discounted prices open new possibilities for you while selecting a new phone.

If a brand new phone costs you around 1000 pounds, the same phone of the same brand in a refurbished form will costs you 50% less or even more than original pricing. So, the ‘low cost’ factor is one of the most important things about refurbished phones.

  1. Refurbished Phone Stays Longer Than People Think

You should avoid buying phones with short lifespan. But as far as refurbished phones are concerned, you can buy them without any hesitation. Refurb phones work the same as new ones. But these phones are always repaired and tested by a skilled technician. When a phone is installed with replacement parts like a new battery, its lifecycle automatically increases and you get a chance to stick with your favourite phone a bit longer. That is why refurbished iPhone 5s is still common in mobile phone users.

  1. Refurbished Phone Comes With Extensive Guarantee

Because refurbished phones get repaired and tested by professionals, so they come up with a long guarantee. Normally the guarantee time period is between 6 to 12 months. For comparison: if you buy a refurbished iPhone from Apple, you will get a guarantee of maximum 3 months, and if you buy a refurbished phone from a trusted online mobile phone marketplace then you will get a guarantee of up to 12 months.

Alpha SmartPhones is one such company who provides 12-month long and extensive guarantee with its high quality refurbished phones. If mobile phone malfunctions in that time, you can return it without any hassle and get your cash back. And this condition applies to all mobile phones available at Alpha SmartPhones.

  1. Refurbished Phone – An Environment-Friendly Choice

Buying a refurbished phone is more environment-friendly gesture than buying a new phone. Millions of smartphones end up at landfills every year. These phones are continuously polluting our environment due to the toxic materials inside them. So it is better to recycle these phones instead of throwing them. Refurbished phones contain good condition parts of these recycled phones. Thus, reusing refurbished phones is a great service towards protecting the environment.

  1. Risk-Free Money Back Guarantee With Refurbished Phones

Usually, people get shy while buying a refurbished phone online for the first time. However, there is no such risk involved when you buy a refurbished phone from an online mobile phone marketplace. You can buy your favourite phone in a refurb form online with free home delivery. By the way, Alpha SmartPhones does provide such service to its customers with 30 days money back guarantee as well. So you can easily claim your money back if the mobile phone doesn’t meet your expectations.

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Guide to SME ERP Implementation Success

10 top data science and analytics education programs of 2018

These universities and institutes offer the best programs for professionals interested in a career in big data and analytics.

Data science and analytics

Data science and analytics

Data science and analytics- There’s never been a better time to begin a career in data science: By 2020 the number of annual job openings for all data-savvy professionals in the US will increase to 2.7 million, IBM predicted. And those with data science skills can command an average salary of $139,840 in the US, according to Glassdoor. The massive growth in data has changed the way enterprises operate, and data science has become crucial for making smart business decisions.

But how do you gain this coveted skillset? Analytics Insight Magazine has named the 10 best analytics and data science institutes of 2018, naming the top programs with experienced global faculties that offer real-world experience to their students, according to a press release.

“The featured institutions offer a comprehensive curriculum in Big Data and Data Science, delivered by top-class faculties along with extraordinary industry exposure,” Ashish Sukhadeve, founder and editor-in-chief of Analytics Insight, said in the release. “We congratulate all the ten institutes for providing world-class analytical education and building impactful data professionals.”

Here are 10 analytics and data science institutes that are at the forefront of education in the field.

1. Carnegie Mellon University’s Heinz College of Information Systems and Public Policy

Carnegie Mellon offers a Master’s program in Business Intelligence and Data Analytics (BIDA), and deploys the latest in analytical education, according to the release.

2. Cornell University

Cornell offers a unique flagship program in Master of Professional Studies in Applied Statistics (M.P.S.). It also offers undergraduate degrees in statistics and data science, as well as M.S and Ph.D. degrees in these fields.

3. Great Lakes Institute of Management

Great Lakes Institute of Management is a leading business school in India, which offers a one-year Post Graduate Program in Management (PGPM), a regular two-year Post Graduate Diploma in Management (PGDM), along with weekend and executive programs in analytics, for professionals who want to gain these skills while working.

4. International School of Engineering

The International School of Engineering (INSOFE) in India is an applied engineering school with a focus on data science and big data analytics education, according to the release. The school offers courses in big data analytics, teaching the latest machine learning and deep learning techniques to solve real-world problems.

5. New York University Stern School of Business

The NYU Stern School offers a Masters of Science in Business Analytics degree that aims to help executives understand the role of data in decision-making. The program focuses on domain-specific areas such as analytics strategy, marketing, and optimization, according to the release.

6. Penn State University

Penn State University offers Master in Data Analytics, preparing students to work in positions that require the design and maintenance of big data and data analytics systems with exposure to real-world datasets.

7. Praxis Business School

India’s Praxis Business School offers a one-year post-graduate program in data science with machine learning and artificial intelligence (AI) capabilities aimed at equipping students with the tools, techniques, and skills to enter the analytics field, the release said.

8. Saint Mary’s College, Notre Dame

Notre Dame offers a Master of Science in Data Science program provides students with a deep dive into the mathematical and computational skills needed to take on complex data challenges.

9. University of Chicago Graham School

The University of Chicago offers a Master of Science in Analytics (MScA) program to help students analyze complex datasets and and solve real-world problems, the release noted.

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Article Credit: TechRepublic

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Top five business analytics intelligence trends for 2019

From explainable AI to natural language humanising data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019.

Business analytics trends

Business analytics trends

Business analytics trends- Business analytics intelligence prediction number one: After the hype, the rise of explainable AI

AI promises to enhance human understanding by automating decision-making. But, as organisations rely on AI and machine learning for data-driven decision-making, we’re seeing a rise in human hesitation about the trustworthiness of model-driven recommendations. Many machine learning applications don’t offer a transparent way to see the algorithms or logic behind decisions and recommendations. This need for transparency will drive growth of explainable AI in 2019. If you can question humans, why not have the same option with machine learning when making decisions?

Business leaders will put greater pressure on data science teams to use models that are more explainable and reveal how models are constructed. AI has to be trusted to make the strongest impact, and the generated conclusions must be intelligible, simple and dynamically answer questions to help humans understand their data.

Explainable AI : The margins of accountability

How much can anyone trust a recommendation from an AI? Yaroslav Kuflinski, from Iflexion gives an explanation of explainable AI

Business analytics intelligence prediction number two: Natural language humanises data analytics

Natural language processing (NLP) helps computers understand the meaning of human language. BI vendors will incorporate natural language into their platforms, offering a natural language interface to visualisations. At the same time, natural language is evolving to support analytical conversation—defined as a human having a conversation with the system about their data. The system leverages context within the conversation to understand the user’s intent behind a query and further the dialogue, creating a more natural, conversational experience. That means when a person has a follow-up question of their data, they don’t have to rephrase the question to dig deeper or clarify an ambiguity. Natural language will be a paradigm shift in how people ask questions of their data. When people can interact with a visualisation as they would a person, it allows more people of all skill sets to ask deeper questions of their data. As natural language evolves within the BI industry, it will break down barriers to analytics adoption and help transform workplaces into data-driven, self-service operations.

Business analytics intelligence prediction number three: Actionable analytics put data into context

Data workers need to access their data and take action—all in the same workflow. In 2019, expect more organisations to use data analytics exactly where it’s needed and not in isolation. Organisations will truly reap the benefits of how BI platform vendors are offering capabilities like mobile analytics, embedded analytics, dashboard extensions, and APIs. Embedded analytics puts data and insights where people are already working so they don’t have to navigate to another application or shared server, while dashboard extensions bring access to other systems right into the dashboard. And mobile analytics put data directly into the hands of people in the field. These advancements are equally powerful as they meet the needs of different business teams and verticals by empowering new audiences with on-demand data in context.

Organisations across the globe lack data analytics maturity, says study

Harvard Business Review Analytic Services report reveals that only five per cent believe that their organisations are very effective at implementing modern data sharing, while 67% want to move towards that approach, showing that data sharing is very much on the agenda of organisations in the year ahead

Business analytics intelligence prediction number four: Enterprises get smarter about analytics

Business intelligence initiatives often have a well-defined start and end date and it’s not uncommon for them to be considered “complete” after they are rolled out to users. But merely providing access to business intelligence solutions isn’t the same as adoption. Chief data officers, primarily, are re-evaluating how BI adoption plays a part in a strategic shift towards modernisation, because true value isn’t measured by the solution you deploy, but how your workforce uses the solution to impact the business.  The assumption that everyone is getting value out of a BI platform just because they have access to it can actually be an inhibitor to real progress with analytics.

As these internal communities on-board workers onto a BI platform, organisations can start to delegate analytical responsibilities and create new user champions. This will ultimately reduce the heavy lifting for maintenance and reporting, traditionally reserved for IT.  More internal champions will start to emerge, acting as subject matter experts who socialise best practices and align people on data definitions. Inevitably, all of these movements will lead to more people using and getting value out of BI software. And most importantly, your workforce will become more efficient and your organisation more competitive.

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Article Credit: IA

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Making the most out of Customer Data Analytics

Customer Data Analytics

Customer Data Analytics

Customer Data Analytics- Being a successful entrepreneur isn’t easy. A viable idea, a profitable niche, a target demographic and something of value to sell are the prime requirements to run a business. Aside from these customer relations also ranks high on the list. A customer responds better to what is done for them. An entrepreneur should catch his/her customer base, their preferences and purchasing behavior. For attracting and retaining profitable customers a systematic examination of customer information is needed.

Customer data analytics is not just collecting numbers on product sale. It can lead to an accurate prediction about the future and provide actionable roadmaps for achieving desired goals. The process involves examining and capturing customer behavioral data. Descriptive analytics, Prescriptive analytics, and predictive analytics are three types of customer data analytics. The foremost one describes ‘what happened?’. This is the standard type of customer analytics, in which summarizing of raw data is done. They provide a clue into the past. Prescriptive analytics provide insight into a fruitful outcome. It is more like providing a solution. Predictive analysis try to answer the question ‘what could happen?’. It provides information on what can be expected in the future. Together it becomes the backbone for all marketing activities.

The key features of an effective customer insight are consumer research, data quality, database marketing, and analytics team. Customer’s perception of their behavior could not be neglected. Customer data management also is vital in data analytics. The predictive customer insight is proportional to the quality of data gathered. Database marketing is a robust way of testing hypothesis, a scientific method to know customer insight. A proficient team also is required for strategic marketing.

There are many benefits to using customer data analytics. Primarily they help in improving business operations. It is used extensively to create promotion and campaigns. Provision into a better understanding of the target group helps in reducing campaign cost by picking up customers who are likely to respond. In future, this customer data can provide insight in designing new products too.

Customers expect a faster customer service and instant gratification today. Relevant knowledge to address these pain points will nurture customer relationships which in effect will help in the business itself.

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Article Credit: CR

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