Dexlab, Author at DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

How Big Data Will Impact E-commerce Industry in 2018?

Posted on March 15, 2018May 23, 2020 by Dexlab

Whatever happens online and offline, it’s because of DATA. As the technology evolves, the ways to gather and measure data also diversifies. The best way to grasp the data world mechanisms is to study and analyze trends in behavior.

Big data is a concentrated accumulation of conventional and digital data from within and outside company operations. The inception of big data has enabled businesses to use huge amounts of data to carry out bigger and more complicated analyses.

However, the pressing issue that people face today is that they have “too much” data – collecting, organizing and understanding data has become quite complicated because we now are inundated with ceaseless numbers, percentages, stats, facts and perceptions.

To be precise, for years, Big Data has been buzzing around the digital front – let’s delve into what it actually means and what promises it holds in 2018 for ecommerce…

E-commerce industries are the biggest consumers of data. They can extract any information from Big Data and predict customer behavior and streamline robust operations.

Here are 4 ways in which big data will change the shape of e-commerce in 2018:

Better shopper analysis

For online success, understanding shopper’s behavior is more than important. Harness big data; it offers information on trends, customer choices and spikes in demands. It is the key to successful marketing.

Throughout this year and more, big data analytics will continue tracking shopper behaviors and fine-tuning your marketing strategies based on that.

Flawless customer service

Figures regarding dissatisfied customers and frail customer service are alarming. Truly speaking, more than 90% of unhappy customers won’t like to do business with a company that has turned their expectations down, owing to poor customer service. Therefore, for ecommerce success, utmost focus on customer service is downright important.

This year, expect data analytics to improve customer experience, while giving more focus to predictive monitoring. This will aid companies in identifying crucial issues and resolve them before even a customer gets involved.

More secure and easy online payment options

Since big data came into our lives, several things, like online payments got easier and more secure. How?

Big data incorporates various payment functions in a single centralized platform. It helps in making the process easier, as well as reduces fraud risks.
The advanced analytics are powerful enough to identify threats and structure proactive solutions to combat potent risks.
Big data helps in detecting money laundering transactions.
Productive data analytics allows e-commerce chains to cross sell and upsell.

Mobile commerce evolution

Day by day, the use of smartphones is increasing. The use of desktop computers is soon becoming obsolete. Big data is making impossible things possible, especially in the world of smartphones and ecommerce. Companies can now very easily gather data from multiple sources and analyze customer trends through mobile technology. Google has pioneered a wave of technologies, giving preference to mobile friendly and highly responsive sites. They bring in higher traffic to their pages. Hence an instant hit!

As closing thoughts, ecommerce companies wholeheartedly thanks Big Data for the way it has simplified the process of online shopping. For more big data inspiration and blogs, follow DexLab Analytics.

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The article has been sourced from – http://dataconomy.com/2018/02/5-ways-big-data-analytics-will-impact-e-commerce-2018/

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How Algorithms Shape Public Discourse and Opinions?

Posted on March 14, 2018May 23, 2020 by Dexlab

The rapid evolution of today’s communication mediums has brought about a radical change in how public opinions are framed and public discourse is conducted. In general, the conventional boundary between public and personal communication has somewhat disappeared.

Incredible global platforms, like Google allow us all gain access to information in a blink of an eye. In order to do so, they use computer algorithms that weigh “relevance”, but sometimes the standards do not correspond to the expectation of the users.

Custom-fit Relevance

Algorithms function distinctly and descriptively. For an instance, the technology alters “relevance” for a user based on what links he or she has clicked in the past. But still, many users think the results are normative (‘higher’ up in Google results). In several cases, Google’s algorithms determine a massive divergence between content quality and “relevance”.

Not only that, owing to their encompassing database, Google and Facebook play a strong role in forming public opinions. 57% of German internet users get access to information about social affairs and politics through Google and other social networks. The researchers from Hamburg-based Hans Bredow Institute quoted in 2016, “the formation of public opinion is no longer conceivable without intermediaries, such as Google and Facebook”.

Keep Engaged

The design elements that Google and other intermediaries use are leading to a structural change in public discourse. Today, publishing is a piece of cake. Anyone can publish anything on the web, but everyone might not find an audience – for the latter, decision-making algorithms are needed. They garner the needed attention. They also determine the relevance of each content piece that goes through various social networks, like Facebook and filter the items that should be displayed for each user. In making an individual’s feed attractive, Facebook runs a detailed analysis and determines which content the user and his or her friends’ likes or prefers to hide. Both signals are important to perform a fairly straightforward analysis.

Moreover, Facebook deploys signals that users have no idea about, such as the amount of time they take to look into a single entry in the feed. In other areas too, algorithmic decision-making plays a crucial role, like offering help in legal matters or assessing where and when the police officers are on duty.

Diversity Rules

To guarantee a diversity of media in the public, make sure the algorithmic decision-making processes that determine relevance are diverse in the same manner. The digital discourse is supported by the robust algorithms that constantly ranks and personalize content.

To instill transparency to algorithmic decision-making methodologies, follow the steps below:

Back external researchers, and open platforms and their impacts
Support diversity among algorithms
Develop and maintain a strong code of ethics among developers
Educate users about the importance of the mechanisms used to influence public opinions

Coupled with strong industry self-regulation and legislative measures, a true and impartial notion of social and political influences on algorithmic ranking is established, which carries the potential to discover and combat dangers early on.

Are you still craving for more information on algorithms? Yes? Then satiate your data hunger with Machine Learning Using Python courses from DexLab Analytics. Arm yourself with a Machine Learning course online, and hit the notes of success through life!

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The role of Big Data Analytics in the World of Media and Entertainment

Posted on March 13, 2018May 23, 2020 by Dexlab

A reverberating revolution is on the go in the media industry. Reason: thorough digitization and data-driven marketing.

A seamless amalgamation between digital and analytical solutions is transforming versatile media platforms across the globe. Not only does it help in curating more personalized content for its niche audiences, but also bolsters newer capabilities, such as master data management for digital assets and improved customer engagement programs.

Is Big Data Big Enough?

Facebook gathers and processes more than 500 TB of data every day.

Google processes 3.5 billion requests every day.

Amazon records 152 million customer purchase data every day.

With the rise of digitization, media and entertainment companies are leveraging big data technology like no other for better customer engagement.

Here are a few examples showing how media companies are using the power of big data:

In predicting audience preference

Large chunks of data helps in predicting and understanding the demand of audiences – right from the genre of shows and music they like to content selection for a given age group or for different channels.

Better acquisition and retention

Any day, big data help to fathom the reasons why consumers subscribe or unsubscribe a particular channel. It aids companies in developing robust promotional and product strategies to attract and retain more loyal user base. Social media data also lend a helping hand to enhance consumer interest.

Content revenue generation and new product development

With the power of accurate and productive data, media houses incentivize consumer behavior and while doing so, they understand the true market value of the content generated.

How Media Uses Data Anaytics?

Netflix

Netflix assimilates large chunks of viewership data, with which it performs an in-depth analysis of viewer’s behavior for millions of viewings of shows. The analysts conduct thorough research on the attributes and qualities of data about consumers to know which show is the most popular. This analysis also helps them know how long viewers are watching a program or season or any individual show. Hence, in this way it outbids its competitors and owns rights to showcase blockbuster hits.

Bollywood

Talking about our very own Bollywood, SRK’s Chennai Express used big data and analytics to boost social media presence and digital marketing endeavors. And, no wonder, it smashed the box office records of 2013. It became such a raging success that the IT services company Persistent Systems released a statement saying, “Chennai Express related tweets generated over 1 billion cumulative impressions and the total number of tweets across all hashtags was over 750 thousand over the 90-day campaign period.”

This is a single instance. Many other bigwig producers have time and again collaborated with cutting edge big data analytics firms to better understand consumer trends and drive customer engagement.

Conclusion

Big Data is a surefire boon for media and entertainment houses; it helps companies to solve crucial questions about consumers, things they like, content they feed in, and shows they treasure. Moreover, it aids in tracking clicks, shares and views across multiple devices and media platforms.

To lead the change, join DexLab Analytics. Their Data analyst course in Delhi NCR is surely worth taking up to fulfill all your divine career aspirations. Check out business analytics course itinerary now.

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Posted on March 12, 2018December 17, 2018 by Dexlab

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.

Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

To grasp emerging trends, newer solutions, robust techniques and real-life case studies, take up Machine Learning Using Python courses from DexLab Analytics. Their Machine Learning Training Gurgaon simply gives an out of the world experience, thus need to be tried on.

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5 Ways to Grab the Hottest Job in Town

Posted on March 10, 2018May 23, 2020 by Dexlab

Even though it’s expected that the unemployment rate will follow a steep decline, yet finding the right job can still be challenging enough. On the other hand, hiring the right candidate is also an equally difficult task.

At times, it may seem your job search not working. You are sending resume after resume for matching job positions, but nothing fruitful is coming out. Then you start giving it a thought, why is this happening? How come, others are getting new gigs and you are stuck in the same mundane job or still looking for one!

In some cases, you might need to learn new skills or receive any particular kind of training, but in a majority of cases there are plenty of normal things that you can do to boost your candidacy. Some are right here, please take a look:

Make your LinkedIn Profile attractive as much as possible

Internet is everything now. Probably, you have been busy devoting a lot of time on building a powerful resume, but instead you should create a good, powerful LinkedIn profile with updated information. Having an optimized profile helps you gain the interest of notable recruiters who are in search for people like you. Include a summary, a headline, a photo and specializations, so that it pleases anyone who visits your profile.

Be a complete standout

A promising candidate should have all the attributes of being competent, adaptable, flexible, collaborative and influential. Flexibility is the key to success and a fuller career.

Do your homework before interview

Culture fit is essential. A certain skillset can be taught, but the zeal to do homework on the company you are going for an interview cannot be taught. That you have to develop on your own. Recruiters appreciate people who do homework on the respective company and frame answers about how they can be an asset to the company. It’s always advisable to do your homework. Applicants whose answers are down to earth, authentic and show passion stands out in any interview.

Go get a personal website

The best way to impress your employers or recruiters is by having a personal website – with just one URL, you can allow hiring managers derive a whole lot of information about yourself and your work. And if you are looking forward to change your career path, you can flaunt your new passion in a great way on your personal platform.

Tap into your network

Every company receives a lot of internal referrals for recruiting and some of them are quite successful. Through this procedure, they make it sure that they find the right candidates for designated job positions. Hence, networking matters – let your network know what kind of job you are looking for. You never know which conversation will lead you where – because here every interaction is seen as an opportunity. So, just don’t waste it in vain.

Today, jobs in the field of data science and big data are flourishing. More and more interested candidates are getting trained to join the bandwagon of data analytics. DexLab Analytics is a premium data science learning platform that offers in-depth training on all major data related in-demand courses. Their data science training online is excellent. Get all the details from the website.

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

Posted on March 9, 2018May 23, 2020 by Dexlab

This blog is a continuation of the previous blog that talked about how data science is improving retail – through cutting edge machine learning training models. For sure, retail and ecommerce churns out a humongous amount of data, but with no proper tool of analysis, the vast pool of data lies unutilized, unearthed and you end up knowing what you already know.

In this blog, we will delve into a few common uses of data science in retail – which demands absolute attention, before we start automating procedures.

Product Recommendations

In a traditional shop setup, retailers would have consultants who would understand customer requirements and use their own judgments to help them find a suitable product. In an online scenario, this would be based on past performance and revenue generation and the sole aim would be to pitch the highest selling product. Not only historical purchases and recent online activity, but consumer’s social media and online sharing would give more idea about their interests, preferences and designer they like.

Sometimes, we also come across ghost clients – clients about whom we have no information, in fact, we don’t even know from where they are browsing. In this case, your recommendations would be based on intuition and might not be 100% accurate. The deal becomes trickier here. On the other hand, there are clients about whom who know everything – and thus tailor our offerings.

Product Assortment

No wonder, we have to keep products to satisfy our niche customers, but on a wider scale, we have to introspect what stuffs to keep in stock. A proper analysis of our product demands and the kinds of products our clients swear by, we can ascertain what items to restock again and again. Also, we can take a cue or inspiration from our vying competitors, as they are a good source of information for a perfect assortment of products you want to include. A full account of their inventory will enlighten you about a few blind spots you had, and devise how to correct them before it’s too late.

Pricing

The people will pay whatever rates the market supports. The price of the product is still subject to change, depending on the country of origin, taste and preferences and market scenario. But these are more of a supply side changes, so what about the demand side? Interested customers are keen to buy products even at varying prices, but the products should be truly good enough. The scale also plays an important role in deciding prices. The best pricing decisions take into account data regarding weather, day of purchase, several economic factors, location and more.

Customized branding/marketing

It is mostly curated for large retailers. For example, how about some doing some routine advertising – it applies to both digital and offline branding, though much easier for digital. A monthly newsletter carrying all the needful information about discounts, new product launch and promotions always will keep your customers’ updated about everything that’s going around the company. But, make sure they have some sort of personal touch – personalized marketing helps!

Summary

While the sky is the limit for data science, the blog above sheds light on the benefits of data science and the true impact of having trust on data. After all, it is of no use to keep data and not take advantage of it!

DexLab Analytics offers premium data science training in Delhi NCR for affordable rates. Try their courses today!

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

Posted on March 8, 2018May 23, 2020 by Dexlab

The mammoth growth in ecommerce signifies an entire paradigm shift in retail sector. Figures say, ecommerce accounts for $2 trillion dollars in sales and more. Though traversing through both the offline and online market seems a rather challenging task, but when we finally concentrate on each customer and their purchasing manner, it feels easier to break up the analysis into a few different paths.

In this blog, we will take into account a few interesting ways, in which data science increases your sales, online and offline, alike. But before that, understand whom are you selling your products? Hoarding information about your clients is crucial, and of course there are many ways to do this. Amazon is one of the biggest examples of this. They predict future purchases of customers, based on the past behavior. Companies lose valuable customers if they don’t look at the data with a wider scope and search for insights. But Amazon is definitely not one of them, and their technique is clearly working for them with over $2 billion profits made last year.

The Mechanism Behind

At Amazon, products are shipped even before customers have ordered them. This means, when the products are shipped, there’s no one to receive them. But, does it really matter! The main logic behind such steps is that once the products are taken out of the warehouse and transported to a particular area, they can easily be marketed to other dealers at discounted rates or kept inside the final hub. This is more like a logistic marvel than an ecommerce miracle – but it definitely makes us believe in the concept of forward thinking to lead the change.

The working principle in here is the most innovative concept of machine learning that helps in predicting future client behavior pattern. It works on data to train a formidable model. Training is a notable process of pouring data into the model so that it can employ statistical weights to automatically identify future purchase trends. For example, Mr. A purchases a new item every two or three weeks, so it’s expected that he will make a purchase within that time limit. For this, we don’t have to use data, but just divide it into train and test data. However, this is a very simple example – in reality these trends are juxtaposed with other millions of clients to differentiate clients into numerous cohorts that overlap and vary. Machine learning techniques are used in a plethora of different use cases, like product recommendations, churn predictions, logistics planning and automatic personalized marketing. We will discuss deeply about them in our next blog section.

For Machine Learning courses in Delhi NCR, drop by DexLab Analytics.

Make Flexibility Your Bae

While working on data science, it is important to focus on flexibility – the whole structure of data warehouse will start changing once you start trying something new. At times it may seem to be amusing, but on the long run, you will come across several significant insights.

With all these on point, scoring high on retail is no more a distant dream. Data science and machine learning methods have made everything so easy, and so manageable. To give a robust push to your career in data science, take up data science online training from DexLab Analytics. Apart from data science, they also offer excellent Machine Learning Certification for all data-hungry candidates – go take a look at their course structure.

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How Big Data is Influencing the Sports Industry?

Posted on March 6, 2018May 23, 2020 by Dexlab

Imagine you have stepped off the field, and your team has lost. Obviously you can look at it as a failure, but if you closely introspect, you could look at it as an opportunity to improve. Why? Because of those embedded sensors in your jersey that are tracking your every move, and proper data analysis of that data will help you get better in the next game.

Yes, you heard it right – data plays a key role in the world of sports. Collected data now guides team towards victory. Whether you are a cricket fanatic or a rugby supporter, you will find several instances that would show how big data is influencing this mega industry. Today, the professional sports industry stands at a whooping value of $90 billion – just as other industry domains are utilizing the power of big data and putting it to good use, why should sports industry lag behind? They also are looking for ways to enhance their athlete’s performance and improve organizations’ and fans’ total experience. Continue reading “How Big Data is Influencing the Sports Industry?”

If Big Data is the Problem, Then Hadoop is the Solution

Posted on March 5, 2018February 15, 2019 by Dexlab

A lot of IT professionals and tech nerds are curious to learn about the difference between Big Data and Hadoop. A majority of them are yet to understand the subtle line of distinction between the two. And the increasing prominence and popularity of Big Data Hadoop certification has further added to the confusion.

Importantly, Big Data and Hadoop, the most popular open-source Hadoop program actually ends up complementing each other, in every way. If you think of Big Data as a problem then Hadoop acts like a solution for that problem – yes, they are that much compatible and complementary to each other. While big data is a dubious and complex concept, Hadoop being a simple, open source program that helps in fulfilling a certain creed of objectives of asset, in this case Big Data.

The best way to explain this issue would be by talking about the challenges associated with Big Data and how Hadoop efficiently resolves them – this would be the best way to know the differences between the two.

Challenges with Big Data

Big Data is best defined with 5 characteristics: Volume, Variety, Velocity, Value and Veracity. Here, volume depicts the quantity of data, variety means the kind of data, velocity is the rate at which data is being generated, value points at the usefulness of the data and veracity is the amount of inconsistent data.

Now, let’s talk about two of the emerging problems with Big Data:

Storage – The archaic storage solutions are not adept enough to store such mammoth amount of data that is being generated every day. Moreover, the variety of data is different, thus the data needs to be stored separately for effective use.
Speed of accessing and processing data – Though the hard disk capacities have increased manifold, not much development has been done on the front of the speed of accessing or processing data.

But no more, you have to worry about all these issues, as Hadoop is here. It has effectively mitigated all the above-mentioned challenges and made big data powerful as a rock!

What is Hadoop?

Generally speaking, Hadoop is an open source programming platform – it helped big data to get stored in distributed environments so as to be processed in a parallel way. It is composed of two important elements – Hadoop Distributed File System (HDFS) and YARN (Yet Another Resource Negotiator), Hadoop’s processing unit.

Now, let’s see how Hadoop resolves the emerging big data challenges:

Storage – With the help of HDFS, Big Data can now be stored in a proper distributed manner. For that, datanode block is used, it’s an efficient storage solution and allows you to specify the size of every block in use. Additionally, it doesn’t only divides the data across different blocks but also replicated all the blocks on the data nodes, thus making way for better storage solution.
The speed of accessing and processing data – Instead of relying on traditional methodologies, Hadoop prefers moving processing to the data, which means the processing dynamo is moved across different slave nodes and parallel processing of data is carried on throughout the slave nodes. And the processed results are then shifted into a master node, where a mixing of data takes place and the response arising out of it is sent to the client.

Hence, you can see how big data and hadoop are related to each other, not like alternatives but like complements. So, to climb the ladder of success and be an ace developer or data scientist, Big Data Hadoop certification in Gurgaon is your go-to option. Get Big Data Hadoop certification today from DexLab Analytics.

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Better shopper analysis

Flawless customer service

More secure and easy online payment options

Mobile commerce evolution

Interested in a career in Data Analyst?

Custom-fit Relevance

Keep Engaged

Diversity Rules

Interested in a career in Data Analyst?

Is Big Data Big Enough?

Facebook gathers and processes more than 500 TB of data every day.

Google processes 3.5 billion requests every day.

Amazon records 152 million customer purchase data every day.

In predicting audience preference

Better acquisition and retention

Content revenue generation and new product development

How Media Uses Data Anaytics?

Netflix

Bollywood

Conclusion

Interested in a career in Data Analyst?

AI: An Investment Ally

Get Deeper

Tracking Investor Trends Helps

Never stop evolving

Interested in a career in Data Analyst?

Make your LinkedIn Profile attractive as much as possible

Be a complete standout

Do your homework before interview

Go get a personal website

Tap into your network

Interested in a career in Data Analyst?

Product Recommendations

Product Assortment

Pricing

Customized branding/marketing

Summary

Interested in a career in Data Analyst?

The Mechanism Behind

Make Flexibility Your Bae

Interested in a career in Data Analyst?

Challenges with Big Data

Now, let’s talk about two of the emerging problems with Big Data:

What is Hadoop?

Now, let’s see how Hadoop resolves the emerging big data challenges:

Interested in a career in Data Analyst?

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