Blockchain technology is the single most polarizing tech innovation in the market right now. Many industries are still undecided on whether blockchain technology will be an asset that will unlock new levels of value OR a threat that could potentially send them into oblivion. The many applications of blockchain technology includes cryptocurrency, smart contracts, decentralized ledgers, and consensus protocols. Yet, beyond these, blockchain has the potential to disrupt many industries ranging from real estate, to shipping, and all the way to prescription drugs. This piece examines how blockchain technology could directly influence the turn of events in the big-data industry in the medium to long terms.
Big data is simply a large data set that is too voluminous to be managed by traditional data-processing software. When analysed computationally however, they can reveal interesting patterns, trends, and associations betraying underlying human behaviours.
Big data is big across many industries and the world is starting to pay special attention to the wisdom of the crowd by being patient enough to hear what the data is saying. Big data is one the industries that is set to experience first-hand, the disruptive power of blockchain technology. Below are three ways blockchain could influence the big data industry
1. More efficient data storage
The world is generating an unprecedented amount of data as the Internet of Things enables all kinds of devices record activity across different parameters. In 2017, IBM reported that about 2.5 quintillion bytes of data are generated every day as enterprise continue to strive towards data-driven decisions. The problem however is that enterprise clients, governments, and sometimes individuals are at loss on how to securely store the huge treasure troves of data that they are generating or capturing.
Traditional data storage methods are expensive because of the huge resources needed to manage a centralized data centre. Secondly, storing data in a central location increases the odds of a data breach since a security breach at any of the locations makes all the data vulnerable. More so, storing data in centralized locations also increases the odds of data loss in the event of a mishap.
With the decentralized nature of blockchain technology, data is stored on a decentralized network; hence, all the potential failpoint for a data breach or data loss become less off a worry. The fact that no single individual or company oversees the storing or keeping blockchain data also ensure that accuracy, integrity, and incorruptible nature of data sets.
2. Speedy data processing for real-time analytics
Big data is not much valuable in itself unless the data can be analysed to mine out the underlying behavioural patterns. Real-time data analysis makes its easier to make on-demand decisions – hence, organizations will find it much easier to run effectively and with significantly lower overheads.
Blockchain technology enhances the possibilities of real-time data analytics – all that will be required of developers are smart contracts that acts based on the of “if this, then that” logic of your smart contract. Blockchain technology can also be instrumental in unveiling new methods for monetizing the data.
3. Improved fraud detection and prevention
Blockchain technology can be a powerful tool for preventing data fraud by ensuring the immutability of data. Different kinds of data are valuable in different ecosystems – for instance, sales volume, inventory, and overhead expenses are some important data for a retail business.
The decentralized nature of blockchain technology ensures that you can’t edit, tweak, or manipulate data once stored on the blockchain. Where applicable and allowed, changes to a data set stored on the blockchain will be visible throughout all the nodes of the platform. Hence, it will be practically impossible to pull off data fraud on data stored on the blockchain.
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For today’s business, leveraging the power of big data isn’t a nice option — it’s a clear necessity. For nearly every industry, from communications to energy, architecture to real estate, the power of big data to provide intelligent insight can’t be overstated. The fine-grained detail and big picture are both visible at this level, both captured by careful analysis of huge datasets.
But make no mistake: huge isn’t an exaggeration. When a dataset reaches into billions of points of information, it simply exceeds human capacities. Even an expansive team of analysts can’t deal with data that large. That’s where cutting-edge artificial intelligence (AI) and machine learning come in. Driven by processing power that can handle datasets of that size, the most advanced AI can deliver the goods for 21st-century business.
For example, ride-sharing services like Uber need some way to manage demand and make sure that their customers don’t wait too long for a ride they’ve hailed. And by using machine learning to predict the details of demand, they’ve made massive gains in efficiency. For instance, Uber Eats, Uber’s food-delivery service, adopted AI to improve its customer satisfaction. As Danny Lang, the head of machine learning at Uber, explained, they moved from “a finite approach — where you compute the time using the distance between you and the restaurant, the average speed and the time to prepare the meal — to taking the delivery times for thousands and thousands of meals and basing the prediction on that. Overnight, that improved our estimates by 26 percent.”
The popularity of AI and big data stall as they expand
That kind of wizardry has been an easy sell to companies already occupying a high-tech niche. But as AI begins to mature, really coming into its own, its developers are finding it increasingly difficult to impress less tech-oriented companies and more conservative industries. As Joe McKendrick writes for Forbes, “To be sure, there is no shortage of excitement around the possibilities AI and machine learning bring to enterprises. But most organizations are still tepid about embracing these approaches in a big way.” In fact, in industries like real estate, the major players are holding off on AI until it can demonstrate clearer return on investment. “The real estate industry is conservative, technology-averse, and not prepared to take technology on board as a product,” cautions Paulo Scarpelini Neto, a real estate tech entrepreneur.
Indeed, a new Imprev Thought Leader Survey revealed that a solid majority of decision-makers in real estate were giving AI a pass, despite expressing interest in the power of big data. As they report, “Don’t hold your breath for widespread adoption of Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR) 3D tours in the next five years. Real estate execs expressed their doubts by giving these emerging technology [sic] lukewarm ratings. In fact, AI was ranked highest among the emerging technology that executives were ‘least likely’ to invest in, followed by AR and VR.”
That may put the breaks on the market penetration of AI, and with it, greater adoption of big data strategies in business. But we think that’s simply wrong.
But this is because AI is poorly understood
The power of big data and AI is too good to miss, and especially for conservative industries like real estate, architecture, and retail, big data offers revolutionary gains. Want to know more about your customers and their preferences, allowing you to personalise offers to them in real time? Big data is the answer. Need to design a building that can accomodate a long list of competing demands? Big data is the answer. Having trouble matching prospective buyers with properties or figuring out who’s really looking from the merely curious? Big data is the answer.
This isn’t hype — big data really can deliver.
But the problem McKendrick and Neto point to isn’t an issue with the tech or the data. Instead, because AI is so new and so high-tech, it’s poorly understood by most companies and their leaders. It’s understanding that’s at issue, not the utility of big data and AI.
In fact, as Kriti Sharma reports for Business Insider, “43% in the United States and 46% of respondents in the United Kingdom admitted that they have ‘no idea what AI is all about’”. That’s understandable — just a few years ago, this tech wasn’t available; it takes time to adjust and adapt. Moreover, as Michael Chui, James Manyika, and Mehdi Miremadi explain for McKinsey, “It is hard to reach a leading edge that’s always advancing.”
In plain English, the tech has outpaced understanding. And to make sense of it, business leaders “need to understand not just where AI can boost innovation, insight, and decision making; lead to revenue growth; and capture of [sic] efficiencies—but also where AI can’t yet provide value”, they insist. “What’s more, they must appreciate the relationship and distinctions between technical constraints and organizational ones, such as cultural barriers; a dearth of personnel capable of building business-ready, AI-powered applications; and the ‘last mile’ challenge of embedding AI in products and processes.”
AI explained, very briefly
Most people don’t get AI and big data. They don’t understand what it is and what it can do, nor do they have a sense of its limitations. Let’s go over those quickly.
What makes artificial intelligence and machine learning unique is that it learns to do something without being specifically programmed to do so. Using sophisticated neural networks that mimic the way human beings think and learn, AI uses carefully labelled data to teach itself. Consider facial recognition. In this application, a neural network might be shown millions of pictures of faces, each expressing a carefully identified emotion. By learning to associate the identified feelings with ‘maps’ of each face — the positions of the corners of the eyebrows, the distance between the edges of the mouth and the nose, etc. — a machine learning system actually…well…learns. After a while, it can look at unlabelled faces and have a very, very good sense of what that person is feeling.
It takes an enormous amount of data to get this process started, and there are technical challenges. Bias in the algorithms or the data can cause problems later, and sometimes, the AI learns to do what it does without us knowing exactly how, a problem known as the ‘black box’. And once it’s learned to recognise emotion, for instance, it can’t then automatically apply what it has learned to do something else with faces, like recognise gender. Its learning is often poorly generalisable, or in other words, limited to a very specific task.
But as AI advances, so too does its promise. And we predict that as businesses recognise the added value of big data — and the AI that manages it — they won’t be able to say no. And the undeniable benefits of AI are simply amazing, even in the most conservative industries.
AI in retail
These are real obstacles, fundamental challenges to the application of AI. But none of these are unworkable issues. And especially for routine tasks like tech support, in which the vast majority of calls relate to lost passwords, or customer service, in which the vast majority of tasks don’t demand the skills of a living, breathing human being, intelligent chatbots are revolutionising how we do business. For instance, Mai-Hanh Nguyen reports for Business Insider that “60% of US consumers have not completed an intended purchase based on poor customer service experience”. That’s a number that should terrify anyone who sells anything, not just the high-tech giants. But with intelligent chatbots that have learned to recognise emotional states and respond appropriately, your business can field top-notch customer service, 24 hours a day, 7 days a week. Take a look at IBM’s “Tone Analyzer”, for instance.
AI in real estate
Don’t underestimate the power of intelligent chatbots in real estate, either. Whether it’s fielding calls from clients, answering simple email queries, or organising showings, bots can free up valuable time for agents. And with emerging AI in the guise of smart speakers, some revolutionary changes are on the horizon for realty.
But where big data and AI really shine in real estate is in matching buyers to properties they’ll love. By using massive datasets, artificial intelligence can do some pretty amazing things. Already in 2016, simple bots demonstrated that they were better than human agents at predicting buyer preferences. With the more advanced machine learning and better data available now, AI offers realtors a ground-breaking new approach to marketing homes.
AI in architecture
Though traditionally slow to embrace change, the architecture industry will soon give AI more than a passing glance. Generative design is an exciting new approach in architecture. And by tasking AI with drawing up plans, and giving it a sense of what you need a building to do, it can run thousands upon thousands of designs to see which permutations give the best results. For instance, when Autodesk wanted to build a new headquarters, they realised that weighing competing design goals one against another at this scale was Herculean, so they handed the project over to AI. As Danil Nagy, a designer and senior research scientist for the company, explains, “The starting point for our use of generative design was trying to determine which aspects of the architectural design process were the most complex and difficult for humans to think through, and figuring out how to get a computer to work them out for us … It’s all about isolating those very tricky practical issues, and then using a computer to automate the development of solutions for those problems.”
Don’t listen to the naysayers
If anything’s clear, it’s that big data and AI are here to stay. They’re not just flashy tech and empty hype, and as these examples suggest, even industries reluctant to adopt new tech will soon happily embrace the awesome power they offer.
It’s understanding — not utility — that’s lacking.
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Python is a general-purpose programming language that is becoming an increasingly popular tool for data analysis. Its simplicity allows quick learning, so many data scientists choose Python for their professional needs.
With the average national salary of Python developers being $92,000, more and more people are interested in learning this programming language.
Moreover, the number of software libraries have reached maturity, allowing users of the popular statistical software packages like Stata and R to take advantage of the performance and flexibility of Python without any functionality sacrifices.
If you’re looking to increase the efficiency of your data analysis in Python, you can use the following quick wins.
1. Accumulate Resources
If you’re just beginning to learn data analysis in Python, you need to learn about it as much as you can. For that, you may turn to online resources. Thankfully, there are lots of useful resources that will get you up and running, including books, tools, tutorials, and interactive courses.
So to save you some time with finding resources on data analysis in Python, we have gathered this list of nice free resources.
2. Convert Data to int Type in Pandas
Pandas is a software library written specifically for Python, so it’s safe to assume that you will use it to manipulate data. This quick win improves your data analysis by making it easier to make int-type data.
There is an easy way to convert data to this type. “Most commonly, programmers use .astype(‘int’), but the conversion may fail if there are some errors,” says Charlie David, a programmer. “I’ve learned to avoid it by using an alternative:
Pd.to_numeric()
This command ensures conversion even if errors are detected.”
3. Find Unique Sets of Values among Millions of Entries
Let’s suppose you have a large set of data that you need to analyze. For example, there is a column with tens of thousands, hundreds of thousands, or even millions of unique entries, and your task is to identify a set of particular values using that data.
Many people use df.column_name.drop_duplicates(keep=”first”, inplace=False) to achieve this task, but you should also know that df.column_name.unique() does the same much quicker.
In case the final set of values contains a lot of duplicates, you can use keep=”first” option to remove them and make analysis easier.
4. Split a Column Using a Function
Use this simple split function to separate one column into multiple columns:
In [13]: df['column_name'] = df['column_name'].str.split(” “, expand=True)
5. Combine Two Existing Columns
If you need to combine two columns of data in Pandas into a single one, you can use the following options:
6. Quickly Group Columns by using GroupBy
With this function and value_counts, you can group by one column and count the values.
df.groupby(‘name’)['activity'].value_counts()
7. Increase Efficiency with diff
If your task is to determine the differences between certain values in the data, you can use a special function. For example, if you have a long list of people who spent various amounts of time at work, you can calculate who was in the office the longest by applying the following:
df = df.sort_values(by=['name','timestamp'])
df['time_diff'] = df.groupby(‘name’)['timestamp'].diff()
df[‘row_duration’] = df.time_diff.shift(-1)
7. Pull in Third-Party Data into Python
Let’s suppose that you’re a financial analyst and want to read data from sources like Google Finance and Yahoo! Finance (read about this Yahoo fetching issue here before you start). To get the data, you need to install the package called pandas-datareader via pip:
pip install pandas-datareader
Installing the package provides a wide range of options to pull in data into Python.
Conclusion
Python has seen an extraordinary growth in the recent years, with more and more world class companies using and supporting this programming language.
If you’re among those contributing to this growth, hope these quick wins will help you to increase the efficiency of your day-to-day data analysis tasks. Happy analyzing!
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Elections are like writing an exam! There are only two kinds of people who succeed in the exams – the people who work hard and the people who work smart. Elections are no different.
To win the elections, you either have to work hard or be smart. In the past few years, the big data analytics have helped various political parties across the globe make smart decisions during their electoral campaigns.
Do you remember 2010’s US Presidential Election where Barack Obama won it over Mitt Romney? Well, it was the first time people started talking about the potential of big data analytics in helping politicians in their electoral campaigns. And guess what, now every other party all across the globe is trying their luck with big data analytics.
Big data analytics in the field of politics
For years, marketers have used big data analysis to create smarter strategies to engage customers and generate new leads in the market, and have succeeded. Even in politics, some strategies influenced by the big data analysis (like US presidential election campaign or the Brexit referendum) seem to work.
For political parties, it was not possible to go from door to door and reach every voter before planning their campaign strategies. With the help of big data analysis, they could easily read the behaviors, mindsets and the preferences of the citizens at large. This is what most of the political consultant companies are doing to help their clients in setting up a successful election campaign.
However, it was recently revealed that the British political consulting company, Cambridge Analytica, gained access to the data of 50 million+ Facebook users. This data was later “misused” for political advertisements during the presidential electoral campaign in the US and the Brexit referendum campaign, as reported in the Economic Times.
When the security of private data is violated on such a large scale, it is meant to stir controversy, which it did. However, one cannot deny the fact that well-made political ads, which address contemporary issues, can have a significant impact on the voters if they are properly circulated on Facebook. So, yes, there is a possibility that big data analytics can be helpful for the political parties to some extent.
Then what is this risk people are talking about?
Experts have expressed their doubts about the efficacy of the big data analytics that uses social media data. It is not clear what kind of strategic decisions can be made using big data analytics? Popular social media websites like Facebook, Twitter can be used very well to gather data, but the collected data can be quite complicated to handle.
Social media data is often influenced, which can lead to flawed results in the analytics. Besides, finding the necessary information on such vast network can be as difficult as finding a needle in the haystack. However, the problems do not end here.
While analyzing the data, a lot of “nonsense correlations” among different variables can be identified. And as the number of variables rises, the rate of likewise correlations also surges. As a result, it often becomes too hard to recognize the various forms of causes and effects. So one can clearly see how unreliable the resulting data can be.
Nick Heudecker, who is an analyst at Gartner (an American research and advisory company), told Tech Republic that the failure rate of big data projects is close to 85 percent. Even though he was talking about the field of business, it clearly suggests where the big data analytics stand in today’s world. The experts are now using several models and algorithms from various fields of study (like statistics, mathematics and engineering) which, however, haven’t done anything new as of now. In fact, it displays more “nonsense correlations” than its previous approach.
Conclusion
The instances of the failure of big data analytics are way too many. The biggest one, however, remains to be the Google Flu Trend experiment (2008) that tried to predict the prevalence of flu just by analyzing the search histories before the government authorities did. In elections, where the stakes are incredibly high, relying on big data analytics can cause a major blunder.
Reportedly, big data itself is not very useful to people. It can only be effective if the analytics are applied properly to the data. It won’t be wrong to suffice that relying on big data analytics to frame the strategy is nothing less than a gamble, where the chances of succeeding are very low.
However, most political parties are putting their trust on various political consulting firms these days which use big data analytics to help them strategize the campaign. And since the political parties don’t have a better choice at this moment, they may just stick with the big data analytics for longer period, even if it is full of uncertainty.
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Not very long ago, people saw a meteoric rise in the value of a virtual currency called Bitcoin. By investing in it, they became millionaires overnight. Suddenly, everyone wanted to own some and others were skeptical about its permanence as they thought that the bubble can burst like that of Dotcom and Tulips.
Just like these two historical events, ever since it made it big, several leading bank executives have termed Bitcoin as a “Bubble” because of its sharp rise by a fundamental margin. Its value reached its highest point in December 2017 at $19,498.63. Moreover, major exchanges such as the CME Group and Cboe Global Markets, are about to launch bitcoin futures exchanges.
Nevertheless, it is not just the steep growth that makes the news; the value of the fastest-growing cryptocurrency was halved from November 2014 to January 2015, and it crashed in December 2017.
Image Source: https://99Bitcoins.com/price-chart-history/
In an expert roundup on Business Insider, Tom Lees, who’s a managing partner at Fundstrat Global Advisors, revised his mid-2018 Bitcoin forecast. He increased it to $11,500 from $6000.
Does this mean that the Bitcoin bubble is on its way to the big burst? We guess not, because it just makes Bitcoin akin to any other volatile purchase vehicles and hence, it is a territory that needs to be treaded carefully. Having said that, let’s take a look at the risk factors that everyone intending to invest in the cryptocurrency should be aware of:
Risk #1: Limited Supply of the Cryptocurrency
Unlike any other centralized banking system, Bitcoin is decentralized in nature. It is more like gold whose underlying store of value cannot be affected by currency production. And even if central banks create derivatives based on Bitcoin, people can always buy it directly. However, the cryptocurrency’s value depends on trading and remains highly volatile as there will be only 21 million Bitcoins that can ever be produced. This is where you need to tread carefully.
Risk #2: The Alarming Rate of Transfer Fee
Currently, the transfer fee for purchasing Bitcoin is not expensive enough to change the market price of the currency. However, looking at the mushrooming growth rate, the time is not far when the transfer fee is going to be bigger proportionately. This can be a factor for worry, if not risk as you’ll also have to think about shelling out a huge transfer fee along with the increased value of the coin.
Risk #3: You Could Be Dealing with Criminals Unknowingly
All sorts of credit charges and other relevant details are supposed to pass through a third party and this this creates a traceable trail. Bitcoin transactions do away with such trail and instead all your transaction information creates a block in the blockchain. This block of data is public and can be read, seen by all those in the chain. Hence, all the transactions that take part in a blockchain will remain public for sure. What remains anonymous here are the identities of the buyer and the seller. It stays shared by them only instead of all the middlemen.
As long as these exchanges happen over-the-table and for legal transactions, it is not going to be problematic. The catch lies in unknowingly entering a digital transaction or an agreement with a criminal party.
Moreover, there are several illegitimate reasons to purchase Bitcoin, which is leveraged by tax evaders and people who want to avoid the rules and regulations that dictate fiat currencies. You cannot rule this risk out, bubble or no bubble!
Risk #4: Only a Small Number of People Exercise Control
The next risk associated with cryptocurrencies such as Bitcoin is that the market control remains in the hands of a small group. For instance, about 1000 people hold almost 40% of all the Bitcoins. And let’s not forget the fact that only a certain number of Bitcoins can be mined. These “whales” are big dampeners to your plans because they can choose to sell their stock of Bitcoins to make the most of the high market prices. Sometimes, the whales can go ahead and coordinate between themselves in order to make the market fluctuate. The uncertainties are real and the laws concerning cryptocurrency are not accurate.
Risk #5: Cryptocurrencies are Getting Banned by Institutions and Countries
A number of major international banks are banning Cryptocurrency purchase using credit cards. You can still purchase the coins by using your debit cards though. As more banks join the bandwagon, several countries are also making the virtual coins illegal.
For instance, if you trade in Bitcoin or other alt coins in Bangladesh, you can be punished and receive a sentence of up to 12 years in prison. The central bank of Bolivia went on to release a statement that said, “It is illegal to use any currency that is not issued and controlled by a government or an authorized entity.” China, which has become of the largest Bitcoin trading market in the world, there’s a ban but that remains strictly on banking institutions and employees. There’s also a ban on engaging in Bitcoin businesses via banking or dabbling in any business or service associated with the Bitcoin industry. However, trading or mining Bitcoins is not considered as illegal for the citizens.
The key takeaway here is that transaction using Bitcoin remains largely anonymous. This makes it a potential tool for criminals who dabble in illegal acts such as tax evasion, drug trafficking and money laundering to name a few.
Risk #6: Insecure ICOs or Initial Coin Offerings
Cryptocurrency startups are all rage nowadays, with founders, CMOs and Chief are coming up with a wide range of startups that draw the users’ attention. It all started in 2017, investing in projects associated with a blockchain or cryptocurrencies became very popular among cryptocurrency holders. This type of fundraising is called an ICO — Initial Coin Offering. With a simple internet connection, you can be a part of cryptocurrency-based firms or startups. The problem is that this market is on its own with no regulation. The risk assessment mechanism, productivity and guarantee are some of the things you need to see before jumping into one without a second thought!
Risk #7: Possible Attacks on Exchanges
Bitcoin exchanges are not safe anymore. With major attacks on the exchanges, the value of the cryptocurrency may falter. Take the instance of Mt.Gox, the heist of which made them lost 850,000 coins. The value of these coins after four years runs into billions. It never recovered from the attack of this scale and went bankrupt. The exchanges are also threatened by DDoS attacks.
To Conclude
At present, we cannot tell for sure that whether the complete bubble is going to burst or whether blockchain will continue to rule the industry. What we do know is that even though the price tag of the cryptocurrency is enough to knock your socks off, awareness is the only thing that will save you from its potential risks. And like other investments, this is a risk that you can and should take to see where it takes you!
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We all know how marketing can enhance sales. However, enhancing marketing requires real skills and creativity. Or does it? Can machines do a better job at promoting, advertising, and ultimately selling products than a creative human being? A complete switch to AI seems futuristic, but some major brands have already implemented the tactic of using chatbots, voice assistants, and other smart systems for marketing enhancement purposes. In fact, a vast majority of globally known brands has already benefited from using AI. Let’s see why and how.
The AI may be more efficient at certain tasks, but it’s still a long way from being able to replace people. For instance, only 7% of customers are open to buying a product through a chatbot. However, companies like Nordstrom have found the right job position for AI-powered devices. Namely, their digital tool called ‘Style Boards’ allows salespersons to create and send personalized recommendations to existing and potential customers. Combine that with one of the many Nordstrom promo offers, and you’ll get an outstanding value purchase.
Another good example comes from one of the most recognizable brands worldwide. Nike uses an Artificial Intelligence system called ‘Nike On Demand’ to encourage people to lead a healthier life and exercise more by sending motivational messages. Still, if AI fails to motivate you, Nike deals and promo codes certainly won’t. Whether AI is taking over our jobs or not is debatable, but it’s a well-known fact that more and more brands are exploiting it for marketing purposes. Take a look at the infographic below, and you’ll learn that the future is a lot closer than you think.
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