Alternative Data News. 13, May 2020

The AltDataNewsletter by CloudQuant

Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.


Our weird behavior during the pandemic is messing with AI models

Machine-learning models trained on normal behavior are showing cracks —forcing humans to step in to set them straight.

In the week of April 12-18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too—and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.

When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

But they have also affected artificial intelligence, causing hiccups for the algorithms that run behind the scenes in inventory management, fraud detection, marketing, and more. Machine-learning models trained on normal human behavior are now finding that normal has changed, and some are no longer working as they should.

2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 2.3098, Raw Interest Score: 0.9174,
Positive Sentiment: 0.1189, Negative Sentiment 0.1869

CloudQuant Thoughts : A very interesting article with lots of quotables! “This is also a reminder that human involvement in automated systems remains key”, AI and ML are extremely powerful tools but one only has to try to send a simple text using SIRI, a quite narrow AI/ML task these days, to witness how easily it can go wrong. One interviewee described AI based systems as “fragile”, they are certainly not “set and forget”. The section about high speed online advertising pricing was also very interesting. One of my colleagues worked in that environment, where online advertisers have algos which bid against each other for ad-space for fractions of a penny in fractions of a second. Having recently witnessed the massive OIL price crash, I can only imagine how out of control these ad markets must be right now.  “You need a data science team who can connect what’s going on in the world to what’s going on the algorithms, an algorithm would never pick some of this stuff up”, an interesting quote to see in light of the “How To Get The Best Out Of Your Freelance Data Scientists” article a few down from here and its attitude to outsourcing Data Science work. These points of view are not compatible so one of them must be very wrong.

‘A $35,000 data set that could have saved or made $100 million’: Alt data is back in the spotlight — here’s how providers and buyers have adapted.

Demand for alt data from hedge funds and corporates is surging. Here’s the outlook for the industry, and why some are wary about chasing the trend. Armando Gonzalez never thought his alternative-data company would make something that his father, who is in his 70s, would be checking everyday.

But the novel coronavirus pandemic has forced Gonzalez’s Ravenpack, a Spain-based alt-data company that tracks media reports for investors, and just about every company in the world to rethink the way they do business. What that meant for Ravenpack was the introduction of trackers specifically targeted for mentions of the virus in the media, with indices like the fake news index, which tracks when dubious information is spread about the virus, and the fear index, which tracks hysteria in conjunction with the virus.

“It’s become a tool for anyone who wants to take a more data-driven approach to understanding the reaction to the virus,” he said. Gonzalez told Business Insider that local governments have used and customized the index to understand why people might believe a certain thing about the virus — and that his father is checking in daily with him about the fear index’s rise and fall.
2020-05-07 00:00:00 Read the full story…
Weighted Interest Score: 3.6380, Raw Interest Score: 1.4483,
Positive Sentiment: 0.0980, Negative Sentiment 0.1307

CloudQuant Thoughts : “This is a $35,000 dataset that could have saved or made $100 million”. If you trade for a living and make large sized transactions you really should be using Alternative Data. And if you are developing automated algos you should, on a regular basis, fold in an alternative dataset to test its impact, it is cheap and easy.

Hedge funds’ use of alternative data tipped to surge, new industry study finds

More than half of hedge fund managers are now using alternative data to gain a competitive edge, according to a wide-ranging new study into alt data trends by the Alternative Investment Management Association and fund services provider SS&C. The report, ‘Casting the Net: How Hedge Funds are Using Alternative Data’, explores the ways in which hedge funds now utilise alternative datasets – defined as unconventional, non-market and non-traditional economic and financial information, such as satellite imagery, social media trends and weather patterns – in their businesses.

The study, jointly published by AIMA and SS&C today, quizzed some 100 hedge fund managers globally, collectively managing about USD720 billion in assets across strategies, including equity long/short, relative value, event driven, macro and CTAs, among others. More than a quarter of those polled (27 per cent) manage more than USD5 billion in assets, while 25 per cent of those surveyed are considered to be “market leaders” – or hedge fund managers that have been using alternative data for more than five years.
2020-05-11 00:00:00 Read the full story…

CloudQuant Thoughts : Don’t forget that we also have Alternative Data Sets available, head over to our Data Catalog to find out more.

How To Get The Best Out Of Your Freelance Data Scientists

With the advent of remote working culture, employers are getting comfortable in hiring freelance data scientists instead of creating a full-time core data science team. Not only does it provide a flexible work schedule for data science freelancers but also offers a lot of benefits for organisations, such as the best return on investment for companies, especially small businesses, and the hiring is quicker as well as less expensive.

In fact, in a report, it has been stated that there are 15 million freelancers in this country, across the industry, which is expected to double up by 2023, including data scientists. This growth can be a result of the economic downturn due to the COVID-19 pandemic, which is forcing many professionals to start freelancing to stay relevant in the industry.

However, for an organisation, managing and getting the best return on investment from your data science freelancers is indeed a challenging task. Considering the freelancers work on their schedules, as well as have different working habits, it gets complex for employers to have control over their freelancers virtually. Nevertheless, there are a few ways that can help organisations get their projects done smoothly via freelance data scientists.

2020-05-12 11:49:05+00:00 Read the full story…
Weighted Interest Score: 2.3699, Raw Interest Score: 1.4116,
Positive Sentiment: 0.2849, Negative Sentiment 0.2590

CloudQuant Thoughts : In my experience, extensive knowledge of the business is the golden key to high quality Data Science. Freelancing your Data Science may be a false economy.

Amazon launches “cognitive search” service Kendra in general availability

Amazon today launched Kendra, an AI and machine learning-powered service for enterprise search, in general availability. Kendra debuted in preview last December during Amazon Web Services (AWS) re:Invent 2019 in Las Vegas, and it’s now available to all AWS customers.

Enterprises typically have to wrangle countless data buckets, with upwards of 93% saying they store data in more than one place. As you might imagine, some of those buckets inevitably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends. This is where services like Kendra come in — they use AI to return results that are more relevant to users or embedded in apps issuing search queries.
2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 2.1189, Raw Interest Score: 1.1577,
Positive Sentiment: 0.0772, Negative Sentiment 0.1029

Detecting Weird Data: Conformal Anomaly Detection

An introduction into conformal prediction and conformal anomaly detection frameworks (with code)

Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of tumors, banks monitoring credit card activity, or self-driving cars using feedback from a camera — in these cases, its likely more important to know whether or not the data is weird or abnormal. Weird data is more formally called anomalies and mathematically can be thought of as data that is generated from…

2020-05-13 01:03:29.156000+00:00 Read the full story…
Weighted Interest Score: 2.1044, Raw Interest Score: 1.1210,
Positive Sentiment: 0.0989, Negative Sentiment 0.3682

Bloomberg Data License clients get access to Trendrating data analytics

Trendrating, a specialist in trend capture analysis with a focus on helping customers more effectively profit from bull markets and avoid bear phases, has made its ratings data and analytics available to Bloomberg Data License clients via the Bloomberg Enterprise Access Point (BEAP).

The new additional offering on BEAP is touted as making it possible for portfolio managers and quantitative analysts to access data for professional equity investors with a “robust methodology” in order to rate price trends, validate investment ideas, add an extra layer of risk control and capture additional alpha.

This strategic collaboration with Bloomberg is seen as just the latest step in an industry wide trend where buy-side participants have long been demanding access to new data sources and innovative technology solutions to address limitations and key intelligence gaps of their legacy platforms. Such short comings have been painfully and brutally exposed during the recent market downturn.

2020-05-07 00:00:00 Read the full story…
Weighted Interest Score: 5.0505, Raw Interest Score: 3.0303,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

Practical reasons to learn Mathematics for Data Science

Demystifying the need for learning math to deal with real-world challenges as an ML practitioner

Mathematics in data science and machine learning is not about crunching numbers, but about what is happening, why it’s happening, and how we can play around with different things to obtain the results we want.

The misconceptions around learning Math for Data Science have been augmented by courses, videos, and blog posts with titles like “Data Science with No Math”, “Data Science for Deve…
2020-05-13 04:19:15.912000+00:00 Read the full story…
Weighted Interest Score: 3.4900, Raw Interest Score: 2.0296,
Positive Sentiment: 0.0000, Negative Sentiment 0.0870

What Do You Need To Do Before Hiring A Data Scientist?

Artificial Intelligence brings a promise of exponential growth and taking your business to new heights. No wonder there is a lot of excitement around the application of Artificial Intelligence (AI).

Many companies are rushing to hire their first Data Scientist or build a Data Science team right off the bat. Their enthusiasm is understandable as they want to innovate with data and not be out-competed by the market. However, these early missteps and false starts are causing a massive opportunity cost to companies, and Data Scientists are moving on from these companies within just a couple of years.

Here are some recommendations for you to prepare before investing in Data Science function at your company.

2020-05-12 15:09:06+00:00 Read the full story…
Weighted Interest Score: 3.2867, Raw Interest Score: 1.8287,
Positive Sentiment: 0.1441, Negative Sentiment 0.2660

Why India Is Such An Important Market For Data Science Product Vendors

India has been an important marketplace for data science and analytics products for many years now. If you look back, the data science market saw an upheaval with the coming of the e-commerce market in India. With so many e-commerce players running in billions of dollars of net worth, it led to the creation of strong data science needs and talent to optimise processes.

Another sector that created the boom of the data science product market is the BFSI sector. With numerous banks and fintech firms deploying analytics and driving customer support, data science products have come to the front, in optimising financial processes. Not just customer personalisation, but also fraud management are areas where data science solutions are deployed extensively in India. Marketing analytics in India has also been a strong area for the usage of data science.

2020-05-12 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2735, Raw Interest Score: 1.5707,
Positive Sentiment: 0.3300, Negative Sentiment 0.0264

Expanding Data Governance into the Future

Shortened time frames to leverage business insights and navigate data privacy and ethics call for the next generation of Data Governance (DG). This DG describes a collaborative, thoughtful, long-term framework consisting of processes managing trusted data assets across the organization. Kelle O’Neal, Founder, and CEO of First San Francisco Partners, sees a need to make firms aware of Next-Gen Data Governance, while at the same time helping companies adapt to successful Data Governance practices with other business areas.

Recognition that good Data Governance has become a must has come none too soon. Donna Burbank, Managing Director at Global Data Strategy, notes that many companies are beginning or planning to begin a Data Governance program, including a broader range of industries than before.
2020-05-05 07:35:27+00:00 Read the full story…
Weighted Interest Score: 2.9881, Raw Interest Score: 1.8451,
Positive Sentiment: 0.2590, Negative Sentiment 0.1403

Private Equity Investors Need to Keep Up With Data Analytics

If you had a key that could unlock a shortcut to business achievement and professional success, wouldn’t you use it? The obvious answer would be yes, of course — but today, some private equity firms are fumbling with the metaphorical lock.

Big data and advanced analytics pose an incredible opportunity for growth and achievement in the private equity sector. In a financial landscape where assets are expensive, deals competitive, and stakes high, the strategic insights that advanced analytics can provide are clearly invaluable.

As one writer for Bain & Company frames the matter, “These emerging technologies can offer fund managers rapid access to deep information about a target company and its competitive position, significantly improving the firm’s ability to assess opportunities and threats. That improves the firm’s confidence in bidding aggressively for companies it believes in—or walking away from a target with underlying issues.” If leveraged correctly, these tools can help PE firms swiftly parse vast quantities of business data and develop the insights that investors need to make timely, well-optimized investment decisions — and, ideally, achieve outsized returns for their capital.

2020-05-12 00:00:00 Read the full story…
Weighted Interest Score: 2.7433, Raw Interest Score: 1.3975,
Positive Sentiment: 0.3278, Negative Sentiment 0.1208

Data Analyst Salary: 5 Pressing Questions Answered

What’s a typical data analyst salary? How much can those with a lot of experience and skills potentially earn?

Data analysts are crucial members of many organizations. Executives rely on data analysts’ work product to make vital decisions about the overall direction of the business. On a team level, data analysts also provide those valuable insights that allow developers, engineers, and others to make short-term decisions.

In other words, data analysts can mean the difference between success and failure. But does the average data analyst salary match the role’s actual importance to the organization? That’s a very big and complicated question.

2020-05-13 00:00:00 Read the full story…
Weighted Interest Score: 2.7190, Raw Interest Score: 1.6176,
Positive Sentiment: 0.1549, Negative Sentiment 0.1721

Big Data Analytics is Massively Disrupting the Legal Profession

Tech giants such as Amazon and Facebook are mining data to get valuable business insights. Graziadio Business Review has written a detailed article on Facebook data mining. The social media site’s successful utilization of big data is one of the reasons it’s recent quarterly earnings topped $21 billion.

However, large corporations aren’t the only ones leveraging big data. As a matter of fact, almost all successful companies are using data analytics to get their hands onto useful information.

The legal industry appears to have lagged most other professions in leveraging big data. Law firms are the last ones to enter the golden world of data mining, which is a shame. The Wharton School at the University of Pennsylvania wrote that getting law firms to use big data will be the next major challenge. The authors pointed out that big data in the legal profession is still in its infancy.

2020-05-06 18:46:53+00:00 Read the full story…
Weighted Interest Score: 2.5121, Raw Interest Score: 1.3291,
Positive Sentiment: 0.2900, Negative Sentiment 0.4833

Alteryx Unveils Analytic Process Automation Platform

According to a recent press release, “Alteryx, Inc. today unveiled its enhanced analytic process automation (APA) platform, which unifies analytics, data science and business process automation in one, end-to-end platform. By bringing data, processes and people together in a converged approach, the Alteryx APA Platform enables high-impact business outcomes and rapid upskilling of people across the organization. Designed to put automation in the hands of all data workers—from line-of-business users to skilled analysts and data scientists—the human-centered…
2020-05-13 07:05:26+00:00 Read the full story…
Weighted Interest Score: 2.4899, Raw Interest Score: 1.6824,
Positive Sentiment: 0.3365, Negative Sentiment 0.1346

Best Practices for Handling Customer Data

Back in 2006, UK mathematician Clive Humby was the first to coin the phrase, “Data is the new oil.” While the analogy has been controversial to some, the statement foretold how business has evolved in the last decade. Today, companies in all sectors rely on customer data to augment or otherwise enable their business. Whether your company is a merchant collecting billing information from customers or a service provider logging usage of your platform, data aggregation is becoming a standard practice. While the rise in customer data collection has created new opportunities for business, it has also introduced new risks that must be considered and mitigated where possible.

Customer information is both an asset and a liability. As more consumer data is collected for business purposes, more attention is being paid to the enforcement of standards for storage, transmission, and retention. Laws such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Protection Act (CCPA) in the United States outline rules for handling the data for customers in those regions, as well as punishments for failure to handle that data appropriately. Beyond legal ramifications, the loss or misuse of personally identifiable information (PII) can also cause irreparable damage to the trust relationship between a company and its customers.

2020-05-13 07:30:17+00:00 Read the full story…
Weighted Interest Score: 2.4113, Raw Interest Score: 1.4177,
Positive Sentiment: 0.0781, Negative Sentiment 0.2009

How APIs can help families plan their finances

Data modelling and machine learning (ML) offers a tantalising possibility – that by gathering enough data inputs you can predict what will happen in the future based on current information. ML models are commonly used in the context of business decisions, such as assessing investment outcomes or growth performance, where they can add significant value.

They’re rarely used in the realm of human experience for two main reasons. Firstly, humans are famously irrational and hard to predict using the few data points available. Secondly, the cost of traditional data modelling means that it only makes economic sense in a business context.

But easy-access APIs are changing both of these to provide more data and to improve model accuracy. To see why, we’re going to talk about babies.

2020-05-11 16:19:20 Read the full story…
Weighted Interest Score: 2.3885, Raw Interest Score: 1.4874,
Positive Sentiment: 0.1463, Negative Sentiment 0.2195

Twinkle twinkle little staR, have you tried these Data Science Projects with ‘R’?

Twinkle twinkle little staR, have you tried these Data Science Projects with ‘R’?

2020-05-13 04:09:59.092000+00:00 Read the full story…
Weighted Interest Score: 2.1529, Raw Interest Score: 1.3781,
Positive Sentiment: 0.2890, Negative Sentiment 0.1111

Top 8 Data Science Institutes In India For Corporate Training

Corporate training has been playing an essential role in India for training professionals and allowing companies to provide superior IT services. This has helped the country become one of the leading countries in the world that offer IT services. However, with the changing demand for IT skills due to the rise of data science, corporate training has further gained prominence among various companies.

Today, data-driven companies are struggling to find the right talent who can assist them in driving business growth by moulding information and delivering insights into data. Consequently, various institutions in India are offering corporate learning in data science-related fields to help businesses bridge the talent gap in the market. Please note that this list is not a ranking and the institutes are listed in alphabetical order.
2020-05-13 08:30:00+00:00 Read the full story…
Weighted Interest Score: 2.1056, Raw Interest Score: 1.3696,
Positive Sentiment: 0.2283, Negative Sentiment 0.0351

Madrona Venture Labs spinout Simplata aims to protect sensitive company data in cloud apps

A new spinout from Seattle-based startup studio Madrona Venture Labs wants to help companies protect data flowing through their various cloud apps. Simplata Technologies is led by co-founders Steve Banfield, CEO, and Bruce Roberts, CTO. Banfield previously led BMW ReachNow as CEO and was an executive at traffic data company INRIX. Roberts spent seven years as CTO at Domain Tools and has extensive cybersecurity experience. Both tech vets held entrepreneur-in-residence titles at MVL before heading up Simplata. Reached via email this weekend, Banfield didn’t want to divulge much about the company, but said Simplata is “focused on a new approach to protecting sensitive data in cloud applications.” The idea was incubated inside MVL and born out of its focus on privacy and security.
2020-05-11 16:30:00+00:00 Read the full story…
Weighted Interest Score: 2.0924, Raw Interest Score: 1.3514,
Positive Sentiment: 0.1308, Negative Sentiment 0.0872


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