Industry News

Industry News

Alternative Data News : Top Tech Trends To Watch in 2020 – Alternative Data is #5 : Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics : Ex-Google Cloud engineers raise $8M for Seattle machine learning startup Kaskada to facilitate AI feature engineering : Labelbox raises $25 million to grow its data-labeling platform for AI model training

Alternative Data News. 05, February 2020

Alternative Data News. 05, February 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.

Top Tech Trends To Watch in 2020 – Alternative Data is #5

Alternative data drives financial services.
Investors and hedge funds are looking beyond financial statements for a competitive edge. They’re harnessing “alternative data” from job listings, social media posts and satellite images to foot traffic at stores and the flight paths of corporate jets. Thanks to artificial intelligence, machine learning and natural language processing, these vast pools of data are easier to search and more accurate in predicting trends. Alternative data startups include AlphaSense, BattleFin and Ireland-based Eagle Alpha.
2020-02-03 15:34:55 Read the full story…
CloudQuant Thoughts : Alternative Data makes it to # 5 on  Forbes top 5 Trends to watch in 2020! You are ahead of the curve baby! Stay ahead by heading over to the CloudQuant Alternative Data Catalog.

Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics

With mountains of market data, historical prices, and transactions data stored in disparate systems, securities and investment firms are shifting from a focus on collecting data to extracting value from it.
A December 2019 paper by capital markets consultancy GreySpark Partners examined the potential for buy-side and sell-side firms to transform large quantities of big data into actionable intelligence – producing what is known as ‘smart data – through specialized analytics.
The move comes as electronic trading has generated massive data sets across equities, fixed income and currencies. Firms are hiring data scientists and coding analytics to mine this data for trading opportunities or to identify patterns that help lower transaction costs.

2020-02-03 15:34:55 Read the full story…
Weighted Interest Score: 4.2167, Raw Interest Score: 2.2246,
Positive Sentiment: 0.1055, Negative Sentiment 0.1247

CloudQuant Thoughts : Smart Data is just automated Feature Engineering. In my experience the Smartest data is produced by a Human Expert working with the source data to create unique and quality data. For example, in human traffic analysis, the years of expertise may point a Human Expert to immediately check the duration of Amber on a high accident rate intersection. Whilst we would expect an ML system, given the right data, to come to the same conclusion, a Human Expert can shortcut the data leaving the ML to focus on what is the ideal solution.

Ex-Google Cloud engineers raise $8M for Seattle machine learning startup Kaskada

Machine learning is all the rage in big tech, but still largely unavailable to most companies that don’t have the resources or the knowhow to build it. Seattle startup Kaskada wants to change that.
The company just raised a $8 million Series A round to grow its software platform for big companies to deploy machine learning — systems that can learn from experience without requiring additional programming. The round brings the startup to $9.8 million in lifetime funding, and investors include Voyager Capital, NextGen Venture Partners, Founders’ Co-op, and Walnut Street Capital Fund.
Kaskada’s software is geared primarily toward enterprise companies outside the tech world that still rely on some form of data science for their products. The startup wants to make it easier for the two main roles involved with machine learning products — data scientists and data engineers — to work together.

2020-02-04 17:00:13+00:00 Read the full story…
Weighted Interest Score: 2.7668, Raw Interest Score: 1.8651,
Positive Sentiment: 0.2778, Negative Sentiment 0.1587

Kaskada raises $8 million to facilitate AI feature engineering

Feature engineering — the process of using domain knowledge to extract features from raw data — is essential to tuning machine learning performance. It’s also typically arduous and involves rewriting features before they’re deployed, which is why in 2018 Ben Chambers and Davor Bonaci cofounded Kaskada, which uses mining techniques to compute and serve AI features in real time.
Today the Seattle, Washington-based startup announced it has raised $8 million in a series A round of funding, with participation from Voyager Capital, NextGen Venture Partners, Founders’ Co-op, and Walnut Street Capital Fund. This brings Kaskada’s total raised to $9.8 million, following a $1.8 million seed round in September 2018. CEO Bonaci says the capital will be used to accelerate the company’s growth, expand its team of software engineers, and fulfill customer demand ahead of its flagship product’s launch in the first half of 2020.

2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 4.1863, Raw Interest Score: 2.4801,
Positive Sentiment: 0.0886, Negative Sentiment 0.0000

CloudQuant Thoughts : This is obviously a hot topic this week!

Labelbox raises $25 million to grow its data-labeling platform for AI model training

Labelbox today announced the close of a $25 million series B round to grow its platform that helps customers label the data needed to train AI systems. The round was led by Andreessen Horowitz, with participation from Google’s AI-focused Gradient Ventures fund, Kleiner Perkins, and First Round Capital.
The funds will be used to develop and accelerate Labelbox’s roadmap for machine learning and computer vision models by doubling the size of its engineering and sales teams. Labelbox also enables users to automate some labeling so a company can manually label all data except any that falls below a particular prediction confidence threshold, COO Brian Rieger told VentureBeat in a phone interview.

2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2148, Raw Interest Score: 1.7570,
Positive Sentiment: 0.0976, Negative Sentiment 0.0000

CloudQuant Thoughts : Break your data so that you manually label the high prediction confidence data and leave a machine to label the low prediction confidence data. Hmmmm… Interesting…

NGD raises $20 million for storage hardware designed to handle AI workloads

IDC predicts that worldwide data will grow 61% to 175 zettabytes by 2025, which will invariably put a strain on infrastructure as organizations look to sort, search, scan, and otherwise derive value from this data. That’s where NGD Systems comes in — it’s an Irvine, California-based company offering a computational storage drive tailor-made for on-device AI and machine workloads. To lay a runway for growth, NGD this week closed a $20 million series C financing round led by Western Digital Capital, with participation from existing investors including Orange Digital Ventures, Partech Ventures, BGV, and Plug-N-Play.
The round brings NGD’s total raised to date to $45 million following a $12.4 million series B in February 2018, and CEO Nader Salessi says the funding will enable NGD to execute its go-to-market strategy by supporting production as well as sales and marketing efforts.
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.4074, Raw Interest Score: 1.8519,
Positive Sentiment: 0.2593, Negative Sentiment 0.1481

New Books and Resources for Data Science Central Members

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.

  • Statistics: New Foundations, Toolbox, and Machine Learning Recipes
  • Deep Learning and Computer Vision with CNNs
  • Getting Started with TensorFlow 2.0
  • Book: Classification and Regression In a Weekend
  • Online Encyclopedia of Statistical Science
  • Book: Azure Machine Learning in a Weekend
  • Book: Enterprise AI – An Application Perspective
  • Book: Applied Stochastic Processes

2020-02-04 16:04:32+00:00 Read the full story…
Weighted Interest Score: 3.8875, Raw Interest Score: 1.5997,
Positive Sentiment: 0.0849, Negative Sentiment 0.0425

2020 is the Year for Enterprise Data Connectivity

Expect this in 2020: Data will come to the forefront of enterprise priorities. The consumerization of IT and democratization of data have triggered a dramatic shift in data control across small and large organizations. Data control – once the sole domain of the CIO or CTO via the IT department – is increasingly in the hands of users.
The proliferation of applications created a shift within IT departments. Fewer and fewer companies look to IT to control data and instead, these organizations are governing data. Furthermore, with few exceptions, data is now firmly in the hands of business units given the propagation of cloud-based enterprise and functional and departmental applications. Today, organizations are continuously installing new applications to gain a competitive advantage and are establishing yet another source of data with each one. Our enterprise data truth is that digital information is now created by and accessible to the average non-technical user of applications and systems, without having to require the involvement of IT.
But what are those users – and the enterprises they represent – missing? They’re most often missing access to the right data, when and where they need it … in part due to the data fragmentation resulting from the multitudes of departmental apps, data in the cloud, and the diversity of enterprise systems.

2020-02-03 08:35:22+00:00 Read the full story…
Weighted Interest Score: 3.6155, Raw Interest Score: 1.9649,
Positive Sentiment: 0.2090, Negative Sentiment 0.1254

These China Companies Can Survive The Wuhan Coronavirus

Workers use their laptops near a display showing sales data at the command center at the … [+] headquarters of e-commerce retailer in Beijing. Investors perception is that e-commerce players will weather the coronavirus outbreak better than most. The KraneShares China Internet ETF was up over 3% on Monday, a down day for China. (AP Photo/Mark Schiefelbein) ASSOCIATED PRESS

China’s stock market fell on Monday. Anyone who was surprised by that is …
2020-02-03 00:00:00 Read the full story…
Weighted Interest Score: 2.7916, Raw Interest Score: 1.3462,
Positive Sentiment: 0.1122, Negative Sentiment 0.2083

If You Need Somebody — Not Just Anybody — Data Literacy Help Is Here

Some organizations need a little help with data literacy just to get their feet on the ground. Maybe they can’t seem to move the bar in terms of using data to make decisions. Or they sense that their employees struggle to understand and aren’t so self-assured when it comes to data. Others lack the resources and talent needed to deliver timely insights or scale existing internal efforts.
The report “Data Literacy Matters: The Writing’s On The Wall” presents Forrester’s data literacy framework, which outlines the components of a comprehensive data literacy program. The second report in our series on data literacy, “Build A Data Literacy Curriculum Of ACES,” which is coming soon, will address the question of external training. Organizations needing guidance on building a data literacy program can turn to:

  • Peer exchanges.
  • Data literacy specialists.
  • Data and analytics tools vendors.
  • Insights service providers.

2020-02-05 09:37:14-05:00 Read the full story…
Weighted Interest Score: 2.4778, Raw Interest Score: 1.3246,
Positive Sentiment: 0.2805, Negative Sentiment 0.1247

How to ensure your data science projects are successful every time

In a 2017 survey, Gartner analysts found that more than half of data science projects never deploy. This might lead some to believe there are flaws within the data, analytics tools or underlying ML models, but that’s not the case. At Plotly, we know from experience that failures to launch typically stem from an inability to bridge model outputs with real business or organizational next steps.
As the statistic suggests, this is a major sticking point across our industry. To help navigate this roadblock, we’ve pulled together our learnings from work we’ve done with our clients over the years and what we’ve heard from the Plotly community. It consistently comes back to three main points — if you get these right, you’re on your way to ensuring your projects will have real business impact each time they are developed.

  • Ask the right questions
  • Build the right UI
  • Define the structure for operationalizing your application

2020-02-03 17:10:44.286000+00:00 Read the full story…
Weighted Interest Score: 2.4703, Raw Interest Score: 1.2586,
Positive Sentiment: 0.2517, Negative Sentiment 0.1831

Does Google Cloud Stand A Chance Against AWS?

Google’s cloud computing business may have a hit $10 billion annual revenue run rate with the fourth-quarter 2019 earnings, but is it on course to topple Amazon in the enterprise cloud war? With Google’s parent company, Alphabet Inc’s showing worst fourth-quarter revenue growth since 2015, the company will need to aggressively pursue more growth opportunities and strengthen its capabilities within this vertical if it wants to have a fighting chance.
A marked shift from typical financial disclosures, the company published its Q4 results on a minute basis, including fresh data on Google Search, YouTube and Cloud. The growth in cloud, which has been up 53% year-over-year, was driven by Google Cloud Platform (GCP) and brought in $2.6 billion, up from $1.7 billion in the year before. By comparison, Amazon Web Services (AWS) brought in nearly $10 billion in sales in its quarterly results, hitting a $40 billion annual run rate, which is four times the projection for Google.
2020-02-05 05:42:17+00:00 Read the full story…
Weighted Interest Score: 2.2682, Raw Interest Score: 1.4676,
Positive Sentiment: 0.2224, Negative Sentiment 0.0445

Google Faces Fresh EU Probe Over User Location Data

Alphabet-owned Google (GOOGL) – Get Report is facing a fresh probe in the European Union over concerns the company’s collection and processing of user location data through its various programs and apps could be violating the region’s stringent privacy rules. In a statement on Tuesday, the Irish Data Protection Commission said the Mountain View, Calif.-based search-engine giant was being questioned about “the legality of Google’s processing of location data and the transparency surrounding that processing.”
Of concern to regulators is whether Google and other companies that offer location-tracking apps such as mapping and other functions are collecting and analyzing more than just individuals’ locations, such as information about their shopping and commuting habits, sexual orientation and even political affiliations.

2020-02-04 06:47:33-05:00 Read the full story…
Weighted Interest Score: 2.1696, Raw Interest Score: 1.5779,
Positive Sentiment: 0.1578, Negative Sentiment 0.4734

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