BI Tools News alert April 2019 | Computer vision is hot

BI Tools News: computer vision is hot

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BI Tools News Alert April 2019

Software developers and analysts are experts at coming up with new acronyms and exotic-sounding names for their platforms, BI tools, features, plug-ins, and add-ons. NLG, VBD, MOLAP, BIaaS, federated analytics, augmented intelligence, the Prep Conductor, Vizzes, smart analytics, the list goes on. End-users have the unenviable task of looking past the jargon and trying to judge all new announcements on their own merits. A critical attitude, focusing on the promised functionality, will get you far.

And that’s exactly what Passionned Group, publisher of the popular Business Intelligence Tools Survey 2019, does. We’ll walk you through a selection of the most interesting announcements made by BI vendors over the last two months. This is the second installment of our BI Tools News Alert.

Automated machine learning

Qlik started the second quarter pragmatically by signing a co-operative agreement with the Boston-based DataRobot, which specializes in automated machine learning models. Thanks to DataRobot’s expert system, customers have the opportunity to base their decisions on predictive data within analysis workflows. Users can then perform the entire range of data analyses (historical, current, and predictive) directly from the Qlik interface. The co-operation is in line with Qlik’s ambitions when it comes to augmented intelligence and machine learning.

Massive parallel processing

Using the Qlik open source extension and connector, users can prepare data with key fields and send it straight to DataRobot from Qlik Sense. They can also combine data from various tables within the associative data model and process it in DataRobot immediately. Finally, users can manage DataRobot projects in Qlik by sending models straight to the Qlik application. Thanks to this integration, predictive modeling and remodeling is possible in one interface, so that the user always has access to the best possible analysis model. The DataRobot platform uses massive parallel processing (MPP) to train and evaluate thousands of models based on programming languages like R (training) and Python, the Spark MLlib, H20, and other open source libraries.

Data for Good

Last month, SAS showed it doesn’t just have a business side, but a social side too. In the spirit of the Special Olympics World Games 2019 in Abu Dhabi, the software vendor provided worldwide data analytics for charity as part of the so-called Data for Good program. The software vendor made its platform for Data Management, Artificial Intelligence, and Machine Learning available to the Games’ local organization committee. This allowed large volumes of (big) data about the performances, health, and location of the athletes to be analyzed from various sources. The committee could use this to monitor the location and heart rate of the athletes in real time, and call in medical help if necessary.

The data was also used for medical research in order to gain new insight into behavior, which can contribute to the social integration of people with intellectual disabilities. An international team of volunteer data scientists were on stand-by during the Games. With 7500 athletes and 3000 coaches from over 190 countries, the Special Olympics World Games 2019 was the largest sporting event in the world for people with intellectual disabilities.

Computer vision takes off

After the Games, we quickly went back to business. We can tell that SAS has deep pockets from the fact that the company plans to invest a billion dollars into artificial intelligence over the next three years. The money will be spent on software innovation, education, offering expert services, and more. This allows the software vendor to build on their self-professed strong AI foundation, including advanced analytics, machine learning, deep learning, natural language processing (NLP) and computer vision. Computer vision is all the rage these days.

In the wake of its mega-investment in AI, SAS announced that it will work together with NVIDIA, a leading manufacturer of graphics cards and chips. The two companies will work together closely in the fields of machine learning, computer vision, and NLP. Nvidia’s GPUs and so-called CUDA-X AI acceleration libraries offer support for the core components of SAS’ AI solutions.

Data-driven football

SciSports, a Dutch startup in the field of sports analytics, is already applying SAS’ computer vision to data streaming from football matches. The software runs on NVIDIA GPUs and provides in-game insights for coaches and (interim) managers. By recording and analyzing this data, football clubs can improve many aspects of the game, like the in-game strategy, player scouting, and the fans’ experience. Computer vision is taking off in other sectors, too. Using AI solutions, medical professionals may be able to use object recognition to distinguish between benign and malignant cancer cells. Manufacturers can detect defects or mistakes during the production process, and financial institutions could save billions of euros and dollars using fraud detection.

In search of a holistic perspective

The Australian Yellowfin announced the release of Yellowfin 8.0.1. They claim that this new release offers significant improvements to usability, performance, and management for customers and partners, so that they can more quickly and securely manage insights and act on them. Yellowfin Signals has been improved with the addition of Signal Actions. The vendor claims these Actions provide better governance of the automated insights generated by Yellowfin Signals. Actions enables users to assign owners to Signals, who are then expected to take action. Finally, users can share the signals with others for analysis purposes.

Automatic color-coding

Yellowfin also added automatic color-coding to the signals, so that they can easily be identified as positive or negative changes to the business. This means, for example, an increase in customer retention would be marked as a positive change with blue, instead of orange. The most conspicuous addition to Yellowfin Stories is a new plugin framework which enables integration with other platforms, including embedding live and interactive reports from Qlik, Tableau, and Microsoft Power BI. This allows organizations to tell a story about the data inside the various BI and analysis tools, creating a more holistic insight into the business.

Another new functionality is the option to generate and edit reports from stories, so that the user workflow is completely integrated. Users can consult the Patch and Release notes in the user community, or watch the video demo.

The ultimate data platform?

The American company TIBCO software, owners of Jaspersoft since 2014 and Spotfire since 2017, is announcing its takeover of SnappyData. The Portland-based SnappyData is a startup founded in 2016. The company offers a so-called high-performance in-memory data platform based on Apache Spark, an engine for processing big data. TIBCO Software means to supplement their own so-called Connected Intelligence platform and intertwine it with SnappyData’s data platform, creating what should be the ultimate data platform, or so they say.

The goal is to lift analytics, data science, streaming, and data management to a higher level. Better intervals for data refreshing, faster query response times, and higher productivity are just some of the advantages the vendor is promising its clients. They claim that the combination of platforms will offer an overal performance increase of a factor twenty (!) compared to the so-called native Apache Spark implementations.

Predicting aberrant patterns

The “ultimate data platform” is ideally suited (according to TIBCO and SnappyData, that is) to delivering insights in real-time, predicting maintenance, and discovering aberrant patterns, among other things. Use cases aplenty in the Internet of Things and in vertical markets such as manufacturing, financial services, and telecom. TIBCO claims that the takeover makes the company completely compatible with the open-source Apache Spark ecosystem, which they have committed to. How much was paid for the takeover is unknown.

Searching like Google

Finally, Thoughtspot, specialist in search queries and AI-driven analytics, announced a partnership with Alteryx, provider of an end-to-end data science & analytics platform. Both partners have the goal of simplifying the complete data pipeline for business users, from data preparation to generating insights. To this end, they developed special tools, enabling Alteryx users to integrate so-called native ThoughtSpot Bulk Loader connections and ThoughtSpot TQL statements directly into their workflow. Thanks to the time gained by quick, parallel data loaders, end users can start digging into the automatically-generated insights faster. They can also build AI and machine learning models within a code-free environment.

The partners claim that every user, regardless of their technical skill, can easily acquire analytical insights based on natural language queries in speech. They’ll get an answer within seconds. The experience and response speed should be comparable to a Google search query. ThoughtSpot was crowned leader in Gartner’s 2019 Magic Quadrant for Analytics and Business Intelligence Platforms alongside Microsoft, Qlik, and Tableau.

More information

Passionned Group is the publisher of the Business Intelligence Tools Survey 2019, a 100% vendor-independent comparison report and market analysis. This survey will provide direct insight into the pros and cons of the various solutions and vendors. It will save you precious time by providing a complete overview of the market, allowing you to choose a BI platform that suits your needs. This report is a must for the modern Business Intelligence consultant. The BI Tools Survey is available in various editions and can be ordered online.

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