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WHY IT MATTERS: Digital Transformation
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Curated by
Farid Mheir
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To succeed in the digital age, mall operators will need to instill a culture of fact-based decision making throughout the organization. In addition to implementing advanced-analytics tools, they should invest in collecting more of the valuable data that will inform their business decisions. For instance, they can deploy new technologies (such as beacons, granular Wi-Fi, and facial-recognition cameras) to capture behavioral data. They can launch mallwide loyalty programs to gather individual transaction data and generate insights into the customer journey across the entire mall ecosystem. They can also pursue partnerships with tenants—for instance, by negotiating preferred rents in exchange for data sharing. Armed with robust data and advanced analytics tools, malls have the potential to revitalize and revolutionize not just their own business performance but that of the rest of the retail industry as well.
Farid Mheir's insight:
WHY IT MATTERS: mall owners - and other large property space owners such as cities - should invest in technology that they can resell or leverage to be more effective and valuable to their retail tenants. The article explains some ways to do it. 
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Lors de la soirée du Débat des chefs, bien des choses ont été dites sur Internet, par les électeurs qui suivaient le débat, par les analystes, les sympathisants et même, les partis eux-mêmes. Voici une analyse de ces propos produites par les gens de Semeon Analytics pour chacun des chefs présent lors du débat de jeudi…
Farid Mheir's insight:
WHY IT MATTERS: big data and AI can extract the information from social networks - this is a perfect example of the use of such tools to analyze the sentiments towards politicians during debates in Quebec province.
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The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. I’ve written about the Analytics of Things (AoT) before (some examples here, here, and here). For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.
Farid Mheir's insight:
WHY IT MATTERS: as I work with colleagues to bring IOT into retail stores, challenges are many including those highlighted here.
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Over 1 billion activities, 13 trillion data points create the ultimate map of athlete playgrounds.
Farid Mheir's insight:
WHY IT MATTERS: IOT and wearables are giving us a whole new way to see our world. Here, Strava fitness app has built a new world map to show where people go to play and exercise.
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Google has gathered so much data, in so many areas, that it’s now crunching it together and creating features that Apple can’t make—surrounding Google Maps with a moat of time.
Farid Mheir's insight:
WHY IT MATTERS: Google has been capturing and translating data for a very long time and by doing so it can provide digital services that way better than anything else their competitors can do. This example of maps is just one in many but this one is particularly well documented. Kudos.
EntreCulturas's curator insight,
January 4, 2018 11:02 AM
En este estudio sobre las apps de geolocalizacion como Google Maps o Apple Maps, se muestra el mayor desarrollo de la primera. Google se ha actualizado principalmente permitiendo el acceso exacto al edificio concreto de una calle. Se puede ver clickando ,encima, una descripción detallada de la ubicación con opción de encontrar imágenes del interior.
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Disruptive technologies are transforming all end-to-end steps in production and business models in most sectors of the economy. The products that consumers demand, factory processes and footprints, and the management of global supply chains are being re-shaped to an unprecedented degree and at unprecedented pace. Industry leaders who were consulted believe that new technological solutions heralded by the Fourth Industrial Revolution – such as advanced robotics, autonomous systems and additive manufacturing – will revolutionize traditional ways of creating value. As the costs of deploying technology continue to fall, international differentials in labour costs will no longer be a decisive factor in choosing the location of production. The resulting greater spatial and temporal flexibility brought about by technology will bring locations of production and sale closer together, and drive major changes in the design of future value and supply chains. These trends will change the shape and form of globalization, and thereby impact the trajectory of goods. Regional and local flows will become more important, to the detriment of intercontinental trade.
Farid Mheir's insight:
WHY IT MATTERS: the world economic forum keeps putting out great reports on the impact of digital transformation. I love the illustrations, great summary of key trends and their adoption. Must read.
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IBM data scientists break big data into four dimensions: volume, variety, velocity and veracity. This infographic explains and gives examples of each.
Farid Mheir's insight:
WHY IT MATTERS Big Data is based on 4 key characteristics that have driven its adoption in recent years.
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From
redash
Use Redash to connect to any data source (Redshift, BigQuery, MySQL, PostgreSQL, MongoDB and many others), query, visualize and share your data to make your company data driven.
Farid Mheir's insight:
WHY IT MATTERS Everyone is looking at data and very often excel does not provide a scalable solution - especially when looking to build dashboard that everyone uses. Here is one: redash.io. For Google users looking for a free solution, consider DataStudio.
tavaresandro's curator insight,
October 5, 2017 12:57 AM
WHY IT MATTERS Everyone is looking at data and very often excel does not provide a scalable solution - especially when looking to build dashboard that everyone uses. Here is one: redash.io. For Google users looking for a free solution, consider DataStudio.
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Instacart is excited to announce our first public dataset release, “The Instacart Online Grocery Shopping Dataset 2017”. This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders.
Farid Mheir's insight:
WHY THIS IS IMPORTANT In today's eCommerce world, winners do not limit themselves to building great catalogs and payment processing experience. They actively analyze data to understand customer behaviour, to provide them with exceptional information to speed-up their shopping experience, but also maybe make them buy more or more expensive products. This is a great example of such a data analysis by grocery shopping leader instacart. It provides amazing insights into grocery shopping but also has open sourced the data for others to benefit.
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100 million rides and runs, 220 billion data points visualizing the best roads and trails worldwide.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Big brother is watching and when it does you can gain tremendous insights into the behaviour of humans. I zoomed in on Montreal and the diagram is very clear about which run trails are most popular on the Mount Royal. On the dark side, of course, this means Strava performs some high level surveillance on us as we share our bike and run rides with their software.
Eric Hunter's curator insight,
March 27, 2017 11:43 AM
Taking this beyond just being really cool, into value creating is the next challenge. Great #dataviz work!
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Farid Mheir's insight:
WHY THIS IS IMPORTANT Based on a detailed report, this article provides a chockful of stats about investments in certain technologies. Although based on a survey, it confirms certain trends that are widely accepted - so there should be no surprises for readers of this blog.
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Only Facebook could create Safety Check, not because of resources as you might expect, but because Facebooks lets employees build crazy things like Safety Check and because only Facebook has 1.5 billion geographically distributed users, with a degree of separation between them of only 4.74 edges, and only Facebook has users who are fanatical about reading their news feeds. A small team couldn’t build a big pipeline and index, so they wrote some hacky PHP and effectively got the job done at scale. This paper details how Facebook build Safety Check
Farid Mheir's insight:
Essential read on this new technology from Facebook that was made possible because of the social graph that Facebook builds. Impressive if only because the solution was easy to build.
Also read the companion article for a high-level description rather than this detailed one: http://highscalability.com/blog/2015/11/14/how-facebooks-safety-check-works.html
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Training materials obtained by the Electronic Privacy Information Center show Palantir plays a role in a far-reaching customs system
Farid Mheir's insight:
Just follow the link to the 2012 report and find out what it means to cross reference different digital databases.
WHY THIS IS IMPORTANT Our lives are digital and we leave digital exhaust behind us everywhere we go. This paper shows what the US government uses to put together a composite picture of your digital twin. For now, they plan to use this for border crossing but in the future - 10 or 20 years down the road - what will this info be used for? Also, knowing that military and very advanced technology always makes it into the corporate world, how will this be used by corporations?
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From
hbr
Now a new breed of software applications is reshaping sales force management. Their common characteristic: Using digital data exhaust, which is the data generated from the regular activities of a sales force or their customers, to change the behaviour of frontline sales representatives in ways that dramatically improve sales productivity and effectiveness.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Daily activities leave digital traces that can be leveraged to improve processes and deliver value in organizations. We often overlook this data in organizations but we should realize that companies such as Facebook, Google, Apple and others have invested huge amounts of efforts to dig into the data exhaust of our digital and physical world activities in order to extract, infer, predict our tastes and behaviour. If it works for them, it should work for us as well, no?
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Onfido delivers next-generation background checks, helping the world’s most innovative businesses verify anyone, anywhere.
Farid Mheir's insight:
AI and Big Data are being applied to identity verification by a startup that has received 25M in funding and is poaching employees from Google.
WHY THIS IS IMPORTANT AI and other techniques will provide enhance capabilities to deter theft and privacy violations. They will become essential tools in digital transformation of businesses. For example, UBER is using Onfido to verify that people are who they claim they are, thus making their service better able to curb credit card theft, and protect drivers by ensuring their passengers can be tracked is they behave inappropriately.
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In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”. While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014. This was the period when, at least in the press and on industry panels, Big Data was the new “black”, “gold” or “oil”. However, at least in my conversations with people in the industry, there’s an increasing sense of having reached some kind of plateau. 2015 was probably the year when the cool kids in the data world (to the extent there is such a thing) moved on to obsessing over AI and its many related concepts and flavors: machine intelligence, deep learning, etc.
Farid Mheir's insight:
A chart that shows the number of companies that offer products and services in the big data space.
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A team of scientists has developed an algorithm that captures human learning abilities, enabling computers to recognize and draw simple visual concepts that are mostly indistinguishable from those created by humans. Via Wildcat2030
Farid Mheir's insight:
Review of work done to make computers learn like humans, the experiment shows that new algorithms can learn from very large sets of images - 60 000 examples to learn how to draw digits 0 to 9.
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Matrices, in linear algebra, are simply rectangular arrays of numbers, a collection of scalar values between brackets, like a spreadsheet. All square matrices (e.g. 2 x 2 or 3 x 3) have eigenvectors, and they have a very special relationship with them, a bit like Germans have with their cars.
Farid Mheir's insight:
An explanation of some key mathematical concepts required to make deep learning possible. WHY IS THIS IMPORTANT Behind what looks like "magic" on Google search, google translate or IBM Watson lies mathematics algorithm that process large amount of data. This is essential information to understand how digital computing, big data and deep learning works.
Nur Hanan's curator insight,
October 1, 2021 10:32 AM
Ringkasan Artikel ini berisi tentang pembahasan vektor eigen dari sebuah matriks bujur sangkar (square matrix) , misalnya matriks bujur sangkar berukuran 2 x 2 atau 3 x 3 . Pembahasan vektor eigen ini untuk menjelaskan kovarians, analisis komponen utama, dan entropi informasi dengan bahasa sederhana dan tanpa banyak matematika. Manfaat konten artikel bagi saya : Artikel ini memberikan informasi baru nagi saya tentang penggunaan konsep vektor eigen untuk menjelaskan tentang kovaroansi, analisis Komponen utama, dan entropi informasi, karena saat ini saya sedang mengampuh mata kuliah yang salah satu materinya adalah konsep vektor eigen yang kajiannya hanya sebatas matematikanya tanpa mengaitkan dengan kajian bidang ilmu yang lainnya.
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I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.
Farid Mheir's insight:
Jump over to the section on Big Data, and you'll get a great description of where Big Data will likely fail on the near future: making predictions. As a good engineer, Jordan states a fact about Big Data that often goes unnoticed: Big Data today is like gambling because it is not based on a formal scientific approach. Rather, he states quite clearly that Big Data today is like building bridges prior to civil engineering: you can build a bridge but cannot GUARANTEE it will not fail. Same with Big Data: we can analyze a lot of data and make predictions but we cannot guarantee they will become reality. (or read the entire piece, it will enlighten you on a typical engineering discussion on new technology: dry, cold, fact-based. Much to the opposite of everyday marketing and scientific media interpretation.)
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In this session we will dive into some of the use-cases companies are currently deploying MongoDB for in the energy space. It is becoming more important for ...
Farid Mheir's insight:
I find useful when mongoDB provides use cases for its tools. Great to hear you can do big things with big data. Better when someone actually provides pointers to good scenarios where to apply it.
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Curated by Farid Mheir
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