McKinsey Updates Estimates of Big Data Potential Value

McKinsey_BigDataPotential2011.png

Source: McKinsey, 2011

[In 2011], we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards.

Source: McKinsey, 2016

Posted in Big Data Analytics | Tagged | Leave a comment

Acquisitions and Investments by Media Companies Since 2010

 

acquisitions-tv

Investments and acquisitions by Verizon, Comcast, Discovery, Time Warner, and Disney

acquisitions_publishing

Investments and acquisitions by Hearst, tronc, The Washington Post Company, News Corp., The New York Times Company, and Gannett

acquisitions_social

Investments and acquisitions by Facebook, Google, Twitter, and Snapchat

Source: Columbia Journalism Review

Posted in Misc | Tagged | Leave a comment

The Deep Learning Market: $1.7 Billion in 2022

deeplearning_investments

MarketsAndMarkets:

The deep learning market is expected to be worth $1,722.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The deep learning market has a huge potential across various industries such as advertisement, finance, and automotive. The major factors driving the deep learning market globally are the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning.

The market for the data mining application is expected to grow at the highest rate between 2016 and 2022

The deep learning market for data mining application is expected to grow at the highest CAGR between 2016 and 2022. The increasing usage of deep learning in data analytics, cyber security, fraud detection, and database systems is fueling the growth of data mining applications in the deep learning market. Medical industries generate huge amounts of data sets related to medication, patient details, and diagnosis. This data is converted into valuable patterns and is used to forecast future trends. Thus, data mining is expected to witness the highest growth rate in the medical industry.

Deep learning hardware market expected to grow at the highest rate between 2016 and 2022

The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others.

North America leads the deep learning market in terms of market size

North America is currently leading the deep learning market and is projected to be in the leading position for the next few years owing to the wide adoption of deep learning technology. The growth of the deep learning market in North America is attributed to the high government funding, presence of leading players, and strong technical base.

Posted in deep learning | Tagged | Leave a comment

Augmented Intelligence (#AI)

 

Posted in AI | Leave a comment

Look Ma, No Drivers: Transportation Automation

futurism_autonomous

Posted in Misc | Tagged | Leave a comment

Fake News on Social Media: What Difference Does it Make?

pew_socialmedia_modify-views

Pew Research Center:

Among social media users, Democrats – and liberal Democrats in particular – are a bit more likely than Republicans to say they have ever modified their views on a social or political issue, or on a particular political candidate, because of something they saw on social media. (Democrats and Republicans include independents and nonpartisans who “lean” toward these parties.)…

…the majority of social media users are not swayed by what they see in their networks. Some 82% of social media users say they have never modified their views on a particular candidate – and 79% say they have never changed their views on a social or political issue – because of something they saw on social media.

Posted in Misc | Tagged | Leave a comment

What is Machine Learning

futurism-machine-learning

Posted in Machine Learning | Tagged | Leave a comment

Online Shopping on Twitter

ecommerce_holiday_2016_infographic

Posted in Misc | Leave a comment

?The internet is living up to its potential as a major source for news about the presidential campaign

election_2008

Pew Internet: January 11, 2008

The internet is living up to its potential as a major source for news about the presidential campaign. Nearly a quarter of Americans (24%) say they regularly learn something about the campaign from the internet, almost double the percentage from a comparable point in the 2004 campaign (13%).

Posted in Misc | Tagged | Leave a comment

Artificial Intelligence: 2017 Predictions from Forrester

forresterfigure-1-biz-insights Insights matter. Businesses that use artificial intelligence (AI), big data and the Internet of Things (IoT) technologies to uncover new business insights “will steal $1.2 trillion per annum from their less informed peers by 2020.” So says Forrester in a new report, “Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution.”

Across all businesses, there will be a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. Through the use of cognitive interfaces into complex systems, advanced analytics, and machine learning technology, AI will provide business users access to powerful insights never before available to them. It will help, says Forrester, “drive faster business decisions in marketing, ecommerce, product management and other areas of the business by helping close the gap from insights to action.”

The combination of AI, Big data, and IoT technologies will enable businesses investing in them and implementing them successfully to overcome barriers to data access and to mining useful insights. In 2017 these technologies will increase business’ access to data, broaden the types of data that can be analyzed, and raise the level of sophistication of the resulting insight. As a result, Forrester predicts an acceleration in the trend towards democratization of data analysis. While in 2015 it found that only 51% of data and analytics decision-makers said that they were able to easily obtain data and analyze it without the help of technologist, Forrester expects this figure to rise to around 66% in 2017.

Big data technologies will mature and vendors will increasingly integrate them with their traditional analytics platforms which will facilitate their incorporation in existing analytics processes in a wide range of organizations. The use of a single architecture for big data convergence with agile and actionable insights will become more widespread.

The third set of technologies supporting insight-driven businesses, those associated with IoT, will also become integrated with more traditional analytics offerings and Forrester expects the number of digital analytics vendors offering IoT insights capabilities to double in 2017. This will encourage their customers to invest in networking more devices and exploring the data they produce. For example, Forrester has found that 67% of telecommunications decision-makers are considering or prioritizing developing IoT or M2M initiatives in 2017.

The increased investment in IoT will lead to new type of analytics which in turn will lead to new business insights. Currently, much of the data that is generated by edge devices such as mobile phones, wearables, or cars, goes unused as “immature data and analytics practices cause most firms to squander these insights opportunities,” says Forrester. In 2016, less than 50% of data and analytics decision-makers have adopted location analytics, but Forrester expects the adoption of location analytics will grow to over two-thirds of businesses by the end of 2017.  The resulting new insights will enable firms to optimize their customers’ experiences as they engage in the physical world with products, services and support.

In general, Forrester sees encouraging signs that more companies are investing in initiatives to get rid of existing silos of customer knowledge so they can coordinate better and drive insights throughout the entire enterprise. Specifically, Forrester sees three such initiatives becoming prominent in 2017:

Organizations with Chief Data Officers (CDOs) will become the majority in 2017, up from a global average of 47% in 2016. But to become truly insights-driven, says Forrester, “firms must eventually assign data responsibilities to CIOs and CMOs, and even CEOs, in order to drive swift business action based on data driven insights.”

Customer data management projects will increase by 75%. In 2016, for the first time, 39% of organizations have embarked on a big data initiative to support cross-channel tracking and attribution, customer journey analytics, and better segmentation. And nearly one-third indicated plans to adopt big data technologies and solutions in the next twelve months.

Forrester expects to see a marked increase in the adoption of enterprise-wide insights-driven practices as firms digitally transform their business in 2017. Leading customer intelligence practices and strategies will become “the poster child for business transformation,” says Forrester.

Longer term, according to Forrester’s “The Top Emerging Technologies To Watch: 2017 To 2021,” Artificial intelligence-based services and applications will eventually change most industries and redistribute the workforce.

Originally published on Forbes.com

Posted in AI, Misc, Predictions | Leave a comment