Forrester and IDC on Consumer Interest in IoT

Figure 5 - Forrester

IDC-IoTAPril2016 Key Takeaways from IDCs 2016 Consumer IoT Webinar_08

IoT-Vs-Industrial

Consumer IoT is lagging behind industrial IoT in terms of interest, investments, and successful applications. CB Insights has found that in 2011, the industrial IoT accounted for 17% of all IoT funding dollars. In 2015, the share of industrial IoT investment rose to 40% of all IoT investment.

In a recent report citing the results of large surveys of consumers in the U.S. and other countries, Forrester observed that the IoT today “seems to open up more business opportunities in the industrial and B2B space than for consumer brands” (see also Forrester’s blog post). Similarly, in a recent report and webinar based on a large consumer survey in the U.S., IDC has concluded that “beyond security and point-solutions to specific problems, consumer IoT is still looking for a clear value proposition.”

Here are some interesting findings:

14% of U.S. online adults are currently using a wearable, and only 7% use any smart home device. Usage of connected devices in smart homes or cars is even lower in Europe. Smoke and home security monitoring are the two smart home services U.S. consumers are the most interested in, followed closely by water monitoring. (Forrester)

More than 8 million households in the U.S. already use some kind of home automation and control. The home IoT applications consumers are interested in are networked sensors monitoring for fire, smoke, water, or CO at home; seeing and recording who comes to the front door using a video camera; and networked sensors monitoring doors and windows. Consumers are least interested in networked kitchen appliances. (IDC)

Reasons for purchasing home control application: 30% cited solving a known problem, either recent or long-standing; 40% cited word-of- mouth, news about such devices; almost 20% said it seemed like “a neat solution to a problem I didn’t know I had” (!!!) and over 15% said that the device “was on sale.” (IDC)

Preferred installer for home automation and control systems in order of preference: Residential security company, myself, other professional installers, cable or telephone companies. (IDC)

Half of U.S. online adults are concerned that the monthly service cost of smart home technologies would be too high, and 38% fear the initial cost of setup would be too high. (Forrester)

36% of U.S. online adults fear using smart home services could compromise the privacy of their personal information. (Forrester)

Among those interested in home control IoT application but haven’t purchased one: High concern around cost (which is common for new applications) and unusually (for new applications) high concerns around reliability and user experience. (IDC)

31% are interested in access to the internet while using the car (i.e., on-board internet) and access to an interactive voice response system (i.e., a digital driving assistant). Telematics-enabled usage based insurance (UBI) is emerging and will disrupt the car insurance industry. (Forrester)

In 2016, 33% of U.S. online adults will use some form of IoT across home, wearables, and car. However, usage in the next two years will primarily be led by wearables and smartwatches. (Forrester)

IDC concludes that “the majority of consumers remain skeptical of the value proposition behind the home Internet of Things and are holding back for a higher overall value proposition.” In the IDC press release, Jonathan Gaw said:

“The long-run impact of the Internet of Things will be broader and deeper than we imagine right now, but the industry is still in the early stages of developing the vision and conveying it to consumers.”

IDC continues: “Winners will solve a problem the consumer didn’t know they had. Security and privacy – punished for a lack of it, probably not rewarded for having it. Voice interfaces have potential, but still need development for mainstream users.”

Forrester’s Thomas Husson and his colleagues cite a pioneering home-focused voice interface, Amazon Echo, as an example of successful consumer IoT device. “Combining the Dash Buttons’ big-data-meets-internet-of-things experiment with Amazon Echo and Alexa Voice Assistant will enable Amazon to aggregate multiple brands’ offering and anticipate consumers’ needs,” says Forrester.

The report continues: “Because consumers invest little in new experiences, they hurt little when abandoned. The consequence is that the vast majority of new IoT products will fail unless marketers develop a customer relationship that is frequent, emotionally engaging, and conveniently delivered.”

Both Forrester and IDC seems to understand what works and what doesn’t work with current IoT offerings and continue to advance their knowledge by surveying consumers and talking to enterprise decision-makers.

The U.S. government may want to pay closer attention to their (and other industry observers’) work. The National Telecommunications and Information Administration (NTIA) recently issued a request for comments which included this gem:

Although a number of architectures describing different aspects or various applications of the IoT are being developed, there is no broad consensus on exactly how the concept should be defined or scoped. Consensus has emerged, however, that the number of connected devices is expected to grow exponentially, and the economic impact of those devices will increase dramatically.

To which James Connolly responded: “How can the public comment when even Commerce can’t really define the term?”

Originally published on Forbes.com

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Why Enterprises Use AI and for What

AI_most-widely-used-solutions

HT: Raconteur

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How Much Time the World Spends Looking at Screens (Infographic)

ScreenMinutes_global

Gizmodo

Ever wondered how much time the average person spends looking at their TV, computer, phone or laptop? Well, this chart shows exactly that, broken down by country.

Produced by Mary Meeker for her annual presentation on internet trends, the chart reveals some interesting insights. Clearly Indonesia and the Philippines are glued to their screens, but it’s the breakdown where it get interesting. Look at the disparity in TV viewing between the U.S. and Vietnam, say, despite their similar totals; or the lack of tablet time in South Korea. (But then, maybe that’s because Samsung tablet suck.) And Italy and France barely spend any time at a screen—but then, maybe that’s what happens if you ban email after 6pm. [KPCB via Quartz]

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Nearly 50% of CEOs believe all of their employees have access to the data they need, only 27% of employees agree

 

Bigdata_teradatasurvey

Source: Teradata and EIU

RTBlog:

Nearly half of CEOs believe that all of their employees have access to the data they need, but only 27% of employees agree.

That’s according to study results from Teradata, a data analytics and marketing firm. The company commissioned The Economist Intelligence Unit to survey 362 workers across the globe — including those in management, finance, sales and marketing, business development and more.

CEOs also overestimate how quickly “big data” moves through their company, with 43% of CEO respondents believing that relevant data is made available in real-time, compared to 29% of all respondents.

Overall, CEOs are wearing rose-colored glasses when examining the overall effectiveness big data has on their initiatives: 38% believe their employees are able to extract relevant insights from the data, while only 24% of all respondents do.

The report notes that of companies that outperform in profitability as a result of data-driven marketing, 63% of the initiatives are launched by corporate leadership, and 41% have a centralized data and analytics group. Of companies that say they underperform, 38% of initiatives are launched by the higher-ups and 28% say data and analytics are centralized.

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Gartner Marketing Technology Map May 2016

marketing_technology_may2016

Kirsten Newbold-Knipp, Gartner:

Here are a few highlights from some of our 2016 marketing cool vendors reports as well as guidance on technology selection.

  • Cool Vendors in Content Marketing: As content marketing grows up from its early tactical success to become a scalable program, marketers need to expand their content pipeline with high quality results. The vendors highlighted in this year’s research all heed that call: Seenit supports UGC video, Canva and Visage extend graphic design capabilities and Cintell helps increase content relevance by making personas more actionable.
  • Cool Vendors in Digital Commerce Marketing: Marketers need to enhance and evolve their digital commerce marketing to create compelling shopping experiences. Both Edgecase and Reflektion make shopping more informative and relevant, while ChannelSight powers distributed commerce and shoppable media to expand marketers’ addressable audience. Two players make it easier to merchandising complex products – Marxent uses virtual reality to bring products and environments to life, while True Fit takes the guesswork out of sizing shoes and clothing online.
  • Cool Vendors in Mobile Marketing: Understanding how consumers use mobile devices to engage online and offline is key to an effective mobile marketing strategy. Location based marketing is top of mind. Bluefox helps retailers and brands engage in location based personalization – without the need for an app, NinthDecimal supports location related insights with a focus on online/offline attribution and Gravy provides location informed behavioral analytics by tracking attendance at live events. Yext, serves the flip side of location, helping businesses maintain their physical-world information across online directories and listings. Marfeel – the only non-location based player in this year’s lineup – provides tools to design native mobile-optimized pages that load faster and create better end-user experiences.
  • Cool Vendors in Social Marketing: Social marketers are looking for new ways leverage the audiences they’ve built over years – from activation to insights – especially now that advertising dominates organic reach in social. On the activation front, Ahalogy helps clients drive sales through pay-for-performance via Pinterest, Chirpify offers awards in return for social engagement that builds out richer customer profiles and Mavrck uses the concept of microinfluence to drive scalable word-of-mouth efforts. Using social data for customer insights, Hyperactivate lets brands track and understand which individuals create the most campaign impact whereas Pixability helps marketers track and optimize their YouTube advertising efforts.
  • Framework for Choosing Digital Marketing Technology (Gartner client access only): With so many cool vendors, you need to choose carefully to ensure that you get the most bang for your marketing buck. It’s vital that you don’t jeopardize your investment by looking too narrowly at a particular feature set or become restricted by unrealistic budget caps. You need to hone in on how the technology will help you achieve your marketing goals. Gartner’s framework will help you define and articulate the capabilities you need the tech to deliver. And it will guide you through the process of vendor selection, to ensure you get the tech that’s best for you.
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Buzzword Watch: What’s In and Out in Technology 2016

compTIA-buzzwords

ComTIA:

CompTIA evaluates trends for its IT Industry Outlook based on their recent or imminent impact. For developments that are just emerging, or trends that are still on under the radar, Buzzwords Watch provides a glimpse of terms that could gain traction. Of course, many will also fizzle out.

Note: CompTIA’s Buzzword Watch is not meant to be a formal, quantitative assessment of trends, but rather an informal look at interesting concepts that may be worth paying attention to in the year ahead.

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History of the Internet of Things (IoT)

Internet-of-things-history

Source: 

See also: A Very Short History Of The Internet Of Things

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A Day in the Life of a Data Scientist

DataScientist_dayinlife

HT: @NinjaEconomics

See also: Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey

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DMway Automates Predictive Analytics

Crystal-ball-predicitve-analyticsIn its most recent hype cycle for emerging technologies, Gartner introduced “citizen data science” and “advanced analytics with self-service delivery.” Both technologies were predicted to reach the “plateau of productivity” in 2 to 5 years before

The shortage of data scientists and the resulting high salaries they command is giving rise to new self-service tools, automating all stages of data science so business analysts, marketing managers, IT staff and others could perform advanced analytics as part of their jobs.

By 2017, Gartner says, the number of these citizen data scientists in small and large organizations will grow five times faster than the number of highly skilled data scientists. Forrester agrees that the “huge demand” for data scientists will not be met in the short term, “even as more degree programs launch globally.” And the demand for advanced data analysis will only increase in the coming years with the rise of the Internet of Things.

Automation also helps the few overworked data scientists available today, making the experienced more productive and helping the newly-minted add value faster.  A number of startups, such as Trifacta and Tamr, have focused on the early stages of the data analytics process—data preparation and transformation—and others have focused on later stages such as data visualization or on specific applications and industries.

An interesting challenge is automating the core of the data science process, the development and maintenance of predictive models (Forrester recently declared that Predictive Analytics is the hottest big data technology). The founders of DMway, which recently raised $1 million dollars in seed funding from JVP Labs, have “spent their entire careers on understanding and mapping the methods of algorithm and model developers,” says CEO Gil Nizri.

“Predictive analytics is a great competitive differentiator but it is still beyond the reach of most organizations,” adds Nizri.  “DMway is enabling any size company, from SMB to enterprise, to compete on a level playing field.”

DMway’s model building “mimics the way a human expert develops a model,” says CTO Ronen Meiri. It starts by exploring the data, searching through all potential predictors and selecting the most influential. Using the set of influential predictors it creates a final prediction model and then applies it to an independent dataset to check its accuracy and over-fitting, making sure the model is general enough to apply to new observations.  Finally, it provides multiple methods for seamless integration and deployment of the model.

The result is faster model development and more accurate models, sometimes 20% more accurate than traditionally-developed models. The benefits of automation, however, do not apply only to the initial development of the model. “Most of the resources are going to model maintenance and not to building the model for the first time,” says Meiri. “In micro-financing, for example, they usually re-build the model every three months.”

Businesses operating in environments with fast-changing conditions are prime candidates for automated model maintenance and a number of DMway’s early customers are Fintech startups. BACKED, providing loans to young Americans, uses DMway to predict loan defaults and Fido Credit, provider of micro-financing in Africa, uses DMway to assess credit risk.  Beyond the financial sector, DMway’s automated model development is used by the marketing department of YES, a Cable TV operator, to predict customer churn and facilitate lead conversion.

As Eric Siegel, founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, declaimed in a previous version of this rap video:

[youtube https://www.youtube.com/watch?v=bSP3z0LmWEg?rel=0]

Modeling means modifying models incrementally,

With a geek technique to tweak, it will reach the peak eventually.

Each step is taken to improve prediction on the training cases,

One small step for man; one giant leap—the human race is going places!

DMway is a good example of how automation is best discussed as human augmentation rather than human replacement, as it facilitates analyst-machine collaboration. The human race may indeed go places when data scientists—both of the highly skilled and of the “citizen” varieties—are supplied with tools that increase their productivity and the accuracy of models that drive decisions.

Originally published on Forbes.com

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IoT: The Explosion of Connected Things

[vimeo 94011734 w=640 h=360]

See also A Very Short History of the Internet of Things

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