Archive for July, 2012

Over view of M2M and Internet of Things (IoT) & issues being faced…

Machine-to-Machine (M2M) refers to technologies that allow both wireless and wired systems to communicate with other devices/systems of the same ability. M2M started with point solutions, created for one specific task. However, in the not too distant future we will get to a point where it is more common for devices to be connected than not. Then we get to the Internet of Things (IoT) where we might get a sharing of data across different sectors and between different devices in a way that wasn’t envisioned when M2M first came about.

The ROI for connected machines is rapidly expanding to a much wider market. E.g.

• Forecast for cellular M2M Connections range between 287 – 400 million connections by 2016 (Ref.: Pyramid research).

• Annual revenues from M2M services have been forecasted at US$35 billion by 2016 (Ref.: Juniper Research).

Both traditional vertical market applications and new cross-sector services are likely to exist, based on a data rich environment. These will vary immensely between the enterprise, or B2B, and B2B2C worlds. Connected homes and connected cars provide current early examples of the direction this is heading towards.

The Internet of Things is about utilizing data from billions of connected devices – the value is in the data. In order for this to happen, much more is required to get these devices to connect seamlessly. Getting billions of devices connected easily and cost-effectively in a way that allows interoperability is critical and does not yet exist. Evidence of this lack includes the high return rates (up to 90%) for home alarm and control products. M2M platforms are one key to solving this challenge.

Storing consumer data in the Internet of Things will create new security issues involving personal data and its acceptance quite different from those related to enterprise data storage. Educating consumers about the security and privacy of data – and its benefits – is and will be increasingly important. The value of stored data highlights key differences between B2B/enterprise and consumer behavior.

Facebook, where users consciously share information with others, provides a possible device model – set up your devices and tell each what you’re willing to let it do. Data ownership then becomes an issue. Who owns the device, who owns the data and how far can you share it? Also, how do I opt-in or opt-out? This is a business issue as well, making the right tools available so that the information can be shared in a secure way.

Scaling up and collecting massive amounts of data is a major challenge. There is a need, though, not to get too far ahead of the reality of the way people see value coming from applications. For example, automobile manufacturers had only a few people managing connected cars a short time ago; now hundreds are involved, across multiple departments. E.g. in case of a car crash, car sends data like location and impact of car crash data, to nearest fire station, police station, hospital etc. That creates a major challenge that must be resolved before creating new business models and sharing data with other industries.

M2M Market Examples :

VERTICALS FEATURES VALUE
Automotive • Vehicle Tracking 

• Traffic Control

• Manage a Fleet of Vehicles

 

  • Use less fuel
  • Have fewer Accidents
  • Gain logistical efficiency
  • Integrated IT / Finance
Security • Environmental / Subsidence / Utility AMR & AMI Monitoring 

• Home Security

• Water, Gas, and Electricity Meter Reading

 

  • Conserve electricity
  • Match supply & demand
  • Lower costs
  • Increase collections
Finance & Retail • ATM / EPOS / Kiosks/ Stock Control / Gaming 

• Digital Signage

• Point of Sale : Speed / Reliability / Security

 

  • More customer interaction
  • Interaction on Demand
  • revenue opportunities
Healthcare & Medical Devices • Patient Monitoring 

• Telemedicine

• Emergency Vehicle Response

  • Fewer Doctor visits
  • Higher quality of care
  • Real time patient assessment

 

Overall M2M areas could be:

  • Utilities
  • Fleet Management & Logistics
  • Connected Vehicles
  • Remote Assets Monitoring
  • Digital Signage
  • Smart Security
  • Smart Vending
  • ATM/POS
  • Public Services ( Safety , Transport etc. )
  • Smart Homes
  • Consumer- Connected Devices
  • Telemedicine… and many others…

Even when verticals are quite similar regulations may vary on a regional basis; individual countries may decide that health data should stay within its borders, for example, requiring data localization. Cellular M2M has been built around the telecom voice model but is actually in the Internet world. Regulators and operators need to adjust their perspective accordingly. Many operators recognize that data is a larger business than voice in the future, with LTE needed for data, not voice. The data business should not be constrained by the traditional voice model.

Scalability is about getting connectivity quickly but it’s not all cellular and doesn’t rely entirely on telcos. Large volumes of devices will be connected with WiFi or 802.15.4 radios connecting to an existing wired network. There are connectivity challenges within that part of the ecosystem as well.

M2M projects take anything from 12 to 24 months. Customers need this time reduced to get to market faster in any way possible, including getting help on device deployment, speeding up certification, and so on. Some of the biggest debates in contract negotiations involve data ownership, data usage, and indemnification around IP; these are major legal issues. Liability is another major legal issue — what happens when eCall doesn’t call or an alarm system doesn’t reach the central station?

Beyond connectivity, who will be responsible for the acquisition, analytics, and storage of large quantities of data? Some M2M customers generate hundreds of Gigabytes of location-based data every month, sending information updates every second; multiply this by 10s of thousands then millions – these are very sophisticated problems. IT companies are more likely to solve them than telcos although some are in the midst of M&A activities with data analytics and “big data.”

(Source: Excerpts from a recent Ericsson round table on M2M in CTIA; CellStrat Research)

July 28th, 2012

Facebook Quietly Created New E-Mail Addresses

Facebook made its name by building one of the world’s most popular social networks. But, it sometimes,  itself comes across as antisocial.

Facebook is invaluable as a platform for finding long-lost friends, not to mention sharing links, photos and personal videos. For better and worse, the site has even redefined the word “like.”

Of course Facebook manages to use all of this goodwill to its own advantage. And the company often needs to be reminded that there are limits to how much it can exploit user information for profit.

Facebook has settled a class-action lawsuit that forces it to be more clear that clicking on the “Like” button means your name and photo can be used to endorse whatever movie, product or politician you “liked.”

Most recently, Facebook surreptitiously modified user profiles to replace their original e-mail addresses with @facebook.com addresses. Mail sent to that address becomes a Facebook message to a user. You’d think that a company with so many loyal followers would have announced this ahead of time. That’s a definite dislike.

(Via Scientific American)

 

July 24th, 2012

Sellers mine Big Data for sales productivity improvements

Big Data has taken the world by storm. Among its myriad applications to the business world, sellers are finding a goldmine in Big Data, the massive amounts of data collected from Social Networks, eCommerce interactions, website tracking, customer interaction and other sources of data. Firms like 24/7 in India are developing specialities in Big Data and helping companies like United Airlines track customer usage and patterns in purchase behaviors. Such technology allows firms like United Airlines to tailor packages and offers on customer profiles and preferences.

Now Big Data is helping Sellers discover untapped or underserved markets and redirect their sales resources including personnel and marketing to the underserved areas. A recent article in Harvard Business Review (authors : Manish Goyal, Maryanne Q. Hancock and Homayoun Hatami of McKinsey and Co) describes how some leading firms are using Big Data to segment their target markets into Micromarkets to detect patterns and sales opportunities. This Data is not the same as CRM data and is sourced from a variety of channels as mentioned before. Ranging from a few dozen terabytes to many petabytes, these data sets are so extensive and complex that specialized software tools and analytics expertise are required to collect, manage and mine them. Such data sets find use in developing sales insights, detecting patterns and predicting consumption for a variety of products. Such data and associated tools allows sellers to chart out futuristic opportunity rather than relying on past data and results. As is common parlance in the Investing world, past performance is not an indicator of future results – the Big Data Analytics brings a dose of reality to this saying in the world of sales and business strategy.

As per the HBR article, Micromarket analysis involves five steps :

1) defining the optimal micromarket size

2) determining the growth potential for each

3) gauging the market share in each

4) understanding the causes of variation in market share

5) prioritizing high-potential markets to focus on

Subsequently, performance of sales teams are assessed relative to the opportunity within micromarkets.

Many leading firms like DuPont are using such Micromarket techniques to segment and analyse their target markets and drive sales productivity improvements and revenue generation.

Source : Excerpted from Harvard Business Review (July-August 2012 edition) article titled “Selling into Micromarkets”.

July 14th, 2012


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