Peter Guidi's Blog

Archive for March, 2013|Monthly archive page

How big is Big? The Uber Cloud

In Uncategorized on March 31, 2013 at 6:32 pm

In my last blog I discussed the three high level priorities of Big Data and its role with mobile payment.  In this blog I take a closer look at how big is BIG DATA. Today, retailers have access to the transactional data that they collect at the POS, or is provided to them by 3rd parties. Big Data is the “other data” from the “Uber Cloud”. The Uber Cloud includes all data sources like web server logs and internet clickstream data, social media activity reports, mobile-phone call and text detail records and information captured by sensors.

How big is big? YouTube, FaceBook and Goggle are estimated to store 1400 petabytes of data including more than 35% of the world’s photographs. Between them, they share approximately 11.2 billion page views per day. People “Tweet” about 128 billion times per year at a rate of 4500 tweets per second. Annually, people spend over 2.2 trillion minutes either talking on the phone or sending 6.1 trillion texts. On any given day people are texting 193,000 messages per second or spending 2.2 trillion minutes talking on the phone.  There are only 7 billion people. We can agree, this is BIG!

That’s a lot of millions, billions and trillions: but what is a Petabyte? When I tried to think about how to explain a Petabyte I found myself thinking of Doctor Evil demanding; “one million dollars” not aware of how little a million dollars had become. It is true, a million dollars is not what it used to be, but the same is even truer when considering data.

A Petabyte is big. Mathematically, “a unit of information equal to one quadrillion (short scale) bytes, or 1 billiard (long scale) bytes”.  It’s hard to visualize what a Petabyte could hold. “1 Petabyte could hold approximately 20 million 4-drawer filing cabinets full of text. It could hold 500 billion pages of standard printed text. It would take about 500 million floppy discs to store the same amount of data”. The promise of mobile payments is that retailers will be able to access and use these data sources to build a more profitable, relevant relationship with their customers.

Big Data means Big Data Analytics. Big data analytics is the process of examining large amounts of data from a variety of sources to uncover hidden patterns, unknown correlations and other useful information to engage the consumer during the purchase cycle. Access to Big Data within the mobile wallet will drive radical efficiencies enhancing social engagement and improve information sharing between the consumer and the retailer.

Big Data and Mobile Payments: Three priorities.

In Uncategorized on March 28, 2013 at 9:11 am

The three high-level goals of a Big Data program are:

  1. Collect information: The objective is to collect information that deepens the understanding of customer’s plans, intentions and behavior so the organization has a basis for decision and action. The first step is to collect and save all of the digital breadcrumbs. The challenge is, since we can’t understand consumer behavior that we don’t have, we need to collect everything. Since it’s impossible to know the future value of data we must hang on to it for a very long time.
  2. Conduct Analysis: Big Data really means Big Data Analytics. The challenge is to find relevance in an ocean of information. There are multiple trends occurring within the consumer base, some evolve quickly, some play out more slowly. The goal of analysis is to provide insight and opportunity to the decision makers managing the business.
  3. Take action: More tactically, it is what you do with the information that counts. The key to Big Data and mobile payments is the dialogue that occurs between the consumer’s mobile application and the POS during the purchase cycle. The objective of action is to promote more profitable consumer purchasing behavior.

The success of mobile payments begins with transactions. Transactions are a result of consumer enrollment and adoption. Enrollment and adoption require constant visibility and consumer incentives, in ways similar to advertising typical of the current payments paradigm. The difference is that Retailers rather than Financial Institutions are promoting the method of payment. The key to ROI is engaging consumers and creating demand using Big Data solutions during the purchase cycle. This is the connection between BIG Data, mobile payments and the POS.  Access to Big Data during the purchase cycle requires an integration that is tightly coupled to the POS at the transaction services layer.

The retailer controls payments in the mobile environment. Retailers are being very careful about who they allow into the wallet…MCX is an outgrowth of this concern. Retailers are concerned that the current card fee structure will become the standard in mobile payments. Retailers are asking, “How do mobile payments make me money or even justify the infrastructure investment”. While the current focus on mobile payments are POS enablement and transaction fees, tomorrow the focus will be driving new business through consumer engagement. Big Data is the backbone of demand generation and the POS controls how Big Data solutions are enabled.

Smart phone technology changed the expectations of consumers, merchants and eco-system partners. The smart phone has also changed the way consumers do business; integrated mobile payments at the POS is the last frontier.

Big Data, mobile payments and the connected consumer

In alternative payment, big data, connected consumer, Convenience Store, mobile payment, omni-channel, payment, Peter Guidi, Platforms, retailers, Uncategorized on March 9, 2013 at 6:36 pm

“Big Data” is a term that refers to the vast quantity of consumer information that is available both on-line through 3rd party resources and within the retailer’s environment. Connecting Big Data to consumers through mobile payments represents the commercial usefulness of the information. Thanks to more powerful ePOS, the internet and the emergence of the “information cloud” this data can now be manipulated and utilized to drive pre-sales consumer engagement and drive sales during the purchase cycle. Big Data information is more potent when it can be applied to areas unconnected with how it was originally collected. As an example, the ability to link the CDC’s tracking of the flu with promotions for cold medications, or the ability to link coupons for hot/cold drinks to National Weathers Services tracking of temperatures. The back bone of retailer performance will be connecting Big Data to mobile payments (the consumer) during the purchase cycle through “personalization” and driving consumer engagement.

Mobile payments. The integration of the consumer through their smart phone to Big Data is the technical challenge facing the industry. Leveraging Big Data in a mobile payment environment means establishing a dialogue between the consumer’s smart phone/wallet and the ePOS at the time of purchase allowing a robust exchange of data so that the consumer experiences payment, loyalty, and offers (product recommendations, coupons) in one seamless experience.

The technical requirements of serving mobile payment and the connected consumer at the ePOS during the purchase cycle will drive change in the payments processing environment. Perhaps the greatest change is the potential disintermediation of the traditional payment processor from the mobile payment. A large shift in consumer payment behavior to mobile payment means a significant drop in card transactions across the legacy payment processing network. 

The legacy payments processing network was built to handle payments at the beginning of the electronic payments era before the emergence of Big Data. The result is that the infrastructure, while highly fault tolerant and reliable, does not lend itself well to change and is not compatible with a robust exchange of Big Data between the consumer and POS at the time of the transaction. This is great for the traditional card based ISO8583 message, but severely lacking for mobile payments and Omni-channel shopping.

The ePOS has evolved from a limited “dumb” machines built around closed systems with proprietary code to a very powerful computing device utilizing open standards. The ePOS now has the ability to communicate in an IP environment and as a result, has the ability to communicate both payment and Big Data to networks outside of the legacy payment network utilizing IP based communication.  ePOS vendors have changed their payments strategy and are moving to cloud based systems. In Petroleum all four major providers are developing cloud based payments applications that will standardize the software between the POS, EPS and Payments Cloud.

The future of Omni-channel shopping depends on the ability to communicate to the connected consumer through an IP/cloud based mobile payment with access to Big Data. Big Data is the “secret sauce” of mobile payments.