TOP METADATA ERA POSTS OF 2013


It’s December and time for us to deliver a retrospective of key topics that defined the 2013 security landscape.

This was the year that metadata made the headlines, big data became a reality, and compliance regulations are now more than ever affecting and influencing company processes and decision-making.

Below are the 5 most-read blog posts we published this past year:

1. Peak Security

Banks are responsible for managing and safeguarding your money and I’m sure you’d find it unacceptable if a bank said that only half your money was safe. Human generated data is like money in the bank: extremely valuable and warranting vault-like security. Sadly, our research tells us that merely half of the data that needs protection hasprotection.

Addendum: As a reality check, at 1:44pm on December, 11, 2013 I typed in “hacked” (see image). Rob was right, we haven’t reached peak security, at least not this year.

hacked

2. Will Big Data Give Each of Us a Pop Tart Moment?

By leveraging consumer purchasing behavior and big data technology, Walmart learned that demand for pop-tarts rises right before a hurricane makes land, so they know to stock up on this easy-to-prepare sustenance. But companies have generally been reluctant to share their Big Data, even though we contribute much of the data that companies collect. For example, wireless carriers won’t allow subscribers to view granular details of their cell phone bills, and electric utilities have similar policies on home power usage. The grand goal is that, just like Walmart, one day we’ll be able to analyze our idiosyncratic behavior and realize our own pop tart moments.

3. How Did Snowden (Really) Do It?

One story I don’t think any one of us could have escaped this year was Edward Snowden. Rather than debate the morality of whistleblowing, our post takes a look at the technology aspects of Snowden’s leaks. Snowden’s story has become a cautionary tale for organizations that want to make sure that the right people have the right access to the company’s data at all times.

4. Metadata Matters

Before the Internet, researchers were well aware that income, education, gender, and other demographic metadata are powerful predictors of who we’ll form relationships with. And the inverse is also true: our friends and contacts can tell us a lot about ourselves. With everyone on a social network and sharing personal details by the nanosecond, online metadata now has awesome powers to reveal more than you might think.

5.   HIPAA’s new rules reach far beyond healthcare providers – are you impacted?

New rules are in full effect – any company that has access to e-PHI will be treated just like a hospital or HMO and come under HIPAA’s privacy and security obligations. If you are still reading this, you’re obviously a subcontractor who has stored and processed medical data from a healthcare entity but for whom HIPAA was a meaningless jumble of initials. Boy, do we have a video for you. You might ask, why me? Well, the ultimate intent of the new HIPAA rules is to close off any holes in security and enforcement when the primary hospital or other ‘covered entity’ outsources its data processing.

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Will Big Data Give Each of Us a Pop-Tarts Moment?


One of the under-appreciated points about Big Data is that as consumers we also act as producers. In each transaction, we contribute bits of analyzable data to the corporate information stockpile. Data hungry companies then draw non-obvious connections by mining zillions of data points. For example, if you are Walmart, you’ve learned that stores in the path of a hurricane often see a spike in demand for Pop-Tarts.

Walmart’s response after spotting this correlation is to now always make sure affected outlets are well stocked in advance with this easy-to-prepare food item. Data mining has been a powerful business decision tool for big box stores, but what about everyone else: can we as consumers directly benefit from all the data we’ve helped to create?

Online retailers and social media sites have led in this area. They have returned to the consumer some of their insights by providing customer-specific recommendations that are based on a global analysis of behaviors. Special collaborative filtering algorithms hunt through the data to find similarities between your own purchasing patterns and larger groups or clusters. These statistically-based recommendations are at the heart of Amazon’s book and Netflix’s movie suggestions.

But outside of e-commerce, companies have generally been reluctant to share their Big Data.

This lack of transparency was taken up in an article recently in The New York TimesIf My Data Is an Open Book, Why Can’t I Read It? The writer tells about the frustrations in getting detailed data about cell phone and electric usage from each of her respective providers. She was hoping to see the geo-location data her carrier records (and, by the way, does make available to third-party marketers), but was told that the company doesn’t share customers’ own location logs with them without a subpoena. Her energy utility had similar reservations.

One of the stumbling blocks mentioned in the Times article is that old-economy companies feel they play the role of a benevolent data owner that shares just enough data to be a little helpful. It turns out that consumers are also uncomfortable with the idea that their long-time vendors might be analyzing, categorizing, and sharing conclusions from their personal data.

But attitudes are changing for both consumers and corporate data collectors.

For example, many of us have probably engaged in on-line banking through third-party applications, using desktop software to pay bills and analyze spending trends. Recently my stodgy bank began to offer direct online bill paying—yours probably has done the same long before mine– and so I transitioned to their cloud-based software.

I lost some of the convenience of instant analysis that I had when I was accessing my data on mydesktop computer. But then I noticed the bank was adding modest features—alerts that could be configured when my balance reached certain limits. I suspect there’ll be more features and reporting capabilities in the near future in their cloud-based service.

And in the equally conservative credit card space, start-ups have emerged to analyze millions of transactions for fraudulent charges. The key innovation here was to borrow a cue from Amazon’s book reviews: crowdsource vendor evaluation based on feedback from the service’s subscribers. I count myself as a customer of one of these credit card fraud detection services. It was clear in the terms of service that I was allowing my credit card data to be used in a collective fashion to help spot fraudsters.

The key mindset change for companies is that they have to recognize that consumers own their data, and consumers must realize that they are granting access to their data with (hopefully) suitable guarantees of privacy.

Once these data ownership understandings are formalized and accepted by both parties, it won’t be long before consumers have their own Pop-Tarts realizations as they reap benefits from Big Data.

Comscore study on mobile retail shopping


Key findings –

– 4 in every 5 smartphone users – 85.9 million in total – accessed retail content on their device in July.
– Amazon Sites led as the top retailer with an audience of 49.6 million visitors, while multi-channel retailers including Apple (17.7 million visitors), Wal-Mart (16.3 million visitors), Target (10 million visitors) and Best Buy (7.2 million visitors) also attracted significant mobile audiences.
– Among both iPhone and Android users, Amazon ranked as the top retailer attaining a reach of 43 percent among iPhone users and 55 percent among Android users, with visitation to the Amazon Appstore largely accounting for the higher reach among Android users.
– Apple commanded a much stronger and expected 33.5 percent reach among iPhone owners compared to 7.3 percent among Android users.
– Females accounted for a higher share of time spent on retail destinations at 53.4 percent of minutes on desktop computers and an even greater share of retail minutes on smartphones at 56.1 percent
– 70.7 percent of smartphone retail visitors under the age of 45 compared to 61.1 percent of desktop users
– Among smartphone audiences accessing retail destinations, nearly 1 in every 3 had a household income of $100k or greater, with this income segment driving a comparable 31.2 percent of minutes spent on retail sites and apps.

The Predictive Analytics Revolution- Are you sitting on the sidelines?


Predictive analytics (or Big Data) is here to stay. You may not understand it. You may not believe that it really works. But the reality is this: your competitors (and it may be just one or two of them) are using predictive analytics to chew up market space as you remain on the sidelines.

Don’t believe me? Consider the retail space. Who is the undisputed king of retail? That’s right, Wal-Mart. What’s their secret? What has given them the edge for so many years over their competitors? Data analysis. They live and die by data and have been for decades. Wal-Mart knows their customer data better than anyone and have the market share to prove it.

Recently the Dollar stores took on Wal-Mart by providing cheaper supplies like toiletries and medicine. Their strategy started to see some success and Wal-Mart even started to lose market share. But the retail giant went back to their data for a solution. The data said that many Wal-Mart customers started pinching pennies at the end of each month and needed a few basic items to get them over until payday. The solution, stocking shelves with thousands of items under $1 at the end of each month. Customers lured to the Dollar stores for such items were back in the Wal-Mart fold.

Target has also jumped into the game with their own consumer analytics program. The most famous example is how they used in-store data to pick out pregnant women through their shopping habits. They used this information to send marketing material promoting baby products. It worked…almost too well.

Wal-Mart, Target, and online stores like Amazon have forced everyone in this market make a decision, if you want to compete in retail you had better jump into the data science and predictive analytics game or a going-out-of-business-sale is in your near future. Sitting on the sidelines is not an option.

This isn’t isolated to just retail. There are stories everyday in the news about companies in a variety of markets taking a second look at their data and finding a treasure trove of valuable information.

Despite the hype and the proof that predictive analytics can give companies a competitive edge, the sidelines are full of businesses that are still not sure about getting in the game.

The New York Times reported that a handful of universities are using their data and predictive analytics to help them find students who are about to drop out of school. These schools know that higher enrollment means more money. These early adopters are reaping the benefits and aren’t afraid to tell everyone. Why? The vast majority of their competitors haven’t given this type of data analysis a second thought. Just like the example above, a few colleges will charge ahead and reap the benefits of higher enrollment while other universities…sit on the sidelines.

You can find the same thing in the health care industry. The Wall Street Journal published an article by Dr. Marty Makary of Johns Hopkins pleading with hospitals to make better use of their data to save lives. You can almost hear the frustration in his voice when he writes, “Medical mistakes kill enough people each week to fill four jumbo jets.” Even though there are 98,000 deaths due to medical errors in the United State, most hospitals and medical facilities are slow to adapt any type of data analytics.

A few forward thinking hospitals and health care facilities will see the opportunity and do what Dr. Makary suggests. Using the data visualization and predictive analytics, the trend setters have improved patient care, are keeping costs down – and most importantly – saving lives in the process. But just like the universities, the majority of hospital will remain on the sidelines. (I hope I can take my family to the forward thinking hospital!)

Why are so many still sitting on the sidelines?

The Harvard Business Review may have the answer. In an an eye opening survey they reveal the source of the bottleneck. (I highly recommend reading this entire study.) The study shows that the hype and awareness about data analytics is at an all time high.

According to the survey, a vast majority of companies are planning Big Data initiatives:

  • 85% of organizations reported that they have Big Data initiatives planned or in progress.
  • 70% report that these initiatives are enterprise-driven.
  • 85% of the initiatives are sponsored by a C-level executive or the head of a line of business.
  • 75% expect an impact across multiple lines of business.
  • 80% believe that initiatives will cross multiple lines of business or functions.

But here is where the rubber meets the road. HBR reports that:

  • Only 15% of respondents ranked their access to data today as adequate or world-class.
  • Only 21% of respondents ranked their analytic capabilities as adequate or world-class.
  • Only 17% of respondents ranked their ability to use data and analytics to transform their business as more than more than adequate or world-class.

The majority of companies are on the sidelines because they think they can’t readily access the data they have, they don’t have in house tools or talent to analyze it and don’t have the ability to put the data to use anyway. In other words, they don’t think their data is good enough.

Don’t let this kind of thinking keep you on the sidelines. I talk to business owners everyday who think they don’t have enough data for predictive analytics or even just analytics. Most of time, just the opposite is true. Many of our clients were pleasantly surprised when we told them they had more than enough data to jump into the game.

Don’t be one of crowd still sitting on the sidelines. Be one of those early adopters in your market space that uses predictive analytics to jump ahead of the competition. Would you like to learn more?

Thanks to http://blog.canworksmart.com/predictive-analytics/the-predictive-analytics-revolution/?buffer_share=e125e