Radical Redesign: What You Can Learn

shutterstock_117069988Bryan’s recently pointed out hidden secrets of the Amazon Shopping Cart and how Amazon was testing a new look for their “ready to buy” area and even potentially changing the iconic rounded rectangle “add to cart” button they have used since the mid 90s for a rectangular, flat designed one.

We’ve recently caught another test:


Screen Shot 2013-08-15 at 11.22.58 AM




They are moving their website design to a cleaner and flatter design. There are pros and cons to using a flat design, especially with mobile users. However, they are doing there redesign exactly the way everyone should. They roll out section by section to understand the data and key performance metrics related to that specific change. Then when they finish rolling out each part of the redesign they can understand what areas need to prioritized focus for further optimization. Are you planning your redesign this way?


*Image courtesy of Shutterstock

Please share if you think others would benefit.

Team Data Science

The Fix Is In

photo: JD Hancock

Big data are a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.

So say the opening sentences of the “Big data” article in Wikipedia. The people primarily responsible for conquering those challenges are data scientists. Being a data scientist these days is rather like being a Renaissance person; one must possess knowledge of and competency in a wide variety of subjects related directly and indirectly to the fields of mathematics and science.

Fortunately, data science has a number of sub-specialties to share the load. Understanding–defining–who does what (capturing, curating, storing, searching, sharing, transferring, analyzing, visualizing, processing) and why they do it means companies building data science teams can intelligently choose the areas of specialization that will best serve their goals.

Five Roles You Need on Your Big Data Team

Of course there’s the data scientist, the coveted knight in shining armor who visualizes models and creates (and continuously optimizes) sophisticated algorithms to transform data into something useful. But she could not do her part to fulfill corporate expectations without the support of equally coveted

  • Data hygienists, who deal with the “dirty data” problems inherent in collecting data so the data is clean now and stays clean in future.
  • Data explorers, who burrow into all the data a company collects to determine what, if anything, can be done with it, including how data originally collected for a different reason might be repurposed.
  • Business solution architects, who structure and organize data so it’s properly updated and where it needs to be within the necessary timeframe of every query–a critical feature of today’s data science when queries are ‘answered’ in real-time.
  • Campaign experts, who, with an in-depth understanding of both the technology and marketing, can turn the knowledge derived from data into insight and then into advice.

Assembling a powerful data science team, whether that team is internal or third-party, is necessary to applying big data tools. However, success rests as heavily in the hands of the right corporate culture as it does in the right specialized people. The best solution? Welcome reevaluation, innovation, experimentation, and keep the focus on the end game.

Please share if you think others would benefit.

Scarce and growing scarcer

Being able to use the knowledge derived from data, achieving the insights to which data can lead is the centerpiece of a marketer’s requirements in the big data era. But before you can acquire knowledge, you must understand the data itself and how its patterns fit together and suggest other patterns, how to work with it to produce useful, meaningful knowledge. Enter data scientists.

Employment opportunities for data scientists are growing. They will continue to grow, and some institutions are putting educational programs in place to help meet future demand. However, projections suggestion the demand for data scientists will soon exceed their availability. A compelling graphic synthesizes the problem.

Top skill set Requirements to be a Data Scientist

Data scientists aren’t data analysts. While the two roles may start with a grounding in scientific and mathematical skills, a data scientist is far more a “Renaissance individual who really wants to learn and bring change to an organization,” says Anjul Bhambhri of IMB. About a data scientist’s skill set, Mark van Rigmenam writes,

They need to have statistical, mathematical, predictive modelling as well as business strategy skills to build the algorithms necessary to ask the right questions and find the right answers. They also need to be able to communicate their findings, orally and visually. They need to understand how the products are developed and even more important, as big data touches the privacy of consumers, they need to have a set of ethical responsibilities.

Often, related fields of study pair with a breadth of programming, managing, processing and curating skills to shape the qualities of individuals who will guide a business’s effective use of data. Rigmenam suggests an ideal data scientist would have the following skills.

  • Strong written and verbal communication skills;
  • Being able to work in a fast-paced multidisciplinary environment as in a competitive landscape new data keeps flowing in rapidly and the world is constantly changing;
  • Having the ability to query databases and perform statistical analysis;
  • Being able to develop or program databases;
  • Being able to advice senior management in clear language about the implications of their work for the organisation;
  • Having an, at least basic, understanding of how a business and strategy works;
  • Being able to create examples, prototypes, demonstrations to help management better understand the work;
  • Having a good understanding of design and architecture principles;

We would add, while an effective data scientist requires latitude to consider and experiment (work autonomously), she must also be able to work cooperatively. Data scientists are members of teams that aren’t simply made up of senior leaders. There are plenty of other employees who work in the trenches with ideas about situations that require solutions and how solutions would fit into goals of other departments. Failures in cooperation and communication can lead to costly disasters.

Likely, few data scientists possess all the above qualities, so a business should prioritize the ones important to them.

In planning for apply new technologies, businesses must also plan for how they will apportion responsibilities for critical data science needs–through third-party applications or data-science-specific internal departments or perhaps, both. At present, we are gazing at the tip of the big-data, data-scientist iceberg. Demand for big data solutions is increasing. So is the demand for the innovators behind the solutions.

Please share if you think others would benefit.

Let’s discuss: The closer you think you are, the less you’ll actually see

1968 Dodge Charger R/T - 2560x1600 Desktop Wallpaper Black Background What sort of search engine magic does a company like Google work to deliver results meant to please and satisfy those who use its services? Truth is, it isn’t magic at all, and neither are the finer points of accomplishing this perceived magic very magical.

Google’s Search Magic Revealed

Details obfuscate purpose, and correlations can often obfuscate the bigger picture: causation.

When you try to determine how best to entice and engage customers, the search engine optimization tactics you employ have a way of distracting you from what is most important. So many correlations, so many microscopic examinations of efficacy, so much attention to a surfeit of arcane SEO advice can lead to a myopic view of how customers want to interact with you. Like a magician’s slight of hand, these tactics redirect your own attention from the true magic that is happening elsewhere. In essence, the closer you think you are, the less you’ll actually see.

When a business focuses on an over-abundance of ‘expert’-recommended small stuff, it typically misses the point, which is: How can you provide the best product for your customers? How can you satisfy, even delight, your customers? How are you going to make each and every customer feel you are there just for them?

The goal of your efforts shouldn’t redirect the attention of your customers. It should be based on moving beyond mystification to deliver the experiences your customers actually want and tailor those experiences to each customer, one at a time. ‘Big data,’ that merger of lots of data points with a variety of data sources and real-time delivery, is your best opportunity to move beyond correlation and into the big-picture realm of causation: If you understand more of what your customers want, you are in a better position to deliver it. The secret both you and your customers are looking for, the secret a company like Google has long since discovered, is customer-centricity.

Is slight of hand the answer? Hardly. Once you understand the magic behind the magic, you learn how to identify the reasons why your customers should care about you and buy from you in the first place. Then you don’t have to deliver the magic of magic; you can deliver the magic of value.

Image: by Barry Wetcher, SMPSP – © 2013 Summit Entertainment, LLC. All rights reserved

Please share if you think others would benefit.

The Conversation: Everything Old is New Again?

1968 Dodge Charger R/T - 2560x1600 Desktop Wallpaper Black BackgroundHow new is big data in the big scheme of things? Will it “replace ideas, paradigms, organizations and ways of thinking about the world”? Or has it evolved from a series of perspectives that have been with us for some time? Is it a development parallel to the invention of the telescope and the microscope, where one can see both big and detailed pictures? What are the trade-offs alongside the benefits? A bigger view of big data.

Sizing Up Big Data, Broadening Beyond the Internet

Please share if you think others would benefit.

The Conversation: Customer Relations Creepiness

Evil monkey from the movie about the evil monkey that smiles awkwardlyForget the creepiness of Google knowing so much about you it can recommend a restaurant based on your eating-out patterns (woe to you and the little gem of a restaurant Google is not going to recommend). The real creepiness (maybe) is when customer relations staff can’t get the essentials of customer relations just right.

In the world of handy big data apps and more, we need to ask ourselves how a business introduces “situational awareness” to the ways it conducts interactions with customers.

When Digital Marketing Gets Too Creepy

photo by: scragz
Please share if you think others would benefit.

Conversion: Big Data’s Sidekick

Kick me (Explored)You can’t have missed the buzz about how big data tools can take your business to a whole new level. They can, but, by themselves, they are not going to solve all your business challenges. Often, they suggest opportunities you can turn into insight and specific solutions. But, other factors directly influence how successful your big data strategies will be.

One of the most important factors influencing success is integrating conversion rate optimization principles into your business practice. This was important back in the mid-90s–back in 1995, Amazon hit the ground running with conversion principles firmly in place. It’s just as important today.

Do you want to know how satisfying your customer experience is? Look at your conversion rates (the number of visitors who made a purchase / the number of visitors who came to your site during a set period of time). That metric isn’t the only one you need to follow, but your conversion rate is first and foremost a measure of your ability to persuade visitors to take the action you want them to take. It’s a reflection of how effectively you satisfy your visitors and customers. For you to achieve your goals, your visitors and customers must first achieve theirs.

If you want those big data tools to work for you, you need to pay relentless attention to the principles of conversion rate optimization. They are quite simple. Getting them just right is the piece that requires you to roll up your sleeves.

  • Great brands, products and customized buying experiences naturally generate better conversions
  • Follow the hierarchy of optimization (functional, accessible, usable, intuitive, persuasive)
  • Master the conversion trinity (relevance, value, call to action)
  • Understand optimizing your conversion rate applies to your entire site; it’s not limited to a landing page
  • Optimization is not a one-time event or project; it’s an ongoing process

Optimizing conversion rates is not exciting. It’s boring, repetitive, detailed, but necessary work, much like general management. (However, big data testing tools—Monetate is one example—can automate and alleviate much of the drudgery.)

There is a part of the big data/conversion equation many overlook. Data is a valuable commodity no matter how much of it you have. Some businesses are not mentally or structurally ready to shift their efforts into big data territory. Some businesses simply can’t afford it at this point—big data tools are becoming cheaper, but they aren’t yet a Global 5 Million, as opposed to a Fortune 100, solution. And, frankly, big data tools may not be the right choice for all your businesses needs.

As you monitor the big data landscape, keep in mind basic analytics are still a powerful way to understand how to improve customer satisfaction. The granularity of information you can derive from big data creates a much better picture of what is happening in your relationships with customers, but the benefits of conversion rate marketing do not depend on the degree of granularity alone.

Data is nice. Tools to turn data into information that can provide insight is nice, too. Putting however much data you have to good use within a conversion rate marketing framework is essential.

photo by: pasukaru76
Please share if you think others would benefit.

Email, Relevance and Big Data

Prettied up and ready to packIn fewer than twenty years, doing business online has developed similarly to the rise of a celebrity discovered in the morass of obscurity and thrown into the limelight. Put the trajectory from Cool Site of the Day (founded 1994) to omni-channel marketing in perspective; that’s pretty meteoric growth in the big scheme of things. The upside is a meteoric rise to fame. The downside is a meteoric rise to fame.

Using the internet as a venue for conducting transactions, many businesses and consumers alike have gone from guarded skeptics to full-blown enthusiasts. But in the middle of the enthusiasm lives an oft-overlooked truth: people are humans. They have preferred ways of interacting with the world, and they have individual needs. For marketers, this is the fly in the ointment.

Back in the old days when many online businesses were trying to find solid ground, email marketing was the rage and high-performing lists were the grail. In short order, ‘personalization,’ with the goal of creating emails that made customers think the company looked upon them as individuals, became the imperative. Still, content was targeted to a segmented though nevertheless large audience, not to one person.

That level of customer attention offered a significant conversion boost over direct-marketing-esque emails. But it hasn’t had the staying power companies hoped for. A recent survey shows respondents favor ‘older-fashioned’ ways of interacting with a company, at least when it comes to learning of new products. Even conventional print catalogues out-perform current darlings like social media, sometimes by two hundred percent.

Intro to Product
But anymore, customers aren’t terribly interested in personalized email. They want customized email: recommendations for products they might like; content that is specific to them. And when they are on the company’s website, customers want tailored content. (Sounds as though they want the Amazon experience in all their online encounters with businesses.)

What is the common denominator in these desires? Customers want relevance. This should really come as no surprise. Customers have always wanted relevant experiences when they make purchasing decisions off- or online. Who among us isn’t a customer? Who wants to give over precious time to stuff that doesn’t matter in the least to us? If we are in control of what we do with that time, we’ll head for the relevant experiences every time.

Conversion rate marketing should be about giving customers what they want so businesses can get what they want. Conversion rate marketing has always been about providing relevance to customers. So, if conversion rates are greatly improved when communications are customized, then customization needs to be a marketing priority.

Send one customer an email that no other customer receives and do this for all your customers? Big data technologies make it possible to do exactly this, even when your customer base is large. But few companies today have the ability to use data at that scale when creating relevant experiences for individuals requires a lot of data, a lot of processing and a level of technology many companies do not have and cannot afford. In reporting results from the above-mentioned survey, Ayaz Nanji writes,

  • Almost half of marketing executives surveyed (45%) indicated that they lack the capacity for analyzing “Big Data.”
  • 50% of marketing executives said they have inadequate budgets for digital marketing/database management.
  • Only 24% of marketers always use data for actionable insight. This limited competency in data analysis is viewed by 45% of executives as a major obstacle to implementing more effective strategies.
  • Only 27% of the marketing executives surveyed said they always integrate customer data from different sources into a centralized customer database.

These are not cheery statistics.

The downside to any online business’s continued (or improved) success finds its expression in an inability, sometimes an unwillingness, to participate in the trajectory of technological developments. Budget constraints cannot be minimized, neither can colleague and leadership resistance. However, no company can afford to overlook one of life’s basic tenets: people want to be treated as individuals not as masses. Delivering what individuals really need is critical. Providing relevance has never been negotiable, and, increasingly, big data technologies allow businesses to take advantage of and relate all the data they collect to offer relevance to each customer.

Today, the money is in the data. It’s time to regroup, even if it takes small, incremental steps such as starting to customize emails. Time to remember what’s important and offer it at whatever scale you can accommodate. Time to consider whether outside providers can do for you what you cannot (currently) do for yourself.

Time to meet the challenges inherent in an industry’s meteoric rise to fame.

photo by: lisaclarke
Please share if you think others would benefit.

10 Qualities a Data-Friendly Business Culture Needs

[ New Perspective ] Tokyo Metropolitan Government Building, Shinjuku, Tokyo, JapanUsing and continuously optimizing data with the long-term goal of providing customer value and increasing conversion sounds easy; it can be difficult to execute. Getting the most from your data requires hard work and a willingness to adapt, to experiment, to learn from mistakes, to correct those mistakes as quickly as possible and to keep doing the process over and over without end. But, no matter the scale, crunching data to generate information and insight is valuable only if the organizational structures are in place to support the effort.

One of the most important problems corporations face is creating, at all levels of leadership a framework of support for and participation in new technologies that can help refine and expand how they do business. As pressure to adopt big data technologies grows stronger, many organizations have understandable fears concerning the integrity of their businesses. At the very least, leadership often perceives these technologies as threats to institutional knowledge and continuity.

Alex Miller of QVC, Anthony Bucci of RevZilla and Slava Sambu of Office Max have offered insight into the benefits of staying current with data processing technologies. They have discussed some of the stumbling blocks they have faced. But, as these three suggest, stumbling blocks are not dead ends. Organizations able to confront problems toward finding solutions soon begin to reap the rewards associated with using data effectively.

One of the goals—and it will become increasingly important—is corporate agility. With the ability to analyze and operate in real-time, a business can be far more responsive to its customers. It can quickly evaluate what is or is not working and correct the problem. It can identify problems and fix them immediately. For example, some data a business collects is going to be “dirty.” This is, and has always been, a common problem. Implementation of information derived from problematic data can potentially create a large problem with a large impact on the bottom line. A company needs to be agile enough to catch these problems, evaluate their nature and devise appropriate solutions as quickly as possible. As in right then; better still, an hour ago. Waiting while information makes its way slowly up the chain as each management level makes a decision—even waiting as long as it takes to organize a meeting of department heads—is not an option anymore.

Businesses are accomplishing this every day, and some have been employing these practices for years, decades. Amazon operates like this. Online newspapers operate like this. Alex Miller’s discussion of the need for immediacy in real-time data management of live broadcasting is particularly relevant. Real-time responsiveness is possible and advantageous with a sympathetic business culture.

What makes for a corporate culture that can successfully welcome and accommodate the emerging landscape of using data?

  1. An ability to understand a data-driven focus can out-perform many, if not all, previous business solutions
  2. An open-mindedness that supports the research, development and experimentation necessary to make best use of big data
  3. A perspective that supports the idea operating in real-time is an excellent way not only to enhance the customer experience but also monitor for problems and quickly correct them. However difficult it can be to negotiate at all levels, business agility is critical
  4. A marketing optimization framework that allows marketers to use the data effectively to make marketing decisions in real-time. Companies with higher conversion rates almost always have better marketing efficiency ratios (net contribution/marketing expenses). These companies understand it’s hard work to accomplish better marketing efficiency ratios, but it’s considerably more lucrative to do so.
  5. Higher standards of accountability throughout the organization, up to and including the CEO. Does the CEO know which factors of the customer experience impact sales, which projects or departments to favor, what truly needs to be done to optimize the marketing efficiency ratio? In a data-driven business climate, the CEO must know these things
  6. Leadership that promotes higher levels of communication, even collaboration, across all teams
  7. A willingness to grant certain decision making powers to smaller teams
  8. A commitment to employing conversion rate marketing principles
  9. An emphasis on making sure data as well as information and insight derived through analytics are flowing to individual teams across the organization so each can make clear decisions and execute in real- or near-real-time
  10. People and processes that foster a culture of risk-taking and ongoing testing

It took very little time to list (or read) these qualities. It will probably take much more time to internalize them so they become ingrained business practice. If you hope to stay competitive, however, just don’t let it take too long.

Please share if you think others would benefit.

Revving up a Data-Driven Culture: Anthony Bucci

DSC_0067Three co-founders. A start-up. A long-term commitment to experimentation and testing. Business began to blossom, and today, the organization is entering a period of hyper-growth. This has been Anthony’s enviable experience shepherding In the beginning, nobody needed to convince co-founder Anthony of data’s value, but he has found it’s not necessarily leadership he needs to persuade to make testing and experimenting a company-wide effort.

Anthony has learned letting institutional knowledge for testing reside with top leadership is not an effective organizational strategy. While all levels within a company need to be involved in testing efforts, he encourages businesses to identify a process, define a framework, then “make sure someone with the bandwidth can at least own it.”

Speaking from the position of leadership, Anthony has more to say about fitting data, testing and experimentation into a business’s mindset. Give him a listen.

Please share if you think others would benefit.

Blowing a Lot of Hot Air Over Big Data

Break freeShopycat. A feature for scanning the social media preferences of a customer’s Facebook friends and suggesting gift ideas sold on’s website. While Walmart has never shied from using analytic technology, it sort of missed the use-the-data digital boat when it came to focusing primarily on the customer experience. It isn’t alone. Difficultly integrating channels is an omni-channel challenge for many businesses with a brick-and-mortar arm. But Walmart is planning to make the shift to a data-driven organization (in fits and starts; selling certain items only online is not a great idea.) Walmart is hoping eighty-seven newly hired “engineers and coders” are going to help turn around the game it’s currently losing playing catch-up with (don’t lose more customers to) customer-centric Amazon.

Why Walmart Is Worried About Amazon

Let’s automate data prep; let’s ditch the internal chain of command that strangles experimenting with new ways to gather and use new data sources; let’s look at AI technology so resulting information and insight is built into the reporting process. That creative, forward-thinking analysts wish for intelligent tools to benefit a company is a clue they’re not just data jockeys and might be deserving of some attention. Big data tools now exist, or are in development, to answer most of the wishes. One thing is missing. Guess what.

Analytics Wishlist: Five Tools, Capabilites Analysts Wish Existed

Big data technologies are helping humans identify their own health troubles and find solutions. It makes sense that those with beloved barking companions wish for a similar technology geared to their four-footed buds. Now, your dog can wear a little ninety-nine buck device called Whistle. Whistle monitors your pet’s activity and compares the data to a large pool of other data to help you spot problems before they become serious. Score one for big data. (Warning: seriously adorable puppy alert)

Whistle Uses Big Data to Help Keep Your Dog Healthy

Is Verizon your telecom provider? If yes, did you know that since April 25 and until July 19, under a court order based on the “so-called ‘business records’ provision of the Patriot Act, 50 USC section 1861,” the NSA is collecting lots of personal datafrom your phone usage. Some folks mind the kind and quantity of personal data they are handing over, sometimes without even knowing they are doing it. Others accept that privacy in the big data world is becoming extinct. That’s a good thing, right? Up until it’s a bad thing.

Who’s Watching You? Not Just the NSA.

And Now for Something Completely Different

The art of air in motion: a real-time data visualization.

Wind Map

photo by: aussiegall
Please share if you think others would benefit.

6 Steps to Begin Using the Data

Steps to Big Data SuccessThe possibilities in big data technology are exciting, but if you know very little about what these information technologies can do for your business, just thinking how to use them can feel like heading out with a plastic straw to fight a behemoth. Can you just walk away from the field? No. Big data technologies are here to stay, and businesses that wish to remain competitive need to embrace a big data mind set. These steps can get you started.

1. Forget the hype.

Every field has its own jargon. Insiders know it, outsiders don’t. It’s awkward to be the outsider still puzzling what data silos are while the jargon-laden conversation you’re listening to has moved on. Don’t let the jargon and all the technology-talk distract you, and definitely don’t let it dissuade you, from your goal.

Consider that ‘big data’ is merely a label people apply to an evolution in technology that provides much more granular information about your audience and allows you to refine—perhaps expand or automate—many of the ways you do business. Because providers of services use this label, it helps you find solutions that take advantage of new technologies. Beyond that, ‘big data’ is simply a slick term that garners attention. Don’t worry about it.

Many of the solutions you might find valuable help solve the same problems businesses have always faced. And, while the scope and scale of what new technologies can accomplish have grown dramatically, the reasons for the solutions have changed little. Trust you will learn the jargon along the way (just look at how familiar you are with the jargon in this simple article!). Trust there are individuals who can help you understand exactly what a big data solution can do for you.

2. Identify your problems.

All businesses have problems that lead to questions about improvement, whether improvement means increasing customer satisfaction or coordinating what you do online with what you do through other channels. You probably know what your problems and questions are. So itemize them, prioritize them. Do you need to find ways to undertake a more sophisticated approach to testing and optimization? Would it really help to automate your search engine strategies and PPC campaigns? Maybe you want to build a review system you can merge reviews with a variety of product-related applications. Perhaps you most want to build a comprehensive cross-selling platform based on relevance. Start with your problems, then …

3. Identify appropriate solutions.

You can employ a good data scientist(s) to help you develop applications in house. Many businesses find it more cost effective to contract with third-parties. Start learning what third-party solutions related to your problem are available. Many will focus on automated and ‘black box’ applications which are often easier to deploy. Talk to other businesses, attend conferences that associations and providers hold, share case stories from others who have successfully incorporated big data strategies. Be a good shopper, and keep in mind, a big data solution may not be the right answer to all of your questions.

4. Take stock of the data available to you.

This is usually an iterative process with identifying your big data solutions. How much in-house data do you have? From where does it come? How easy is it to access? Will you need to start collecting different data? Would it be valuable to start taking advantage of unstructured sources of data? Do you have or will you need to tap into outside data resources? Do you have data resources you never thought of as resources? Intelligence within your business and careful discussions with third-party providers can help you identify the value of what you have and suggest what you need.

5. Educate the people in your business.

Not so long ago, many organizations resisted incorporating analytics into operations. The big data phenomenon is to analytics as a skyscraper is to a house. One of the more difficult problems is getting everyone in your business to agree these new technologies have value and will work. Leadership often worries dramatic changes might fracture the cohesion of the company and change models that have always worked well; these are real and justifiable, if perhaps frustrating, concerns. You may become enthusiastically convinced a big data solution is going to revolutionize your organization’s effectiveness, but the history of institutional intelligence can be a tough nut to crack.

Help other departments understand how these solutions can actually make everyone’s work lives a little easier. Encourage interdepartmental communication and cooperation. Develop presentations and circulate articles so others can see the value of these changes. Reassurance that big data can change the efficiency and efficacy of how people accomplish their tasks without changing the business goals can go a long way to achieving support.

6. Keep an open mind.

An open mind is going to benefit everyone in your business today. It is even more important as you look to the future. In the upcoming years,  as this technology is refined and that technology is invented, the big data landscape is going to expand and change dramatically. Every business needs to pay attention to the evolution of these solutions and be willing to experiment toward optimizing the most effective ways to conduct business.

As you head out, plastic straw in hand, remind yourself that few things feel too big and threatening when you start breaking them down into manageable pieces.

You can do it.

Please share if you think others would benefit.

Four Not-to-Miss Data Stories- June 3rd, 2013

not-to-miss-linksUse your mobile phone. Use your tablet. Use your electricity. Use the internet, the ER, a medical laboratory, a gym. Generate lots of data all about yourself. Who can use that data? Well … probably not you.

If My Data Is an Open Book, Why Can’t I Read It?
(For a cooperative effort toward mutually beneficial solutions, check out #wethedata: for the people, by the people. For a list of companies supporting greater transparency in privacy, see Future of Privacy Forum supporters.)

Facebook tells a visual story of how members exchange music. It’s an amazing, artistic topography of music shared–and think of the mountains of data that went into creating it. Is there a clever marketing way to use this data? Folks are hoping so. Maybe you have a suggestion.

Facebook’s Mesmerizing 3D Music Map: Can Artists, Brands & Developers Use the Data?
(Nothing’s wrong with your speakers; there’s no audio track.)

Mapping Music on Facebook from Facebook Stories on Vimeo.

Back to Google’s “honest” maps. Suppose the only maps you see are based on what you are most likely to choose? Google Maps plans to serve up personalized maps that don’t show every feature–say, something you might want to do on an adventurous day–because that’s not what you normally choose based on all the data Google has on you. It’s a brilliant use of data for the purpose of targeted advertising. But some wonder what happens to your experience of life when the use of your data offers up thirty-six flavors of ice cream where one’s vanilla, the flavor you tend to pick, and the other thirty-five are … vanilla?

My Map or Yours? Google’s plan to personalize maps could end public space as we know it

Personality profiling gives businesses a far better idea what potential customers will respond to. But giving every potential customer a personality test is not feasible. What’s a business to do to improve conversion rates on advertising campaigns? Turn to Twitter.

No Hiding Place: A Plan to Assess Your Personality From Your Tweets

Please share if you think others would benefit.

My Kingdom for a Smoothly Run Conference

Today's "Underappreciated Technology of the Day"Right now, I am in Sweden, preparing to keynote a conference, thinking that the organizers are probably worrying about managing all the details. Every event organizer hopes their event will satisfy all their constituents but know how challenging that can be. They want to do their best to make the conference run more comfortably for the attendee and more efficiently for the hosting organization (while maximizing ROI) and speakers delivering the best content.

Planning and executing flawless events has never been an easy mission and lots of credit goes to those talented event organizers who pull it off regularly. However, what if it could be made easier? Today’s event organizers are flooded with more data than ever but with fewer resources to handle all these data sources.

On the other side of that coin, I speak at fifty-sixty conferences, give or take, every year. I know what poor conference experiences feel like. The experience a host serves up is critical, and how event planners use the data they collect can make or break the experience for attendee and organizer alike. Increasingly, big data strategies could help manage all the logistics associated with events—and they can do it in real-time, as the conference is taking place.

I was invited as one of the many “VIP influencers” to speak at IBM’s SmarterCommerce Summit (Glen Gilmore explains what this means). During and following the conference, many of us related our enthusiasm via social media methods of blogs and tweets. IBM neither asked nor required anyone to blog or tweet about the event, but many did. It was difficult not to share enthusiasm for the ways IBM understands how commerce can get smarter (see Bryan Kramer’s reactions).

People are doing many mind-blowing things with big data technologies, but IBM addressed a matter near and dear to my conferencing heart, and I would like to share my enthusiasm: I have never attended an event run more smoothly.

At the conference, Alliance Tech demonstrated to me how they accomplished this feat. They use RFID technology, embedding sensors in badges to help organizers equipped with iPads manage a variety of tasks:

Track the real-time behavior of people at conference trade booths to evaluate a range of key metrics to encourage more at-show sales and develop an intelligent show strategy:

  • Ascertain the number and quality of leads individual exhibitors are generating
  • Monitor the flow of individuals through the conference spaces as well as keep track of audience numbers and compare them with session evaluations to determine the popularity and value of individual speakers.
  • Evaluate through social media the opinions and comments of people involved in the event to learn customer reactions and preferences.

Information on what is happening in real time helps organizers do things like negotiate the number of breakfasts needed each morning given the attrition rate of participants. Are more chairs needed in a late afternoon session? Does partitioning need to be rearranged to increase room size? Was a person who is criticizing a speaker actually sitting in the session? This information and more is at staff’s fingertips the second they need it.

Social media tools can expand an event organizer’s understanding of what is happening then and there. To demonstrate how these applications fit in, IBM partnered with several companies to set up a social media command center. Whom did they monitor on the dashboard? Every participant.

Smarter Commerce 2013-05-21 (01)

OneQube [] (the left, white half of the dashboard), a company specializing in relationship management and engagement in Twitter chats and hashtags, displayed real-time information on all mentions and tweets for each participant. At one point, I ranked as a Jedi in the volume of real-time impressions; kind of cool since i hadn’t ever been a Jedi before. 🙂 That along side the Foreigner concert IBM put on, gave me real flashbacks to the late 1970s.

Smarter Commerce 2013-05-21 (03)

MutualMind [] (the right, black half of the dashboard), a social media monitoring and analytics company, set up an example of real-time analytics that can help businesses understand their customers in social context.

Smarter Commerce 2013-05-21 (02)

Eric Gore describes the specifics of MutualMind’s analytics.

In addition to its live feeds, MutualMind sent me a copy of “My Personal Profiler” document.

Smarter Commerce 2013-05-21 (MutualMind 01)

A number of bloggers who were at the conference have a hundred thousand+ followers. I am not one of those bloggers. But when it comes to the “klout score,” a measure of influence, I scored very high among the attendees, not because I have the biggest social reach, but because the people who follow me are skew very influential.

MutualMind also prepared a word cloud of my audience’s topics; this draws from the frequency of words they put in their Twitter profile to show who they are and what they are interested in. Many people’s profiles are filled with words related to “life” and “love” such as ‘coffee’ or ‘sailing.’ I was surprised to learn what my word audience topic cloud looked like.

Smarter Commerce 2013-05-21 (MutualMind 02)

Almost every word is indicative of my area of interest and occupation. This word cloud suggests my Twitter connections are in line with the audience IBM wanted to reach during this conference and demonstrates what others find as notable value in the content I shared.

Smarter Commerce 2013-05-21 (MutualMind 03)

From social media tools like these, event organizers can work with a level of business intelligence that hasn’t been possible before. Who is influencing whom? What are they saying that would help planners plan more effectively? What do participants want?

Logistics for a single event are complicated, but managing conference logistics as IBM is doing takes commerce to a whole new, smarter level. As someone who speaks at fifty-six conferences each year, I would be thrilled if all event planners were applying data in this way.

photo by: kevin dooley
Please share if you think others would benefit.

Using Data in Realtime: an Interview with Alex Miller, QVC

Entering HyperspaceLucky are those who do not need to convince their business’s leadership of the value of using big data strategies to shape relationships with the customer within and across digital platforms. In 1986, the shopping channel QVC began broadcasting—in essence, operating in real-time. As Alex explains,

If you’ve ever gone backstage behind QVC and you’ve watched the live show from the producer’s desk, you understand you are in data Nirvana. It’s flowing data on three or four different screens; they are helping us understand: What is the customer really thinking right now? What are they interested in? What they like, what they don’t like? As a company, we’ve always been awash in data.

In taking the next step from analytics to the possibilities the big data era offers businesses, even organizations that value data-driven stories to improve customer relationships will have kinks to smooth out. The biggest challenge Alex identifies is how to balance the ways of using enormous amounts of data while continuing to place a high value on twenty-seven years of institutional knowledge. At times, it comes down to “letting the data tell you what it is really trying to tell you and not trying to shape the data to tell you what you want to hear.”

Listen to Alex talk about testing and experimentation as a data-driven company negotiates becoming more data-driven.

photo by: Éole
Please share if you think others would benefit.

Not-to-miss links for 5/27/13

not-to-miss-linksMicrosoft Xbox gets the living room?

With Google Fiber currently installed (or installing) in select places and Google Glass in the works, can anyone doubt who will win in the long term? For just about any room you name? Well, hopefully not one. Besides, if you’re going to fork over your data, it’s easier to do so when there isn’t a face recognition system continually connected in your living room.

Creepy much?

The Xbox One hears all, sees all, and steals Google’s dreams

Cartography used to be biased; now it’s ‘honest’?

Maybe. Debate aside, Google wants to make sure when it processes massive amounts of data, it delivers one of big data’s most attractive promises–an instantaneous, intimate digital experience tailored just for you.

Why the new Google Maps is the most honest form of cartography

Overcoming our ideas of Privacy

The more we participate in the digital world, the more public our lives become. Unprecedented deceasing privacy is the cost. It’s not just an ethical matter, it also affects personal identity. Where do you draw the line in your life?

How technology redefines norms

Will your job be replaced by a robot?

Worry a little more whether big data and robotic technologies are going to leave people jobless and role-less. Once you are done worrying, remember that economies always cycle when new technologies create a new playing field and people architect their futures.

Driverless cars, pilotless planes … will there be jobs left for a human being?

Please share if you think others would benefit.

Use the Data Roundup: May 20th 2013

not to miss links for the week1. Love the line about running up to the line with privacy and how Google Now was scary last year but applause-worthy this year.

Google Sensors Are Data Mining I/O Attendees – And They Don’t Care

2. Think about how companies who collect your data get it for free. At least one person isn’t happy about that.

A Bit(e) of Me

3. An issue-conscious app can help you decide which companies you might prefer to buy from or boycott. Let your conscience (and lots of data) be your guide.

Soon, You’ll Know As Much About What You Buy As the Company That Made It

4. A change in size leads to a change of state. A change in quantity leads to a change in quality. More is not just more; more is different, and the way we live and the way we think are about to change. Watch an interesting presentation to get a thinker’s grasp on big data.

The Economist’s Data Editor: Big data may be too hyped, but here’s how it will change the world
The video is about 30 minutes long.

Please share if you think others would benefit.

How to Adopt a Data Driven Culture with Slava Sambu, OfficeMax {Video}

We took a few moments to sit together at the Monetate Agility Summit to chat about what it takes to get your organization to use the data to enhance the customer experience. This video interview is just 5 minutes long:

One of the things I admire most about the OfficeMax culture is their use of YouTube to test which video would be used on TV for their Penny Pranks campaign a few years back. This is an organization that understands how to use the data.

Tips on becoming a Data-Driven Organization

You might be grappling with how to justify a shift to data-driven strategies within your organization. The most likely reason you’re grappling is that your business culture is resistant—perhaps highly resistant—to change. You are in good company (as it were). Slava of Office Max offers some advice to facilitate your discussions with senior leadership and other heads of departments.

When describing the nature of a corporate culture that values and supports an effective digital strategy, Slava identifies three must-have attributes for any data-driven organization.

Find the language appropriate to the way your organization ‘speaks,’ and help leadership understand how a high level of cooperation within the company and a focus on data promotes business goals.

1. You want a culture of inclusion. Everyone needs to understand what things have to happen to accomplish the strategy. Everyone needs buy-in; this includes creative, marketing, analytics and senior leadership.

2. You want a culture of ownership. Everyone needs to own the results testing and optimization tactics generate. Everyone needs to understand them and how the organization is going to use them to accomplish the broader strategy.

3. You want a culture of responsibility. Across the board, strategies need clarifying, tactics need defining. Determining success means defining success. Data teams always need to outline what tests and optimization will accomplish within the larger business strategy, and then deliver on the promises. If developments necessitate change, teams need specific ways they can reevaluate and possibly adjust the strategy.

Slava has a lot more to say, too. Just watch the video.

Please share if you think others would benefit.

How is Big Data Different From Previous Data?

Comparing apples to orangesThe three qualities that distinguish big data from all previous information-producing methods—big, unstructured and real-time —suggest a world of possibility. Businesses are creating and applying big data solutions in unprecedented ways that not only help them maximize profits, but also redefine their relationships with their customers.

Why get so excited? Is big data really so different from the kind of large-scale data processing that came before?

Sampling versus All the Data

Until recently, businesses analyzed massive amounts of data using statistical sampling techniques. This produced subsets of data the business could then analyze to infer information and make predictions based on those results. With today’s big data toolset, companies can analyze massive volumes of data all at once.

Big data not only puts businesses in the attractive position of being able to use massive quantities of data without sampling, it renders previous information structures impotent and requires new perspectives and technologies for turning data into information.


More often than not, companies have collected data and analyzed it later. Today, data analysis can take place in real-time. Businesses who can respond in real-time have the huge advantage of agility. An agile organization can act immediately on information to expand the business value of their data, no matter the speed at which it comes into the business.

There is a chance any dataset could be “dirty” (contains data that is not accurate). This is not new; it has long been one of the issues associated with data collection. As a result, businesses have always needed to understand the importance of listening to the data—not just the data providing good information, but also data generating more problems than it is solving. With big data technologies, an agile business can evaluate and correct potentially costly errors quickly, in real-time.

Data Sources

Prior to the big data era, businesses were constrained to using only structured data sources. These sources generate data suitable for relational databases. With big data technology, a business can start tapping into the huge pool of unstructured data now becoming available: video files; audio files; images; texts; tweets; Facebook posts and other yet to be created.


Data processing on a massive scale used to be cost prohibitive for most businesses. Big data can now become the great equalizer for businesses large and small. The cost structure and growing availability of big data solutions make them accessible to a greater number of businesses. As the technology continues to develop, big data can offer a Global 5 million, not just a Fortune 100, solution.

Business Structures

Big data does not change how we need to apply information from new data sources to long-standing business issues, but it does give companies a far more finely-tuned edge for honing business intelligence, and this is the arena in which business efforts to apply the information make all the difference.

Because big data is new, because it requires business cultures to rethink how big data will change the way their organizations operate, businesses that want to benefit from big data solutions must have an organized way to integrate all areas of data usage and information application (e.g., conversion rate marketing, across channels and silos). To accomplish this through experimentation, creative development and application, businesses must have buy-in from higher level superiors and C-level officers.

New Possibilities

Big data creates new possibilities of how we process data to generate useful information. Previously, analytics was a way of discovering information that already fit into the framework of how companies processed what they collected. Big data changes everything.

Explaining how it views big data, IBM writes, “Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity.”

Please share if you think others would benefit.

What is Big Data?

data in cloud comicThe nature of big data suggests a world of possibility, and businesses are applying information derived from big data in ways that are redefining their relationships with their customers and helping them maximize profits. But businesses are lacking a big-data language they can understand. A technology-based language doesn’t help most ‘normal,’ non-technical, business folks ‘get it.’

All businesses can or do generate huge amounts of data. Most do not have a real plan for what to do with that data. If you don’t understand what big data means, how can you apply big-data-generated information to your business? How will you know which big data tools will make a difference?

That’s where we hope to be of service. We wrap a language and a business perspective around the world of big data to help you develop big-data business strategies.

We start with some simple labels to define big data.

Big, Unstructured and Real-time

People usually define big data by three qualities: it’s big; it can come from unstructured sources; you can use it in real-time.


Big. Big data is … big. The-mind-can’t-comprehend-it big. We now generate every two days an amount of data equivalent to all the data in the Library of Congress before 2003. In just one minute, 639,800GB of global IP data are transferred:


135 botnet infections
6 new Wikipedia articles are published
20 new victims of identity theft
204 million emails sent
1300 new mobile users are added
47,000 applications are downloaded
$83,000 in sales take place on Amazon
61,141 hours of music is streamed on Pandora
100 new accounts are created on LinkedIn
3000 images are uploaded to Flickr (20 million are viewed)
320 new Twitter accounts are created (100,000 tweets are sent)
277,000 people log in to Facebook (6 million view a Facebook page)
2 million search queries are entered into Google
30 hours of video is uploaded to YouTube (1.3 million videos are viewed)

As soon as you read these numbers, they are out of date; the staggering volume of digital data we are creating is growing at a phenomenal rate.

When you have massive amounts of data coming from multiple channels and across silos, how do you use it? This is an important business question: what you need to know influences the data sets you relate to each other and the best formats for presenting information in a way you can quickly understand it so you can act on it.

Unstructured. The variety of data available to us is impressive. About ten percent of it comes from structured data sources and can be processed by conventional means. Ninety percent comes from unstructured sources, and processing it requires new technologies. As digital technologies for data-mining and text-analyzing develop, we increasingly use these data sources that aren’t well-suited to relational database formats.

Structured data can come from sources inside or outside a company, from utilities, government agencies and GPS-enabled devices to radio-frequency identification chips (RFIDs), site search and website clicks. These are primarily structured data sources.

Unstructured data comes from from videos, audio files (for example, telephone conversations, audio recordings of presentations), images, texts, tweets, Facebook posts and more.

One of the most powerful aspects of big data is its ability to accomplish the equivalent of comparing apples and oranges without having to normalize them as fruit across datasets.

Real-time. Ideally, some online transfers should take place almost instantaneously. If someone just used your credit card illegally, you hope your bank discovers that immediately, not at the end of the month. The speed at which you can distill useful information from your data and execute a decision confers agility. An agile business has the advantage of being able to respond immediately, in real-time, as opportunities present themselves. Big data technology makes this possible.

big data is big unstructured realtime

IBM, a leader in creating and providing big data technology, defines big as Volume, unstructured as Varied and Velocity as the speed at which the technology can stream data into the business so the business can use the data immediately. These are great definitions; they are just aimed at a more technologically-literate audience. Business people do not use this language, so we substitute these with more intuitive terms.

IBM includes Veracity—the accuracy of the data—as an important big data quality. While this is a critical concern, from our point of view, accuracy is an inherent problem associated with all data, not a distinguishing feature of big data.

“Big Data is really about new uses and new insights, not so much the data itself,” says Rod A. Smith, an IBM technical fellow and VP for emerging internet technologies.

Businesses require the technical expertise of data scientists to extract, manage and refine information from data sources. Technical considerations usually fall outside our purview.

We care about how business people make sense of the information big data generates and how you can apply big data information to your business decisions.

Please share if you think others would benefit.