Big Data and the Trust Paradox

TrustParadox

We have all become blasé about the information that we share on the internet. We openly tweet, share updates, create photos and post images about where we are, what we are doing and who we are with. We carry our mobile phones with us everywhere – and have become so reliant upon them that we have had to name a condition for the state of anxiety we find ourselves in when we leave our phones at home. It is “nomophobia” – literally the fear of having “no mobile”.

And just as our internet connection is “always on”, so too is our phone. And being always on, it’s always collecting, sharing and posting data about us. Even when it’s sitting “idle” in our pockets it is triangulating our position, beaming our latitude and longitude to satellites, connecting to wifi hotspots and cellular phone towers. Many of the apps that we use also collect and share our location – some are obvious like Google Maps and Facebook. Others not so. But it’s when we start using the phone, that the data really explodes.

The following infographic is now quite old, being originally published in 2010. It shows the “meta data” – the hidden data that is relayed along with every update that you make using Twitter. It’s not just the 140 characters of your message, but hundreds of additional characters that accompany your message, including your:

  • User name
  • Biography
  • Location
  • Timezone
  • Follower / following statistics.

And more. So much more.

AnatomyTweet

Trading privacy for convenience

The accepted wisdom is that users of these services are knowingly trading privacy for convenience. The reality is vastly different. After all, when using the internet, we are not working in full knowledge. In fact, our understanding of what we are doing, how much information we are revealing and where our data goes is extremely limited. And even when we choose to share location information with an app or when we accept notifications, chances are that we will forget that consent has been given. Or the context in which that consent was given will become lost in the daily grind of our busy, connected lives.

This plays well for those platforms that collect, harvest and sell the data of their users. In fact, it’s one of the business models that many startups rely upon – data collection, harvesting, sale and exploitation is the name of the main game. But there is change in the air, and we can expect that these business models will increasingly come under greater scrutiny and pressure. A 2014 an EMC poll revealed that only 27% of those surveyed were willing to trade their private information for a more convenient online experience. And over half (51%) straight out said “no”. Moreover:

The majority also believed “businesses using, trading or selling my personal data for financial gain without my knowledge or benefit” were the greatest threat to their online privacy.

These beliefs and expectations were further reinforced in the Pew Research Center’s Future of Privacy report, where “Some 55% of these respondents said “no” they do not believe that an accepted privacy-rights regime and infrastructure would be created in the coming decade”.

Yet despite an inherent and ongoing suspicion of corporations and governments, the Edelman Trust Barometer for 2016 reveals that the general sense of trust is improving. Edelman’s research describes a well educated and well-resourced segment of the population (approximately 15%) as the “informed public” – and measures trust in the wider population as well as this narrower segment. To qualify for the segment “informed public”, people must be:

  • Aged 25-64
  • College educated
  • In the top 25% of household income per age group in each country
  • Significant consumers of media and report high engagement in business news.

This also means that the “informed public” would be considered a “tech savvy” audience.

While trust has grown overall, it has accelerated faster between 2015 and 2016 in the “informed public” segment. And this is what makes this report so interesting. Despite a wide and growing concern around big data, meta data and data analytics, those who are MOST LIKELY to know and understand the use to which their data will be put, are reporting an improvement in their sense of trust.

And it is this “Trust Paradox” which offers both hope for business and a warning. For while trust has been improving, business and government is only as trusted as the last security breach or unexpected outage. The IBM/Forbes’ Fallout Report estimates that “lost revenues, downtime and the cost of restoring systems can accrue at the rate of $50,000 per minute for a minor disruption”. A prolonged problem would take an even greater toll on brand reputation and business goodwill.

The risk of a breach or outage, however, is not shrinking but growing, thanks to the proliferation of “shadow technology”, expanding supply chains and growing online activism. And as digital transformation continues to take on an ever greater role in customer experience, the potential for consumer impact and reputational damage also grows.

John Hagel suggests that as brands work towards a “trusted advisor” status, that they will have a “growing ability to shape customer purchasing behaviour”. But brands will only have this luxury while the Trust Paradox works in their favour. At present, the Edelman Trust Barometer suggests the balance of power remains with our peers. We trust them more than anyone else. And that means securing or “scaling trust” (using John Hagel’s terms) remains our real challenge in the years ahead.

Periscope Captures from the ADMA Global Forum

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Live streaming using apps like Periscope and Meerkat have revolutionised conferences of all kinds. No matter whether you are hosting a small lunchtime gathering of friends, colleagues or experts, or attending a massive conference, these easy-to-use apps allow you to share what you are seeing with the wider world. Or at least those who follow you on social media.

With this in mind, I thought I’d put both Meerkat and Periscope through their paces at the ADMA Global Forum. The ADMA Global Forum brings the world’s leading data driven marketers together to share insights, best and emerging practices, case studies and strategies. This year, there is strong representation from technology firms with good stories to tell. Oracle, IBM and Marketo are represented. Facebook is too. I also dropped by the stands of IVE, Minfo, and others. This year is an improvement on last, but there’s more work to be done on their exhibition stands and their ability to talk to marketers on their own terms. If you have a tech brand needing to talk “marketing”, then maybe we should talk too.

I live streamed and recorded a number of sessions and embedded them below. Tomorrow I will go deeper. Do some interviews. Chat with the teams on the exhibition stands and some of the audience. Let me know what you’re interested in and I will see who I can talk to.

@AndyVen from TheOutNet

Marketing Automation with @Missguided

Case study from @Missguided

Case study from Bosch

Case study from Regions Bank

Content Strategy from @TheOutNet

Case study from Getty Images

Personalisation strategy from Sitecore ANZ

Five Insights into the Psychology of Twitter

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Statistics and sampling are an amazing thing. Even if, like me, you have a healthy scepticism about the way that data is analysed and interpreted, it is difficult – if not foolhardy – to downplay the inevitability of data. Just look at the various disputes around the veracity of climate change – where statistically irrelevant interpretations have derailed important decisions, changes and commitments. Eventually, even the hardiest data curmudgeon will need to yield to the truth of the climate science data – perhaps only as their seaside apartment is swept into the arms of the sea. For though there may be outliers and anomalies in the data, sampling – where carried out correctly – can yield tremendously accurate insight. As Margaret Rouse explains on the TechTarget website:

Sampling allows data scientists, predictive modelers and other data analysts to work with a small, manageable amount of data in order to build and run analytical models more quickly, while still producing accurate findings. Sampling can be particularly useful with data sets that are too large to efficiently analyze in full — for example, in big data analytics applications.

And it is sampling that makes Twitter one of the more fascinating social networks and big data stores of our time. While Facebook grows its membership into the billions, its underlying data store, its connection and interaction architecture and its focus on first tier networks also limits its capacity to operate efficiently as a news source and distribution network. Twitter on the other hand, with its 200+ million members, provides a different and more expansive member engagement model.

During our recent forum presentations on the voice of the customer, Twitter’s Fred Funke explained the view that Twitter was “the pulse of the planet”. Using tools as simple as Twitter search or Trending Topics, Twitter users can quickly identify topics that important to them – or to the broader local, regional and global communities. And, of course, with the new IBM-Twitter partnership, there are a raft of tools that allow businesses to go much deeper into these trends and topics.

In doing so, however, we have to ask. What are we looking for? What information will create a new insight? Which data points will reveal a behaviour? And how can this be framed in a way that is useful?

Five Buyer Insights that Drive Engagement

Just because interactions are taking place online doesn’t mean that they occur in isolation. In fact, our online and offline personalities are intricately linked. And as the majority of our digital interactions take place via text, linguistic analysis will reveal not only the meaning of our words but also our intention. Some things to look out for and understand include:

  1. Buying is an impulse: As much as the economists would like to believe we act logically, we know that buyers are emotional creatures. We buy on whim. On appeal. On impulse. And there is no greater impulse these days to share an experience (good or bad) via Twitter. Look particularly at the stream for comments tagged with #fail. It is full of opportunity for the responsive marketer keen to pick up a churning customer having a bad customer experience.
  2. The customer journey is visible: While we are researching our next purchase, digital consumers leave a trail of digital breadcrumbs that can be spotted using analytics software. For example, we may tweet out links of videos that we are viewing on YouTube, share blog posts related to our pre-purchase research and even ask directly whether a particular product lives up to the hype. Just take a look at the #lazyweb stream around the topic of Windows10.
  3. Understand the pain to optimise the opportunity: When engaging via social media, it is important to understand the challenges or “pain points” that your customers (or potential customers) are facing. Rather than spruiking the benefits of your own products, focusing on an empathetic understanding of your customer’s needs more quickly builds trust and is grounded in a sense of reality. The opportunity with social media is to guide the journey, not short cut it.
  4. Case studies build vital social proof: No one wants to be the first to try your new product. Showing that the path to customer satisfaction is well worn is vital. Use case studies to pave the way.
  5. We buy in herds: Mark Earls was right. Not only do we want social proof, we prefer that proof to reflect on our own sense of belonging to a group or movement. Remember that we go where the other cows go, and structure your social media interactions accordingly.

The folks over at eLearners.com have put together this infographic on the psychology of Twitter. They suggest that we tweet for love, affection and belonging. It may be true, but sometimes we just also want to vent. And every vent is a market opportunity.

psychology of twitter

Forget Big Data, It’s Time for Big Narratives

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It is easy to get excited about big data. After all, it’s lots of small pieces of data woven together into a patchwork that stretches our imaginative capacity. Just think, we’re creating more data every two days than was produced from the dawn of civilisation up to 2003 (or so Google’s Eric Schmidt claims). That means that every photo, status update, movie, podcast, purchase, share and any other form of interaction that we make on a digital forum – PLUS all the metadata of that interaction – is adding to a massive pool of data that sits like a great digital artesian basin underneath our digital experience.

The question about all this data, however, is what do we do with this big data? Sure we can mine it, connect internal and external data. We can use it for retargeting. Or forecasting. Or analysis. We can put it into charts and infographics and in doing so, add our own efforts to the big data explosion. But it feels like we are just scraping the surface. It feels like we are in our digital infancy when it comes to big data.

But there are a few companies who are innovating on the edge and taking a different approach. For these companies, big data is just a means to an end. The real value is not in the data but in the capacity to tell stories with that data. It’s the realm of big narratives – and it is as exciting as it is terrifying.

The team at Narrative Science have been focusing on machine learning and linguistics for some time. Their natural language generation platform takes big data and applies artificial intelligence to it in such a way that reports are not just visual but contextual. That is, there is the result and the reasoning all-in-one report.

I have written about QuillEngage previously, the platform that turns your Google Analytics data into a summarised report email. So I was interested to see what would come out of their new Twitter report.

twitter-quillengage

Based on an analysis of my Twitter traffic and the traffic of my recent followers, Quill examined around 13,000 tweets to produce the report. Most interesting to me was the analysis of my own tweets and the topics that “my community” engage in. While my follower numbers and ratio put me in the “99th percentile of Twitter users measured by followers”, the report provides little in terms of suggestions for growth / improvement. But it does confirm what I suspected. And in most cases, that’s how many marketers are using big data at present – as a sense check. A validation.

But as technologies like this get better, more automated and programmatic, there’ll be less sense checking. Less validation. And more action. It’s just that that action won’t be taken by you or I.

Voice of the Customer @ #IBMconnect

IBMconnect-conf

The IBM Connect roadshow moves from Auckland to Sydney and then to Melbourne over the next few days. Come along and learn how you can turn data into business value.

The Known Unknowns – Small Steps Big Gains with Watson Analytics

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Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know.
Donald Rumsfeld

My morning starts with a river of data. First up, there is email – a quick run by the inbox alerts me to urgent issues, questions, client inquiries and news. Every email I open, every link that I click and every article that I read is marked, tagged, tracked and collated. And then it’s over to LinkedIn’s news feed to check what is happening in the industries I care about. Having built a substantial network of well-connected and insightful connections over the years, I get a very quick sense of what is trending globally, what is vital locally and what needs to be reported or responded to. Finally, I switch to Facebook. I am blessed with opinionated and smart friends who share and increasingly, the richness and quality of news and insight available through that network is out-pacing all other channels.

But what am I doing here? I am working with Donald Rumsfeld’s mantra. I am looking to these networks to tell me the things that I don’t know that I don’t know. Essentially, this data is helping me look beyond my radar.

The same approach can be applied to marketing. In fact, for marketers to remain relevant and responsive – we need to be looking beyond our own radar.

For as long as marketers have been marketing, we have used media to reach our customers. We’ve equipped our teams with megaphones and messages and marched them to the perimeter of the business compound. To reach our customers, we buy and create media – after all, as the adage says, “fish where the fish are”. But this is the inside-out marketing model from the 20th Century – and social media has turned it inside out.

Turning to sites like Twitter or Facebook – or even LinkedIn for the B2B marketer – can feel like facing a firehose. The torrent of information coming through is astounding. Just take a look at the recent social media statistics for Australian audiences:

  • Over 13 million using Facebook
  • 13.5 million using YouTube
  • 2.5 million using Twitter

But it’s really not the big numbers that are important here. It’s what you do with them.

The known unknowns of our customer data

Just as I do each morning, marketers need to be thinking selectively about their customers. Rather than aiming to speak – or “connect with” all 2.5 million Twitter users in Australia, why not start somewhere easier? Why not start with the “known unknowns”? Why not start by figuring out WHICH of our customers are using Twitter or Facebook (ie a lot of them), and improving our understanding of those people?

Most modern CRM platforms have fields that allow you to collect the Twitter handles or Facebook profiles of your customers. Why not start by understanding how many of your existing customers have this information included in their profile? This gives you your known unknowns:

  • Run a report on your completeness of social media profile data in your database
  • Use Facebook custom audiences to match Facebook profiles to your existing email database
  • Do some profile matching via Twitter to do the same
  • Run an email campaign asking for that one additional piece of information. Provide a useful incentive. Make it worthwhile.

For most organisations, taking these small steps would take less than a day. And it paves the way for much deeper exploration.

Combining Voice of the Customer and Analytics as your over the horizon radar

Once you know who your customers are on social networks, it opens the door to a much richer experience for both you as a marketer and your customers as consumers. And what you’ll find on social networks is not the well-manicured conversation of corporate marketing – you’ll find the very direct “voice of the customer”. Generally, however, our “social listening” platforms are built around our own keywords. Our products. Brands. They are only sometimes built around understanding our customers pain points, needs and expectations. This means we are again working from the inside-out – working with the known knowns.

Thankfully, powerful natural language processing is beginning to provide the analytics horsepower we need to decipher social streams. I have explained previously about the way that IBM and AusOpen collaborate to transform customer experience at the Australian Open Tennis events – but analytics is no longer the domain of big business. With platforms like IBM Watson now available at an affordable rate (starting at around $50 per user), you don’t need to be “Tennis Australia” nor a data scientist to understand what’s going on with your market. You just need to understand your business.

Take a look at this video to see how you can use Watson and Twitter data to analyse retail sales. Look at the way that the language in the real time reports is structured around the way that marketers work. Rules are setup and then data populates accordingly. But most interestingly, because Watson works with natural language – it works with the language of the marketer as well as working with the language of the customer.

For marketers, this means that Watson does the hard work of identifying the most interesting facts contained within your data sets, letting you focus on making the right decisions about what happens next. For example (at 2:13), “sales by state” is flagged. Watson chooses the best representation of the data (in this case, a map) but also provides a “ribbon of data” that can be used to interrogate and analyse at a deeper level. Typing in a search related to what you need to know (eg “tweets by hashtag”) turns that data into a report that lets you see immediately what happened to your sales data and why.

Suddenly that river of Twitter data becomes understandable. A connection can be made between your business results and the social media data coming from a particular channel in a particular location.

And in case you need to tell the story of your digital marketing and your analytics to your executive team – or to your customers – with a few clicks, the visualised data can be compiled into ready made infographics. Now you not only have a custom radar to understand your customer – you can link your customer and business value together. Will this make you a better marketer? It will certainly make you more relevant to your customer – and that is a win all around.

Marketing and Dating: How to Get a Date by the Numbers

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Dating is big business. There are generic dating sites designed to help you find a date, a life partner or someone just to hang out with. There are also incredibly focused dating sites that are designed to introduce you to other people who have the same particular passions and interests as you. Maybe you are looking for a “sea captain” or perhaps you just hate it when Movember finishes and need to sate your passion for the tache. Whatever the case, if you look hard enough you’re bound to find a dating site designed just for people like you (yes, you crazy cat lady).

In many ways, the challenge of dating is the same challenge that marketers face. We’re all looking for that one-to-one connection – though often we struggle to a way to meet and start a conversation. In both cases (marketing and dating), digital disruption is creating both opportunities and challenges. And at the heart of this is data.

Inga Ting reveals that what we say in our dating profiles and what we want are often completely different. Dating sites – just like data-driven marketers – are less interested in “stated intentions” and more interested in actual behaviour. By looking at online behaviour – the things that we like, connect with, share and return to – marketers can adjust their profiling to reach and more deeply engage potential customers. This algorithmic approach relies not on focus groups and market research but on an adaptive approach which operates between your stated profile (self designed) and the actions you take online. In the world of online dating it means operating in-between spaces:

Behaviour-based matching is adaptive. It compares what you said you wanted with how you behave to work out things you might not even know about yourself.

For example, you said you wanted a partner with a steady income but you keep messaging “pro-bono computer game testers” and “freelance writers”, so the algorithm changes its recommendations.

Our profile

But, of course, while there can be volumes of data about ourselves online – we are also highly visual. The rise of photo based apps like Tinder for example shows that sometimes dating (and even marketing) is only skin deep. Relying on your photo and your location information, Tinder matches people based on whether they are close and interested (you swipe a prospective date’s photo to the left to reject and to the right to connect).

For those who are serious about dating, perhaps a single app is not the answer. The “multichannel” approach that works for marketers may yield better results. Take for example, the data from Axciom’s infographic (ht Will Scully-Power) that reveals that, in Sydney:

  • Single females outnumber males at all ages except the 18-24 age group
  • Potts Point is home to the most singles
  • Wine enthusiasts are most likely to reside in the Eastern and Inner West suburbs

If you were a male in the highly competitive 18-24 age group, a multichannel (or omnichannel) marketing approach to maximising your chances would include:

  1. Establishing your base profiles on high traffic sites
  2. Create a profile image that shows your passion for fitness and interest in fine wine (please be tasteful)
  3. Spend time in cafes in Potts Point using Tinder

Of course, you could pepper your profile with quotes from Shakespeare, but that may be overkill. Remember, that the algorithms will override your stated profile anyway – so your true intentions will always be revealed in the data – based on who you swipe right and who you swipe left, who you message, like and connect with. And like all good marketing, the question comes down to ROI, engagement and outcomes. I hope you get your algorithm right!

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The Amazing Case of the Disappearing Technology

BranFerren

BranFerren

Technology is stuff that doesn’t work yet.

— Bran Ferren

Bran Ferren’s words echo across the wifi to us like a premonition. The former President of R&D at Walt Disney Imagineering’s deep understanding of the way people use and engage with technology is only starting to play out in the devices that we so readily take for granted. The fact that we can call a piece of technology, a “device” at all shows how far we have come; after all, a device is something personal, knowable, intimate. And it was only twenty-odd years ago that carrying a “mobile phone” could put your back out. Personal technology is shrinking at a considerable rate.

Big Machines, Small Data

For decades, technology has driven business innovation, resulting in the rise of professional services firms, technology companies and most recently, software platforms. Until the early 90s, we designed systems around single business functions – like purchasing or order management. While this was a huge improvement on previous systems, it entrenched departmental silos and required duplication of work – put simply, the same information had to be entered into completely separate systems. Occasionally, the IT teams were able to integrate systems – connecting some pieces of data together, but this also required governance, standards and compliance – which added cost and complexity to already complex systems.

At the centre of this data frontier were the CIOs – vital drivers of innovation and productivity in almost every business. And held tightly in their grasp was information.

We realised that the faster we could crunch business information, the faster we could make decisions. Accordingly we built electronic supply chains, implemented ERP systems and automated what we could. We brought disparate systems together with a single package providing a reliable flow of data from one department to another. We had massive computers pumping relatively small amounts of data through relatively small, connected pipes. In some cases, remote controllers would be hooked up to servers via dial-up connections – and these ran multinational businesses!

The focus for all this innovation was the “back office” – far away from the prying eyes of the customer.

The Rise of the Front of House

While ERP innovation was driving efficiencies within the hardened arteries of businesses, the sales and marketing folks were still working from the same trusty rolodex and dog-eared business cards they had used since the Great Depression. But Tom Siebel had other ideas. His company was to do to customer relationships what SAP had done to finance and enterprise resource planning. The vision was – as it remains today – a single view of the customer. Like many grand visions, the reality remains tantalisingly out of reach.

But this focus on customer facing business functions, brought sales and marketing into the connected enterprise. Customer billing systems, processing, pipeline and opportunity management and a range of other functions were all digitised – and the field of business re-engineering flourished. Consultants had learned through the ERP years that return on investment lies in business users actually using these systems – and that meant customisation, training and change management. In large enterprises, this task was enormous – but was largely contained by the limits of the business. The focus was on engineering the business not extending beyond the safety of the firewall.

After all, even the top of the range, slimline laptops were clunky, heavy and slow in performance. And the business systems were ugly, hard to use and the data networks were notoriously unreliable. It appeared that innovation was always going to stop at the dizzy limit of a thin blue ethernet chord. And everything from the design of the software and hardware through to the challenges of remote access served to remind us that we were always operating out of our comfort zones – that we were dealing with technology that could both help and hinder us.

Outside-In Innovation and the Crowd

While most businesses were licking their wounds after the dotcom bomb, a new generation of tech entrepreneurs flew below the radar to create a whole new way of connecting the dots around businesses. These emerging social networks skipped the B2B market and launched direct to consumers, corralling vast swathes of the population into tightly bunched, loosely connected groups.

Similar to the way that dolphins collaborate to feast on an abundance of school fish, fast moving digital platforms like Google, Facebook and Yahoo skirted around our flanks and drove us together. Overwhelmed by the speed but excited by the possibilities, we willingly handed over our privacy, location and even identity in order to join with others who were “just like me”.

These platforms, working at warp speed, innovated at the speed of customer experience. They were unencumbered by years of process, archaic business systems and entrenched ways of working. They pushed out new features to the delight or disgust of their members, changed as necessary and moved on.

Sensing a fickleness in the consumer landscape, these fast growing startup enterprises blitzed past the “sense-and-respond” mantra proffered by management consultants the world over and created “lean” businesses that responded to changing conditions through automation, strategic outsourcing and peer-oriented customer service. The suggestions of the crowd – the paying customer – drove changes in business models, product features and even business strategy.

And all this outside-in innovation was happening from the comfort of our homes, with the convenience of technology we could hold in our hands.

The Internet of Things Gives Way to the Internet of Me

The real revolution in all this is three-fold:

  1. Consumers have built their own ecosystems around the experience that they want to create and curate for themselves
  2. “Technology” is disappearing from our lives, shrinking to a size that can be incorporated into our daily fashions
  3. Data is proliferating and permeating devices, systems and everywhere in-between

At the moment we are seeing the Internet of Things gaining traction in our homes, workplaces and public spaces. Connected by low bandwidth protocols like bluetooth, devices like Withings weight scales function like an analogue machine, displaying your weight – but add an additional dimension powered by the web and big data. Not only is your weight captured, your profile is queried in real time, and your height details are returned. Then your BMI is displayed while your latest reading is transmitted back to the cloud.

In some retail stores, sensors like iBeacons track your movement and signal your identity based on the apps running on your phone. Store assistants are proactively updated on your current status, interests and so on, and are ready to more readily assist you. Sound creepy? It’s already happening.

This is no longer the internet of things, but the internet of me. We are creating personal versions of the same kind of ERP networks that were developed in the 90s – linking our payment systems (banks) to our supply chains (shops) through sensors, apps, profiles and devices that we carry or wear at all times. And all of this is happening largely out of our view. It’s invisible. And once it becomes invisible it becomes “the way of life”.

No Left v Right Brain – And Other Mythconceptions

Myths

I love this infographic on various urban myths that permeate our modern existence. By author, David McCandless, it visualises some of the most Googled myths and misconceptions – with larger bubbles indicating that it is a common search term. Some of my personal favourites include:

  • That you SHOULD wake sleepwalkers
  • That bats are NOT blind
  • There is no solid division between the LEFT and RIGHT hemispheres of the brain.

What surprises you?

1276_Common-Mythconceptions_Oct22nd

Who Needs Another Day in Password Land?

Only1u

I have a sneaking suspicion that the most successful call to action in the world is Forgot Password?. That small link that sits below a password field is my friend. After all, I have passwords for every blog, social media site, news sites, business sites, bank, retailer and online tool or cloud provider that I use. The use of passwords is, in itself, a personal big data challenge that I have yet to solve.

I have a password manager on my phone, some of which is current. Some outdated, and some automated. I have a list which I keep which is slightly unreliable – mostly because I fail to manage it scrupulously. I have randomly scrawled password scattered through notebooks I can no longer find. There is encryption for the cloud (which also requires some kind of key) and there is even fingerprint identification that works with iPhone 5 (which is actually pretty convenient – even if slightly scary in terms of identity management/theft/security/tracking).

So I was interested to check out the new password manager from There’s Only 1 U. Actually, it was the video that tipped me over. Produced with a great sense of self-deprecation, it captures the frustration that many of us feel when it comes to password management and online security. To be honest, it’s a scene too long, but it did the trick.

Is it useful? I’ll let you know after some hands on use.

First indications are positive

Like most password managers, there’s some pain up-front to set up your sites and accesses, but the long term gain is what is on offer.

The UI and step-by-step setup is relatively straight forward, though very wordy. I was able to easily use the phone’s camera to scan my face and setup the security. There is something reassuring about scanning your own face as a secondary form of authentication. And so far, I have not been able to trick the scanner by using a photo.

There is a good selection of websites, apps etc that can be easily and quickly configured for access. And it’s relatively easy to add your own custom sites using the same process. Of course, you can still use Touch ID or you can use the facial recognition engine.

But the question is traction. Will I use it again? Will I uninstall? Will I just forget about it? Ask me again in a week. In the meantime, register for the app here or get more information about it here on their website.