FreshNetworks Blog

Forget-me-nots (Photo credit: churchofpunk)

After just a few days in the job, the UK’s first youth crime commissioner, Paris Brown, resigned over some of her past Twitter postings. There are no doubt many posts that she wishes could be deleted, forgotten forever, and she is not alone. As we leave more and more behind us in our digital exhaust there will no doubt be Tweets, photos, comments and the like that all of us would like to be forgotten. And not just because they were misjudged in the first place, as was the case with Paris Brown.

Social media will provide a continual record of our lives – of the detail of what we did and what we said at a particular time on a particular day in the past. Some people liken this to a diary, but it is different in two fundamental ways:

1. A diary is always written after the event, reporting something we did in the (near) past; our social media records were composed in the heat of the moment, in real time
2. What we write in a diary is selective, we think about what it is from the day that we want to record; our social media records are less so – our posts and photos often go through fewer filters

So social media is leaving behind us a very different set of records – records that are written in real-time, are less filtered, and tend to discuss the detail of what we were doing or thinking at a particular point in the past. And, in many cases, they can be seen by anybody – without us there to explain where this particular record fitted into our lives at the time; without context.

These new records present a number of potential challenges to us in the future, not least to how we remember and think about our past.

• We tend to forget detail – except for the most special of memories. Rather we remember events at a macro-level – we know broadly speaking where we were and when, what we were doing at different stages in our lives, and the things that happened to us. Our social media records are only the detail – they provide no context and no structure to our memories. Just a set of detailed comments that we will not be able to escape from.
• We think of the past through the lens of today – we interpret what we did and said based on our current experiences, beliefs and moral compass. This is why even reading diaries from your childhood can be cringe-worthy. Our social media records will come with no interpretation; there will be no escaping what we said or thought in the past.

So, our social media records will provide a different view of our own pasts (for ourselves and for others) than we might currently want to portray. And this is why we might want to explore a right to be forgotten online, a right for our posts to be removed or replaced and for us to curate our own pasts. Not for that odd ill thought-through Tweet, but because social risks changing the way we make and store memories of our lives.

Facebook Home

Facebook Home, announced last week, is the social network’s latest bid to expand its social web over mobile devices. The Facebook Home app, available on Android phones, integrates the phone’s operating system with the Facebook platform by taking over your home screen with your Facebook news feed  and making SMS messaging look like a Facebook chat. This integration also gives Facebook the potential to mine vastly more information about its users than ever before.

What data can Facebook Home mine from your phone?

1. GPS: According to Gigaom, Facebook’s integration with the Android operating system allows Facebook to receive constant information about the phone user’s whereabouts via the phone’s GPS. From this Facebook could potentially work out things like where you live, based on your phone’s GPS location between the hours of 10 p.m. and 6 a.m.
2. The phone’s accelerometer: The phone’s accelerometer could tell Facebook whether a phone user is walking, running or driving.  Adding this to the data Facebook already has about you, it can build a much better profile of its users, such as the places you shop, the restaurants you dine in and where you might spend a weekend pursuing your hobbies.
3. Chat Head: Facebook Home will bring together Facebook chat with SMS messages, so that your messages will get Facebook-ified and potentially, through the Android launcher, allow Facebook to read your messages sent outside its service.
4. VoIP (Voice over Internet Protocol): Facebook has already been inching into this space for a while, but Facebook Home could bring internet calls via Facebook front and center to a user’s mobile experiences and bypass phone calls altogether.  Facebook calls would mean you wouldn’t have to look up someone’s contact details (Facebook already has them) and you wouldn’t have to pay international rates, giving many incentives to use a Facebook VoIP service. Although Facebook is not likely to actually monitor your calls, it would be able to get a lot of information such who you call, and how long you talk them.

What might it do with this data?

David Jacobs of the Electronic Privacy Information Center (EPIC) suggests that the increased information available to Facebook via the new app, will help it to monetise your personal information through advertising. Advertisements won’t be in the first release of Facebook Home, but future versions will include an ad feature which gives Facebook an unprecedented opportunity to aggressively push commercial messages at its users.

However, there have been more and more news stories about a potential privacy backlash, as users are trying to weigh up the benefits of sharing increasing amounts of information with the risks of losing their privacy, and the potential damage caused by personal data getting in the wrong hands. As Jan Dawson, senior telecoms analysts at Ovum points out, “users don’t want more advertising or tracking and Facebook wants to do more of both”.

6/52 - Work (Photo credit: whatmattdoes)

There is much talk about the looming gap between the opportunities being created by big data and finding workers with the right skills and expertise to fill these positions. A recent study by Gartner predicts that by 2015, 4.4 million IT jobs globally will be created to support big data opportunities within brands.

With these opportunities come challenges, not least how you use the data in a responsible way without alienating the customers you are hoping to offer better services and products to. This has led some to suggest brands need a Big Data Ethicist, charged with ensuring data is used in a responsible and ethical way.

Ways your business could be damaged by use of big data

The damage to a brand from use of big data can impact in three main ways: the brand and reputation, the relationship with customers and financial. Three simple examples of these are:

1. Damaging the brand reputation: Target’s brand was damaged when their predictive analytics team knew about a teenage girl’s pregnancy before she could break the news to her own father. This sparked outrage about the legality of Target’s actions and many people have started questioning various companies’ marketing techniques and using their club-cards
2. Alienating customers: TomTom, maker of  popular GPS navigation devices, was forced to apologize after news that Dutch police had used data gathered from drivers TomTom devices to set speed traps up. The scandal resulted in TomTom slashing their 2011 sales figures down by $90 million and company’s CEO issuing a statement assuring customers that it would investigate how the data they sold was being used
3. Fines: Google was hit with a $22.5 million fine for embedding software that bypassed privacy settings of millions of Apple devices in order to collect users viewing data for their direct marketing efforts.

The role of a Big Data Ethicist

The role of a Big Data Ethicist in any firm should be to build a culture in an organisation of using big data in a way that strengthens the relationship with customers, respecting their desires for privacy but helping to deliver better products and services to them. Typically a Big Data Ethicist would be responsible for:

• Thinking through the legal consequences and implications of the data and information your company will be creating, using and publishing
• Build customer trust over the use of your big data activities
• Consideration of long-term, far-reaching implications of the use of big data technologies, for example a backlash from consumers over companies’ use of their data
• Help decisions makers plan new big data activities

How might this role fit into existing roles and processes? Are brands that are experimenting with big data considering these ethical issues?

waves (Photo credit: .EVO.)

Perhaps one of the best overviews of Big Data was published last year by Harvard Business Review: Big Data: The Management Revolution, by authors Andrew McAfee and Erik Brynjolfsson. The authors explain that using big data will cause a “management revolution”, in that it will enable managers to measure and know a lot more about their business and customers, and lead to better decisions and improved performance.

Whilst not a substitute for reading the article itself, the three main questions addressed are summarised here.

What makes Big Data different?

The things that make big data different from analytics are:

• Volume: quite simply, there’s a lot more data made now than ever before, specifically “as of 2012, about 2.5 exabytes… each day”, which equates to roughly 50,000 million “filing cabinets’ worth of text”.
• Velocity: all this data is being created really fast, and notably in nearer to “real-time”.
• Variety: all this data comes in many forms, including social data – i.e. information generated and held in social networks, such as Facebook and Twitter. In addition, much of it is unstructured, i.e. “not organized in a database”, which presents the problem of analysis. However, analysis equipment and approaches are also ever evolving, and becoming increasingly cheaper.

Are data-driven decisions better decisions?

McAfee and Brynjolfsson assert that data-driven companies do indeed perform better in relation to typical financial and operational measures than less data-driven companies. For case studies, they cite a major US airline which used big data to better predict when planes would actually land, and thereby potentially saved “several million dollars a year at each airport”. They also cite Sears Holdings, which was able to analyse its large data sets much faster using Hadoop cluster stores, and could generate more pertinent and personalized promotions in closer to real-time (1 week instead of the usual 8).

They then move on to how it’s typically an organization’s HiPPOs (the Highest-Paid-Person’s Opinion) who make the important decisions, with many relying “too much on experience and intuition and not enough on data”. Data should be used more, and organisations should work with people who are able to ask the right questions of and around the data. In addition, you don’t need to spend huge amounts of money on IT and technology in order to use big data; it’s possible to “build… a capability from the ground up” (see the section on “Getting Started”).

What are the management challenges?

The final section of the article outlines five management challenges connected to making the best use of big data:

• Leadership: the real power of big data will be in combining it with human “insight”, “vision”, market knowledge, and the ability to take others on this journey too.
• Talent Management: data scientists are the people who will make sense of the big data; an often rare type of person in possession of both hard and soft skills. They are able to manipulate big data sets, while also making sense of them in business, management, and human terms.
• Technology: is required to deal with the data (with Hadoop being the most typically used at present), with these new technologies consequently requiring IT professionals to master new skills.
• Decision Making: the insight generated from the data will need to be in the same place as the people making the decisions, and must be capable of being understood by these decision makers. This will require organisations to be flexible and effective working across functional boundaries.
• Company Culture: moving away from intuition to be genuinely more data-driven will need to be embedded in organisational culture – along with, presumably, being willing to change and adapt in the wake of the new insights in order to really be able to capitalise on them.

I hope you enjoy reading the full article, and this post may encourage you to do so.

English: Binary Data Česky: Binární data (Photo credit: Wikipedia)

Imagine if you could observe all your customers all the time: where they are, what they are doing and who they spend time with. If you could, you would almost certainly be making some significant changes to your business and marketing strategy. This total visibility may sound far-fetched but the Big Data pundits will tell you that this is now a reality.

But, I believe the reality is that we are at the very beginning of a transition. We are only just beginning to move away from traditional ways of segmenting and targeting customers (based on generalisations), towards approaches that achieve a fine grain understanding of individual interactions, data sharing and connections.

Good segmentation is fundamental to brands making the most of their market opportunities – from identifying, attracting and retaining the most profitable customers to identifying the most vulnerable customers to the competition.

However, most brands still use segmentation models that are based on averages and assumptions and that focus their products and services at generalised groups of customers. These segmentation models, whilst gradually improving, often fail and for many reasons: lack of alignment across the business, insufficient operational backup and not refining the segmentation model over time.

But probably the main reason for failure is the oversimplification of the customer decision-making process. Because to understand that requires defining the customer’s rational and emotional criteria at each touchpoint in the customer experience. And that is difficult.

Customers have access to near perfect information, from their mobile phones and from peers in their social networks, influencing their purchasing decisions. In this intensely competitive environment, brands are finding it increasingly difficult to differentiate themselves. This makes customer experience an essential area to focus on, along with new approaches to segmentation.

Due to the impact of digital devices on customer buying patterns, segmentation models will inevitably become more data-driven. This brings the opportunity (in theory at least!) to target customers on a real-time basis at an individual level. This could help a brand shift the basis on which they compete to one of value rather than price.

The promise of Big Data is that it provides insight about people’s actual behaviour not just their beliefs or stated intent. Rather than dealing with generalised customer segments, brands are able to find patterns in their data, made up of millions of small transactions between people that may begin to explain specific peaks in sales or high demand of a particular product, for example.

Overlay this with social data and the analysis becomes even more interesting.

Analysing the things an individual posts on a social network like Facebook in isolation isn’t reliable enough because more often than not it just shows what they want others to hear about them. The more intriguing opportunities lie in the integration of unstructured social data with other data sets, such as location data from mobile phones or credit cards. It is this continuous ‘data exhaust’ that tells our real story about who we really are, where we spend time and what we actually buy each day. Where social data can really play a part is the important study of connections between people – something that traditional segmentation methods do not cover adequately.

Developing an understanding of someone’s actual behaviour and then making inferences about their other behaviours (who is most likely to be a safe insurance risk or a frequent ‘switcher’ of your broadband service) becomes possible because of social context.

Comparing you to others in your social circle helps to determine the behaviours that the group thinks are normal and that they have learnt from one another.

This of course means that because Big Data is increasingly about people, the privacy and data ownership issues are only going to become more central to a brand building trust with its customers.

The exciting and more futuristic implications of understanding the way people connect and influence one another are that we can start to see how people form entire markets – financial, government and companies. Once we understand that, we can make predictions and design services that are infinitely better than those we have now – from better transportation systems to dramatically improved public health services.