This continues to crop up in conversation with my colleagues and customers. What are the essential tools for leading in a networked and social era. Here are my top 5.
Evernote. Strictly speaking, not really a social tool. However, those that are living in the cloud (living the vida nuba) need a note taker that is versatile and available on any device that they have to hand. Evernote does this and much more. If you are not using it you are missing out. Check out Evernote Hello too.
Buffer. If you have something you want to say to a community that spans the globe and that have busy twitter streams themselves then you might need to say it late at night or early in the morning. You might even need to say it twice. This needs a tool that manages your posting to a timetable so that your sharing can be sometimes scheduled and sometimes impromptu.
Klout. Err, I’m sorry. I care about my social influence. Not obsessively you understand. I just want to understand if what I am sharing is working. One way of knowing if you are contributing something positive to the global conversation is to check what others are doing with your Tweets, Updates and Posts. That’s what Klout does … and there are Klout Perks too.
Unfollow. If you follow me and we share interests, I follow you back. Unless you spam me, we will spend a long time sharing. I expect the same in return. Nothing anyone has to say is that important that they can’t listen from time to time. Unfollow will highlight all those one way conversationalists so that I can unfollow them. You are not Brian Solis, after all. Unless you are.
Linked In and Twitter Apps. Yes, I know that this is two really but what I mean is native apps. Whilst I use aggregators the native apps keep getting better so I use them frequently for new insights into how they can be used to nourish my network.
That’s my list. My essentials. There are more of course including pocket (you really need this!), bitly, feedly and flipboard but I really could not function without these any more. If you are part of the connected generation you will have a similar list too and no doubt we some in common.
According to Mashable, Facebook Graph Search could be it’s greatest innovation. I tend to agree. FB have eight years of Big Data (including almost over two billion new Likes each day) to help us identify products, services and brands that we might need through the experiences of those in our social network.
Actually, a graph consists of only two things; Nodes (people) and edges (their relationships) Analysis of these though can reveal much. The simplest is a measurement of neighbours, the number of edges and their direction. A node with a large number of inward edges (or indegree) can be thought of as popular. One with a large number of outward edges gregarious. If it were possible, Lady Gaga could make a whole boutique full of dresses out of her indegree. Simple analysis of these elements are behind ‘People You May Know’ features in LinkedIn, Chatter, Connections, Jive and Facebook to mention just a few.
Of course, the FB Graph includes other types of nodes (businesses, brands, products) and many other types of edge including the ubiquitous Like. FB also have demographics and psychographics because we surrender more information about ourselves to FB than we would feel comfortable doing in any other survey online or offline. We’re all concerned about privacy but generally end up somewhere around ‘what are you gonna do?’.
These simple elements add up to something very powerful. It’s possible not just to find French restaurants in Frimley but those that are preferred by frequent travellers to the Côte. Not just DIY stores nearby but those popular with power tool enthusiasts. Robert Putnam could have found countless examples for his book on the decline of social capital Bowling Alone. And it is just the beginning. Let’s not forget that those edges include ‘listened’, ‘read’, ‘watched’,’hiked’ and ‘cooked’ to name but a few of the verbs now residing in your facebook apps list and your personal social graph.
Big Data Breakthrough
This is a Big Data breakthrough for Facebook and puts some distance between them and their competitor, Google. I am not sure that plus’ing is enough of an ‘edge’ at this stage. And for those that can’t see that FB and Google are competitors then remember that there is no revenue in Search. No one actually pays Google to organise the worlds information. Nor do we part with our cash for maintaing personal networks on Facebook. There is, however, a group of people willing to pay for connecting people to products they might enjoy. Advertisers. In other words there is revenue in creating new edges between nodes. That’s the power of the graph.
I have become somewhat preoccupied with gamification of late. After the usual reading and research concluded with some structured study with the Wharton School through the excellent Coursera program, it became apparent that it was less of a diversion than I first thought. Indeed, there is considerable overlap between the aims of gamification and the aims of Business Intelligence.
To understand why, let’s start with the definition of gamification from Professor Kevin Webach, the course lecturer and also the author of ‘For the Win‘ which is;
“The Use of game elements and game design techniques in non-game contexts”.
It’s an excellent, insightful and crisp definition. However it really only explains the ‘what’ but no the ‘why’. For this, I would refer you to Brian Blau and Brian Burke of Gartner who extend the definition as;
“The use of game mechanics to drive engagement in non-game business scenarios and to change behaviors in a target audience to achieve business outcomes”
Both definitions are about using game elements in a non-game context but Webach is being more inclusive whilst Gartner very specific. For Gartner is’s about business whilst Wharton include external gamification and gamification for behavioural and social change. The former is gamification as a marketing device such as Foursquare. The latter is a rich and interesting area that would include Runkeeper and Zamzee encouraging us to be become a little fittter and OPower which, by comparing our energy usage to our peer group, helps us be more aware of our consumption.
The third Wharton category, internal gamification has the greatest overlap with Analytics, Business Intelligence and Performance Management. A definition of which can be derived from some minor modifications to the Gartner definition of gamification;
“The use of analytics, business planning and key performance indicators to drive engagement and to change behaviors in a target audience to achieve business outcomes”
Analytic applications are systems, sets of mechanics, to align, engage and improve the performance of the business. They, like a gamified system, are an abstraction. They are a derivation of business activities not the activities themselves. The numbers, charts and indicators become a new reality distinct from the business activity from which they are derived. They are, in a sense, gamified systems but with only a small subset of the rich set of (game) mechanics that might be made available. In fact I have argued for some time that this subset of mechanics is as woefully inadequate as the user experience/user interace design effort in most corporate analytic applications. We still think that a dashboard is a pretty cool interface.
Business intelligence can, more often than it should, be driven by whatever data is available. Equally common is to deliver a system that is a marginal improvement in the information system it replaced but in a new tool or technology. The design will pay scant regard to how the information will really be used and are open to being ignored or even ‘gamed’. Measure a sales team on orders and there may be an increase in cancelled orders. Measure baggage handlers on the time it takes the first bag to arrive on a carousel and the second and subsequent bags might wait for the first bag on the next flight.
Internal gamification is designed around a deep understanding of the players (staff, workforce) and their motivations. It draws inspiration from an extensive palette of behavioural (game) mechanics.
Business Intelligence then, could reasonably be defined as an early attempt to gamify the workplace. Sophisticated BI intended to engage the workforce and align organisational behaviours through carefully designed elements of which analytics and key performance indicators were just a small subset, would be a game that many businesses would find worth playing.
In part one we examined how the curatorial process is one that is relevant to the way in which businesses make informed decisions. We examined how Frances Morris, curator of the Kusama exhibition at the Tate Modern in 2012, dealt with abundance the most pressing issue for those of us dealing with exponentially increasing data volumes today. We also saw that curation has parallels with analysis. One that starts with very few assumptions, perhaps an inkling that there is a story to tell, but then becomes more focused as evidence is sifted, examined and understood.
In this, second part, we look at filtering, relevance and how the curatorial process helps us understand which comes first … data or information.
Relevance not Completeness
As I listened Morris at the Tate, it was clear that the story she wanted to tell was as much a product of the things she left out as it was the things she included. Morris described how she visited a site on the Japanese island, Naoshima, to see an example of Kusama’s famous pumpkins. Perched at the of end of a pier, jutting into the Inland Sea, she decided that to take it out of context would be to lose something of the truth. This lead to, perhaps, her most controversial decision amongst Kusama’s many fans, to not include one of Kusama’s recurring themes in the summer exhibition. The pumpkins, similarly to the most frequently used data, were popular. They were well known and well understood. However, they didn’t bring anything new. At the end of the pier, they were relevant and contextual. In an exhibition intended to deliver insight into ‘Kusama’s era’s’, the key points at which the artist had reinvented herself they added nothing new.
One of the most telling characteristic of Morris’s curatorial process was that the story she wanted to tell was not limited by the art. Kusama was a leader in the 60’s New York avante garde movement. She was outlandish and outspoken, sometimes shocking. Not all of this is obvious from her art but it was an important thread in Morris’s story. To remedy this she chose to exhibit documents and papers that gave Kusama a voice. Clippings, letters and personal artefacts enriched the story. The result was a much more complete picture of an artist who’s influence on culture and society had as much to do with her activism, performance art and outrageous ‘happenings’ as her art.
Sometimes, as analysts, we limit our story by what is in the database or data warehouse. Smart decisions should be informed but that doesn’t mean to the exclusion of other forms of knowledge. That which is anecdotal and tacit alongside the ‘facts’ might provide a more complete and accurate picture. Information exists outside of columns and rows.
Joining the Dots
Does the curatorial process deliver insight? Does it ultimately leave it’s visitors with the “facts” insofar as we can as they relate to life and art. The test would be Kusama’s reaction to Morris’s exhibition when she visited for a private viewing before it was opened to the public. It seems the answer is an overwhelming yes. At one point, as Morris walked Kusama around the exhibition, she wept. The collection which spanned nine decades of an extraordinary life had struck a deep and personal chord. This visceral reaction was an acknowledgement that it was an essential truth from perhaps the only one who knew, in this case, what the truth really was.
Knowledge does not leap off a computer screen or printed page any more than the life of an artist leaps off a gallery wall. It is a synthesis of data and information. To deliver a report, chart or scorecard is not to deliver knowledge. The job is only part done. The information needs to be socialised, discussed, debated and supplemented with what we know of our customers and products. Neither is the process just ‘analysis’. It is one of selecting that which is relevant, excluding that which is not and enriching with the experiences and opinions of those in the business who’s expertise is not captured in rows and columns. In a world where we are overwhelmed with information, knowledge and understanding requires curation.
The nine decades of Yayoi Kusama at the Tate.
Frances Morris discusses and explores Yayoi Kusama’s life and work. Taking the audience through her curatorial processes, Morris will map out the exhibition from its origins to completion. The curator will also reflect on her personal journey with Kusama, having had the opportunity to work closely with her over the last three years.
Paul Adams, Facebook Product Manager and former Social researcher at Google led the charge into Social Circles. Quite frankly, according to Adams, ‘Friends’ really didn’t cover it. We have family relationships, relationships with our colleagues and closer ‘besty’ friends. We also have relationships that are built during life stages (university) or around hobbies (football teams, diving) and those that are built because of locality (neighbours)
These are all social circles appropriate to (what I call) lifestyle social circles. But what about our professional social circles? Professional social circles, professional communities are built in Social platforms like LinkedIn or inside our own organisation. These circles, or communities, also include life (or career) stage communities such as inductees and locality (same office) But what other types of professional social circles are there? Some might be functional, customer specific, product or service specific?
I would welcome your suggestions and comments here:
Frank Buytendijk delivered a great keynote at 8am in Las Vegas at the TDWI conference in February 2012. He avoided the technicalities of data architectures, the rigours of data modelling and the disciplines of agile methods.
Instead, over breakfast, he dipped into the world of philosophy and asked us to consider the centuries old problems of what is true? what is real? and what is good?
Referring to Plato, Thales and Machiavelli Buytendijk lead us through some fundamentals about decision making.
What is True?
Firstly decisions are not just about the data. Do we decide to pay for parking because we calculate the cost of a ticket against the cost of a fine but factored by the risk of getting a fine? Or do we do it because we think it is the ‘right’ thing to do, the ‘civic’ thing to do?
What is Real?
So often, even with all the dashboards, scorecards, reports and charts, senior executives don’t seem to know what’s going on. Like in Plato’s Cave, the shadows on the wall are not reality, they are representations of reality. How much could really be told by listening to our customers directly rather than waiting for analysis much later?
What is Good?
Predictive analytics can provide great information that allow micro-segmentation. For example it could help an insurance company to identify those most likely to claim on their insurance policy for back and neck strain based on their on-line behaviours. Increasing their premiums might protect the business from additional costs but the insurance business model is about distributing the risk not identifying it perfectly. Taken to it’s conclusion then there is no need for insurance, we all pay for the cost of our health care as and when it happens. However, if the insurance company used this information to promote lifestyle changes for this group then ethics and business models are aligned.
What’s it all about?
Buytendijk’s quirky, thought provoking start to the TDWI conference tells us that in IT, we are wrestling with problems that preoccupied philosophers centuries ago. It also tells us though that in IT we can think too much and reflect too little.
The couple in Ira Gershwin’s song Lets call the whole thing off lamented the way they pronounced the same words differently because it exposed class differences which might eventually be their undoing. Human communication is a funny thing. If Fred Astaire and Ginger Rogers had met on Facebook then regardless of how they pronounced neither, either and tomato, they would have assumed that they, like the spelling, were a perfect match.
Understanding nuance in human communication is a preoccupation for those of us building social media analytic applications and specifically as it applies to the Social Listening process. Social listening is the data collection process in a social media analytics application, the point at which the vast sea of blog, editorial and social media content is collected and converted into usable analysis. The purpose of Social Listening is to collect and filter ‘mentions’, instances of the company, brand, product or marketing campaign being referenced in an item of online content. Most platforms are good at collecting mentions but many fail in their level of accuracy, not because of scale and volume but because they don’t understand the human capacity for saying the same thing in so many different ways.
Fred and Ginger were both speaking (American) English and yet still had problems because language is only one of the many considerations when we try to understand the written word. Slang, regional idioms and differences in style relating to social groupings, profession, generation and gender are just a few others.
Anyone with teenage children can tell you about generational language differences. At one time my Son and his friends frequently used the expression ‘you just got pwned’ or ‘he pwned me’ usually but not exclusively when gaming. It describes the process of being decisively and unambiguously beaten by a competitor. ‘Pwned’ is a corruption of ‘owned’ attributed to a mis-spelling by a world of warcraft map designer and for some reason it fell into common usage. Unlike much of what we deal with in information systems, there is no rule, no derivation, it is simply something which is known. Without this knowledge what would a social media monitoring platform make of the tweet ‘coke pwns pepsi’ (or the other way around, of course)?
Other differences are equally obtuse. Take emoticons. Baby boomers rarely use them, gen-X ers commonly use them and gen Y-ers use them but differently. A gen-X er is more likely to use 🙂 and a gen -Y er 🙂 Very little difference to the human eye but in traditional text filters they simply don’t match.
Many are a little surprised when I point out that the author’s gender makes a difference to the language used. Of course, women might be more likely to discuss hormone replacement therapies and men more likely to discuss male pattern baldness if they are blogging about their mid-life crisis but given a gender-neutral topic, men and women still use different language. One website, gender genie, can identify the gender of the author of a piece of text with a surprisingly high degree of accuracy.
What does all of this mean? It means that Social Media Analytics platforms have to understand the rich, inconsistent and unfathomable ways in which we all converse. To get more specific and technical, social listening must employ linguistic variant sets to accurately disambiguate language variations. Simply put, they must be able to handle a set of alternative way of saying the same thing. Social listening must be inclusive of all diversity regardless of age, gender, ethnicity, social status, profession and yes, sexuality before they can capture data suitable for the purpose of analytics. Otherwise, you might as well just call the whole thing off.
Also reproduced for IBM Vision for the IT expert community.