Decision Sourcing: Which jacket do you prefer?

We would really appreciate your vote from four different jacket design treatments for the upcoming book ‘Decision Sourcing‘. Click on the image for a closer look at each design.

If you like a treatment generally but want to suggest a change (perhaps to the typeface) then please feel free to add a comment.

Thank you

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Gamification and Gamified Business Intelligence

Gamified

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”

Level 1

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.

Achievements

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.

Level Up

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.

What Has CRM Ever Done for Us?

Actually the Romans come out rather well when Reg asks the questions of a bunch of masked activists in Matthias’s house in ‘The Life of Brian’. The aqueduct was just the beginning. Would CRM fare so well in a contemporary and probably unfunny update of the classic scene?

What has it done for us? Don’t misunderstand me. I use salesforce, my chosen flavour of CRM, every day. I wouldn’t be without it. Everything I do is captured in those seemingly simple customer, contact and opportunity tabs. However, what has it ever done for me … as a customer?

I have just finished Doc Searl’s latest book, the Intention Economy. It is a jarring book which turns CRM on it’s head, instead describing a world where software helps customers manage their suppliers rather than the other way round. It manages to be visionary by illustrating with situations which are utterly everyday. As customers, like frogs on slow-boil, we have come to accept the unacceptable. We tolerate what should be intolerable.

For example, Doc makes the point that when he travels by air (not unlike me) he has no special dietary requirements, places few demands on cabin crew, is likely to offer up his seat to accommodate a family or couple travelling together and is willing to pay (a little rather than take out a mortgage) extra to reduce the stress of travelling because the novelty has long since worn off. What his frequent flyer programme knows about him (and mine about me) is the total miles we have travelled and our address. Hmmm.

Yesterday,  I received a ‘personal’ note from a high street chain that I used to visit often but haven’t been able to recently.  Let’s say it’s a shop for the body. I shop here because I admired their deeply principled founder and her stand on ethical, environmental and social issues. I also like smelling like a satsuma. Mostly though, I shop there because there is convenient outlet on Waterloo concourse my gateway into and out of London. Rather, there was an outlet. It closed down during the station refurbishments and has yet to reappear. The CRM system that delivered the ‘personal’ note to me notes that they hadn’t seen me in a long time and offered me a generous discount to return. So far, so good. However, the featured products were wild rose hand cream, lip butter and a free makeover. I am a modern man and I freely admit that I prefer the smell of citrus fruit to masculine musk but it didn’t seem like a particularly compelling selection even for me.

And, this is a business I respect. At least their CRM had  spotted that it had been an unusually long time between the last transaction.

Another on-line retailer that I have been ‘loyal’ to for years has been through a recent CRM upgrade. I now only receive the section of their clothing catalogue for men. They finally understand my gender and no longer assume that my wife and I automatically like the same brand because we pick out curtains together. They worked out that I am a male and that I have different shopping habits to my wife. Big whoop.

This is the reality of CRM and Big Data today. Companies at the top of their game, with the most sophisticated CRM have worked out households, genders and not much more. And B2B is generally not even close. Many direct mail (interruptions) that I receive in my office inbox don’t even get my name correct and few, if any, are relevant to my job title or role.

It is true that sophisticated relevance marketing exists. These are the types of systems that can tell when you have started and finished the Atkins diet but they require a level of exclusivity associated with a church service and a gold band rather than the somewhat lighter associations most of us have with our grocers, coffee shops or satsuma scented shower gel supplier.

The Romans did actually give us irrigation, underfloor heating and straight roads but what has CRM ever done for us? We need more than a wallet full of loyalty cards, an iphone full of apps, licensing terms that we accept without reading and  discount vouchers with a redemption date just expired at the time we want to use them. It has a long way to go before it makes good use of all of that data, all those cookies and screens of social analytics. Mostly CRM needs to respect that unless it is going to make good and positive use of all of that data, that customers might tire of waiting, take it all back and start building VRM. The clock is ticking.

Just Stop with the ‘Big Data is Just’

OK, I get it. You’re sceptical. You’ve seen stuff come and you’ve seen it go. To you big data is just BI, just data, just analytics for the hip kids, just a distraction or just hype and fad.

Except it isn’t. Big data is only ‘just’ analytics in the same way that cloud is ‘just’ asp or bureau. That is to say it isn’t at all.

It ‘aint hadoop either

Others define it in terms of the technology. I get this too. New tech is making it all possible and existing databases have been a barrier. New approaches like hadoop were borrowed from those that pioneered extracting value from enormous volumes of data. To the traditional data vendors, a terabyte was a big deal. They failed to notice that this was becoming standard in a home pc and that insurgent innovators were capturing, processing and mining mountains of data. They  didn’t keep up, so others had their lunch money and now they are playing catch-up.

But it would be wrong to define big data in terms of the innovation that allows it to happen. A little like defining fine dining as an activity conducted with knives, forks and a high quality napkin. It would be the most common mistake of the Big Data muggle.

The end of transaction oriented business

So if it’s not just ‘just’ and it’s not the technology … what is it?

It’s nothing less than a profound change in our approach to data. Historically, businesses managed themselves as a series of transactions. Occasional snapshots if you will. Only the essential financial and operational interactions between them and their customers. A quotation, an order, a despatch note and most importantly an invoice. Early on-line commerce  began to change this. Every gesture a customer made on their shopping journey could be captured. An abandoned basket in a supermarket tells the store manager nothing. Online, the same shopping cart could tell us that the delivery times are too long, the accessories were out of stock or that the secure shopping statement was in the wrong place. For the first time, so much data was being generated that ‘traditional’ analytics started to creak and groan and most of this type of analysis took place outside of corporate BI. It was ‘special’ click stream, needed specialised tools and the BI specialist and vendor shook their heads at it’s lack of structure. Where were the columns, rows and indexes.

This was just the beginning. Social platforms don’t just allow the analysis of shopping behaviours but all behaviours. If a customer comments, complains, compliments or converses in general about you or your brand, it is possible to know. It’s no longer heresay or anecdote, it’s available from the blogsphere or the Twitter firehose. It’s data.

Another beginning

Actually, that was just the beginning of the beginning. New classes of devices that can generate more data than the most active surfer or shopper are boosting the on-line population. Forget smart meters and the internet fridge, at least for now. Think more about ultra-low cost devices that remind you to water the yukka, feed the guppy or take your medication. If you forget any of these, particularly the medication, they will probably tell others too. Connected asthma inhalers can provide insight into air quality and cars that connect with your insurers who adjust your insurance premiums because your acceleration and braking patterns suggest that you are driving like you are on a track day rather than on the hanger lane gyratory. Oh and my new pebble watch (when it arrives) will add to the billions of facts, snippets and streams being added to that one big database in the sky. The cloud.

Ambient Data and why Big Data is Big

Big data represents a profound change. In our book Decision Sourcing, Gower, 2013, we refer to it as ‘ambient’ rather than big data. Ambient because we have always been surrounded by our thoughts, gestures, actions and conversations but they have never been data before. They were lost (as Rutger said) ‘like tears in rain’.

Today, we are approaching an an age where it is possible and practical to know everything that there is to know. Everything that is (to use an arcane legal expression) ‘uttered and muttered’. That’s what makes it big. Really big. Teradata think a Tera is big but it’s just a walk to the shops compared to Big Data.

Oh no  it is not ‘just’ anything. It is the beginning of the most significant shift in our industry since it began. The complexities are many, the data as varied as it is voluminous but the prize is knowledge and insight much of it predictive. Indeed, everything we have done to this point has been in preparation for the age of Big Data.

If Big Data is just anything right now … it’s just the beginning.

Information Curation: 1 dot 1

Connecting the Dots

kusama3_bodyOn an uncharacteristically warm Summer evening in 2012 I made my way into the Tate Modern as everyone else was making their way out. It was part of my work to understand the curatorial process and its relevance to information management through one of the Tate’s infrequent but excellent curator talks. This one, from Frances Morris, concerned the recent and enormously popular Kusama exhibition.

 

The notion that curation is an emerging skill in dealing with information is not a new one. It is covered by Jeff Jarvis in his blog post ‘Death of the Curator. Long Live the Curator’ where Jarvis applies them to the field of journalism. It is also the subject of Steven Rosenbaum’s excellent book ‘Curation Nation’ which examines the meme more broadly.

 

Abundance

Japanese artist Yayoi Kusama is prolific. Her work span the many decades of her life, first in rural Japan then New York in the 60’s and in contemporary Tokyo today. It is enormously varied. Her signature style of repeating dot patterns, whilst the most famous, represents only a small part of a vast and sprawling body of work. It is the perfect artistic allegory for information overload. Kusama has too much art for any one exhibition in the same way that information professionals in the age of Big Data have too much information for any one decision.

 

Morris, I figured, must have wrestled with Kusama’s prodigious nature. The problem is not one of assembling a coherent and factual account. Instead, it is one of separating out that which is relevant and that which is extraneous. It is a process of  building a series of working hypotheses and building a story that is a reality, that is a ‘truth’.

 

Analysis and Curation

Like many managers, Morris had a vague sense of the story she wanted to tell but the final story could only be told through material facts, works or ‘data’.  At first, she considered, selected, dissected and parsed as much as possible. Over time Morris selected works through more detailed  research. She travelled extensively spending time with Kusama herself in a psychiatric institution which has (voluntarily) been Kusama’s home since 1977. She also visited locations important to Kusama including her family home and museums in Matsumoto, Chiba and Wellington, New Zealand where others had curated and exhibited her work. This parallels the analytical process. One of  starting with very few, if any assumptions, and embarking a journey of discovery. Over time, through an examination of historical and contemporary data points, the story begins to unfold.

 

In the Next Post (1 dot 2)

Already we can see that curating is a process of research and selection. It has strong parallel’s with early stages of information analysis. In the next post we will look at filtering, relevance and how the curatorial process helps us understand which comes first … data or information.

Social Media Listening, Lets Call the Whole Thing Off?

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.

Big Data Analytics: Size is not important

There was a time when databases came in desktop, departmental and  enterprise sizes.  There was nothing larger than ‘enterprise’ and very few enterprises needed databases that scaled to what was the largest imaginable unit of data, the terabyte. They even named a database after it.

We now live in the world of the networked enterprise. Last year, according to the IDC, the digital universe totalled 1.2 zettabytes of data. And we are only at the beginning of the explosion which is set to grow by as much as 40 times by 2020. Massive data sets are being generated by web logs, social networks, connected devices and RFID tags. This is even before we connect our fridges (and we will) to the internet. Data volumes are growing at such a click that we needed a new term, Big Data (I know) to describe it.

What is meant by ‘big’ is highly subjective but the term is loosely used to  describe volumes of data that can not be dealt with by a conventional RDBMS running on conventional hardware. That is to say, alternative approaches to software, hardware or data architectures (Hadoop, map reduce, columnar, distributed data processing etc) are required.

Big Data is not just more of the same though.  Big Data is fundamentally different. It’s new and new data can present new opportunities. According to  the Mckinsey Global Institute the use of big data is a key way for leading companies to outperform their peers. Leading retailers, like Tesco, are already using big data to take even more market share.

This is because Big Data represents a fundamental shift from capturing transactions for analysis to capturing interactions. The source of todays analytic applications are customer purchases, product returns and  supplier purchase orders whilst Big Data captures every customer click and conversation. It can capture each and every interaction. This represents an extraordinary opportunity to capture, analyse and understand what customers really think about products and services or how they are responding to a marketing campaign as the campaign is running.

Deriving analytics from big data, from content, unstructured data and natural language conversations requires a  new approach. In spite of the name though, it’s less about the size and more about the structure (or absence of structure) and level at which organisations can now understand their businesses and their customers.