What’s just as important to us as aptitude is attitude, and both are more important than experience. We’re accepting applications from all levels – post grads, soon-to-be-grads, and seasoned data-design die-hards.

Please get in touch if you’re interested in talking about any of the following roles, we’d love to hear from you:

Data Scientist

Do you live in that magical intersection between data, maths, and coding? Do you have an irrepressible curiosity to dig deeper into the data, seeking out the patterns and trends, remorselessly chasing down the hidden insight? In short, can you find the story in the data?

The key focus of this role is to support the Chief Scientist in evolving QuantumBlack analytical methods.

• Enhance existing methods, visualisations or software by analysing and identifying areas for improvement.
• Research, develop, validate and document enhancements to the QuantumBlack analytical suite, in particular using machine learning to deliver early warning capabilities.
• Assist in the publication of peer-reviewed papers in leading journals and conferences.
• Support the creation, protection and deployment of new IP for the firm.
• Support the packaging of the QuantumBlack analytical suite to streamline production and deployment.
• Extend QuantumBlack to new data sets and experiment with these new data sets to undercover insights and analytical methods.
• Adhere to the firm’s production processes and adopt best practice coding, testing and development methodologies and styles.

The key skills are:

• Experience of statistic packages such as Matlab or R
• Knowledge of statistical machine learning methods
• Knowledge of econometrics and empirical modeling methods
• Knowledge of Bayesian methods
• Experience in managing and creating insight from large data sets

Data Visualisation Creative

Part-designer/Part-developer is a very exciting place to be at the moment. We have the data; our scientists have built the algorithms to drive the analytics and insight; now we need to visualise the output in a compelling, engaging, and innovative way. Can you show the story in the data?

The key focus of this role is to support the CTO in the creation of interactive data visualisations to present QuantumBlack analytics, methods and thinking across a diverse, challenging, and stimulating portfolio of projects.

• Research, design, write and test showcase data visualizations publishable to the QuantumBlack website gallery.
• Experiment with new data sets to uncover insights.
• Experiment with new visualization techniques and technologies to make the narrative compelling, accessible and engaging.
• Estimate time and effort to design and implement data visualisations and meeting agreed delivery timescales.
• Assist in the development of operational documentation with technical authors.
• Enhance existing methods, visualisations or software by analysing and identifying areas for improvement.
• Adhere to the company’s production processes and adopt best practice coding, testing and development methodologies and styles.

The key skills are:

• Good quantitative skills
• Experience in Processing, Protovis, D3 or equivalent
• Experience in creating visualizations from large data sets

Curator

It’s no point being able to ask the right questions (which we think is a skill in itself!), and find the right answers (ditto), if you can’t communicate the message. Can you tell the story in the data?

The key focus is to support the developing of brand awareness through the creation of blog articles, case studies and a curated Twitter feed.

• Research and write articles on Data Science, Big Data, Data Visualisation, Strategy and decision analytics, publishable to the QuantumBlack blog to build brand awareness and market creditability; ultimately leading to new client engagements.
• Curate the QuantumBlack Twitter account to build brand awareness and market creditability.
• Curate the QuantumBlack website, manage its performance and report on its effectiveness
• Ad hoc support of marketing activities such as events, case studies and client presentations.

The key skills are:

• Experience of writing and producing copy.
• Experience of publishing to WordPress.
• Experience of Twitter tools such as TweetDeck.

We’re very much looking forward to this year’s eyeo festival in Minneapolis on June 5-8, 2012. It brings together a fantastic array of leading creatives, designers, and data visualization professionals. As the organisers say:

It’s an exciting time to be interested in art, interaction, and information. The way we experience all three is changing. The way all three interact and overlap is evolving. Access to data and tools continues to enter new realms. What data is—is changing; It’s a social media feed, it’s a physical sensor, it’s a house plant, a novel, it’s open access to oceans of digitized archives and more and more APIs. What can we do with all this data? What can’t we do? Artists, designers and coders build and bend technology and give us a glimpse into what’s possible, into what’s next. Ones and zeros float all around us just waiting to deliver the next new interaction. The Eyeo Festival brings together the most intriguing and exciting people in these arenas today.

At QuantumBlack, all our projects ultimately result in some sort of visual output, be it rich graphical applications, interactive visualisations, or highly visual presentations and reports. At the other end, more often than not our projects take complex and big (or Big with a capital B!) data sources as their input, and the two meet in the middle with our custom and specialised applied analytics.

In a nutshell, Data + Analytics = Visualizations.

So the necessity to design and create truly innovative, engaging, and compelling visualisations for our analytic output is hard-wired into the DNA of our projects, and hard-wired into the DNA of our team. Our analytics are usually intricately complex, so our visualizations need to be elegantly simple.

At eyeo fesitival we find a community of like-minded people from a broad range of cross-disciplines, who share our passion for design and visualization and for whom, like us, it is part of their DNA. As they say, converge to inspire…

Next week is International Big Data Week and there’s a great line-up of events in London. We will be at all the following in what promises to be a very busy and interesting week:

• Data Science as a Game Changer
• Big Data Community Meetup
• Hadoop Meetup: Big Data Week Afternoon Special
• Data Visualization London: Share some D3 and Gephi love

Things we’re especially looking forward to during the week:

• Meeting Doug Cutting, co-founder of the Apache Hadoop project and creator of Nutch and Lucene
• Hearing how DataSift uses Hadoop to process and store the twitter firehose and other big data streams.
• Seeing our favourite platforms being used in increasingly diverse and exciting ways – Hadoop, R, D3, Processing, creative HTML5/JavaScript, Microsoft Azure, etc.
• Meeting many keen and talented data science and visualization professionals and enthusiasts working in and around London (yes we’re hiring!)

Look out for our QuantumBlack badges and come and say Hello!

Tradition once had it that I’d go over the minutiae of the analysis and its implications for race strategy in Melbourne (which is traditionally the first race of the calendar) with a good friend and former colleague, Pete Wezenbeek, who’s now doing amazing things in Melbourne. In some sense, Pete, I suppose this short post is a way to start our conversation, even if it isn’t anywhere near Albert Park.

With so little testing in this era of F1—there were only single-car 3 pre-season tests this year—inferring pace is arguably more difficult than it ever was. What’s more, teams are shaking down their cars, learning how to set them up, the limits of the tyres, and are playing with different aero configurations (and even various suspensions in a few cases). All the while, teams’ marketing machines are all beating terribly optimistically. Notwithstanding any of this, the data from the last pre-season test in particular (March 1-4 in Barcelona) does afford us our best chance of cutting through the din and saying something substantive about teams’ relative competitiveness.

Paramount to the problem of working out relative pace is inferring each car’s on-board fuel. While teams know their own car’s fuel load, those of other cars’ are generally unknown. Still, every lap that a competitor does reveals increasingly more information about their fuel load and we can use that (in conjunction with any fuel loads that we know exactly) to effectively solve a constraint satisfaction problem, a well-known class of problems, whose solution gives us a distribution over fuel loads for all competitors. Working in our favour is that it’s also very easy (in this era of F1 with homologated engines) to disambiguate between on-board fuel and tyre degradation effects.

We crunched the numbers and the landscape revealed is surprising for being markedly different from last year. The plot is our attempt to capture the gist of those findings in a single graph. It shows the fuel-corrected gap (in seconds/lap around Barcelona) to the fastest car in qualifying trim, which also corrects for tyre degradation, on the x-axis versus race trim, which accounts for degradation normalized to a 20-lap stint, on the y-axis.

The headline: McLaren look to be new pacesetters. Red Bull seem to have lost the advantage over their rivals and are now second in qualifying trim, but largely on an even keel with Mercedes on race pace. The other surprise of course is Ferrari, who appear to have regressed a fair amount from where they were last year.

So how will this analysis hold up to scrutiny? We’ll find out very shortly. One prediction we can make with a great deal of confidence is that we’re in for a great season.

However probable (or not) that particular event, it describes a challenge that is on the minds of increasing numbers of executives. What would you answer?

To us it’s clear that many organisations we meet are at different points on the journey to achieving competitive advantage from big data. It’s also clear that, for many, the case for change has been made, either in a visionary way or based on a fear of being left behind. What is less clear is a vision of where the destination actually is, the implications of committing to make the journey, or a map of the best route to get there.

At QuantumBlack we use a framework that describes the strategy, organisation, capabilities and culture needed to make analytics a powerful organisational asset. The framework helps us to create the alignment needed to move quickly, and to navigate, with clients, through four stages of maturity which we’ve called Novice, Emerging, Competent and Leading. Here we profile the Novice and Leading stages to illustrate the full range, whilst recognising that most will already have made some progress along the route.

Portrait of a Novice
• Analytics don’t play a significant role in developing business strategy, nor are there critical metrics defined to assess progress.
• IT and Business Units are not aligned on the need or role for analytics, either because people in the business do not believe IT understand the issues and opportunities, or vice versa. Or both.
• Organisationally, novices often have lots of data, but few resources, so they don’t use much of the data in ways that effect either strategy or operations. One Fortune 50 organisation that we know illustrated this by paying substantial sums annually for customer survey data, showing performance against their industry peers, but did not use it to differentiate themselves in the eyes of customers despite there being clear opportunities to do so.
• Acquisition, development and retention of analytic talent is not yet a priority. Pockets of it exist, usually financially based.
• Data management challenges are significant: It is on different platforms, uses different taxonomies, and the quality is variable. Reconciliation of data from different sources is a big headache.
• Analytics is largely the preserve of finance and logistics, using traditional tools. Access to data and insights is limited to few people.
• Culturally, significant decisions can be made without reliable evidence-based insights, and decision making tends to be done at the pace of regular meeting cycles – weekly, monthly or quarterly.

The Leading Role
In contrast, the Leading organisation looks different in strategy, organisation, capabilities and culture.

• The organisation has a strategy in which the role of advanced analytics is clearly articulated and well understood.
• Analytics are fundamental to the process of developing strategy, adapting it during execution and tuning operational performance.
• IT and business are closely aligned, facilitated by C-suite accountability for analytics development.
• Analytics teams often exist in a central enterprise Centre of Excellence and within Business Units. These people are recognised as key talent, and investment made in skills development.
• Enterprise standards are used for management of all critical data from capture to reporting, enabling high quality and the ability to reconcile and aggregate.
• Analytic tools are advanced and include machine learning technology. Data and insights are widely available and readily used.
• The culture is evidence-based (cf intuition-based) with respect to decision making. Performance experiments are a common methodology. Use of data results in faster decision cycles, sometimes approaching real-time.

So, back to the question. If your CEO wants a Leading organisation, does she really know a) what that means exactly and b) where you’re starting from? We’ve found agreement on the former and clarity on the latter are great places to start, especially if you consider both the technical and organisational changes together.

Measuring productivity remains a notoriously difficult problem, nowhere more so perhaps than in innovation. Feedback on the progress of projects and the performance of workers is scant, highly uncertain, and collected either too infrequently or too slowly. Yet such information is indispensable to the efficient allocation of resources to innovation projects. These challenges are all the more acute for companies involved in complex product development, where performance hinges critically on an organization’s capacity to constantly and consistently innovate. At the same time, information captured by enterprises has generally gone from scarce to superabundant, affording them an unprecedented opportunity to monitor information flows, observe worker interactions and organizational structures, and estimate individual and organizational performance.

We will discuss how companies are using data to obtain sharper, more timely insights. Specifically, we will present how real-time information about engineering collaborations are being leveraged to measure, model, and ultimately forecast organizational productivity and project performance with a level of accuracy and timeliness heretofore impossible. Over the past couple of years, QuantumBlack has developed and deployed an analytics tool to help companies in a variety of industries, from aerospace and automotive to software and semiconductor manufacturing, improve the yield of their project investments. The software tracks and analyses real-time communication and collaboration data, as well as data on performance metrics related to tasks and projects under assessment, to forecast organizational productivity, predict the success or failure of projects, identify performance bottlenecks and drivers, and ultimately help optimize resource and work allocation strategies.

The talk will center on case studies involving successful deployments at several Formula One (F1) teams. We will show how we were able to forecast the productivity of innovation teams, improve investment yields by as much as 15%, and raise productivity by nearly 20%. Certainly, this is no free lunch and we will dwell on some of the more important difficulties: the technological and computing challenges associated with machine-learning and real-time analysis of a transient data set that can grow at the rate of several terabytes per day, some of the privacy issues associated with trawling employee communications even if by machine-only readers, and finally some of the cultural and management challenges that we and our clients faced in deploying a capability that forecasts individual and organizational performance. By the same token, there is a great deal that enterprises can do to help build and facilitate the adoption of analytical capabilities within their ranks. After all, and as we will show, the returns certainly warrant the effort.

It’s this essential, continuous adaption to new information that is at the core of most of projects. If you’ve read our previous blog on Big Data you’ll appreciate that alongside the clever analytics we believe that crafting the narrative so that people can understand and act upon the intelligence is critical.

The importance of information design has been recognised in recent years as information has gone from been scarce to superabundant, and with a new generation of ‘designers that code’ there has been explosion of infographics with data the enabling ingredient at the nexus of art, science and human factors.

We think this is really cool, and admire the work of these data artists such as Jer Thorp instrumental at The New York Times, Nathan Yau at Flowing Data Flowing Data, David McCandless who curates Information is beautiful and Ben Fry Founder of Fathom – amongst others.

But.

What we’d really like to see is how these informative, and to our eye beautiful, views evolve over time. My instinctive response when looking at these charts, maps and data is to ask ‘what if’ and start playing with the scenarios in my head. And from our experience this is increasingly true with leadership in firms, who when faced with a Powerpoint immediately ask ‘what if we change x’ or ‘what if scenario y happens instead’. Don’t forget that this new generation of forty-something’s moving into senior decision-making positions have often grown up on Playstations and Internet. They seem to seek out the adaptive rather than static view of the decision they’re making, and the data underpinning it.

This is being amplified by the sheer speed at which information decays (the joke was always that the plan was obsolete as soon as it was printed, but even that joke is now obsolete in that your assumptions are often outdated before you’ve even written the plan). And as decision cycles in everything from Brand Management, R&D, Corporate Strategy, and Product Marketing are accelerating the ability for leadership to manage ambiguous decisions over time is a key skill.

It’s one of the key lessons we’ve learnt from developing Race Strategy software engines in Formula One™. Preparedness is important, clearly, but it’s the teams that can respond faster and with more precision as events unfold on the race track that hold the real competitive advantage.

Hence our mantra that you should always invest more in the capability to learn and adapt than you should in the original plan. We’re always striving for that human touch in our work, and managing time is an inherent part of this.

A recent O’Reilly Report stated it simply as “the future belongs to companies and people that turn data into products.” That’s good, that’s what QuantumBlack does!

In May McKinsey & Co got in on the act and published a weighty (it is McKinsey after all) tome on the subject in Big Data: the next frontier in innovation, competition and productivity. Its all pretty exciting stuff (certainly from our perspective), even if some of the forecasts seem hyperbolic there is undeniably a massive new market emerging, and one that will pervade every corner of our world. After all every business today is now an information business.

But.

We have a genuine worry that this rush to make use of information superabundance is missing some important elements. We shouldn’t forget that humans are involved, and when humans are involved it’s not always about the science and the perfectly rational answer.

For starters we don’t live in that world of perfect information; risk, quality and transparency are all challenges we humans throw up from time to time to make it a little harder for machines. We have to keep them on their toes after all. We’re covered of these ideas before with our concept of hard/soft data.

A second key element is the narrative. Why is this important, what does it mean, do I trust it, what if I change the way I look at it? All very human, and something we all do instantly when presented with information and intelligence. Our view is that visual design, information architecture and storytelling are going to be critical skills in tapping the power of analytics.

Bret Victor (www.worrydream.com is an ex-Apple designer, author of the fantastic paper on interface design MagicInk (a must read) with an obvious passion for experimentation. His latest project, Kill Math, is a fascinating concept; it’s not that he doesn’t believe in maths, he does – see how many of his design and engineering projects involve math. No, it’s the interaction and communication of maths, as Victor puts its “equations and squiggly symbols aren’t math at all: They’re merely our interface.” By making this interface complex, static and non-visual it makes it much harder for us to understand and communicate the underlying thinking and insight.

It’s in this combination of analytics and visual design that we believe the real edge will come from.

It seems the US Navy agrees, the Chief of Naval Operations Admiral Gary Roughead describes how “the biggest breakthrough…is the successful integration of intelligence with operations, and using the network to get information to the right person, at the right time, in the right way. That is where the power is.”

He calls this Decision Superiority, and made it one of the top five goals in 2010 for the U.S. Navy use of information dominance as a weapon. We love the term decision superiority; it may get borrowed.

Strategy and decision making has changed, the next decade will see the rapid emergence of data driven products and services, decision-cycles shortening to almost real-time and next generation of leaders having grown up on Playstation. Our core business is deploying analytics to help make strategic decisions so we’re fully paid up members of that club.

What’s interesting for us though is that in spite their undeniable power data and analytics remain tools, ultimately humans will still make judgements and to do they must understand the narrative i.e. what is it, why is that important and what do I do about it?

Technology is now enabling us to acquire, process and manipulate vast amounts of information in real-time but art of good storytelling is still crucial to making the right call.

We call it DDBA, and its more recipe than strict process. It’s pretty simple, and like all good recipes intended for guidance and interpretation to make it more personal and favoured to your taste.

1. Discover. We spend time with the client, their customers and our extended network to understand the specific context, challenges and scope. We aim to deliver an early concept storyboard that showcases the decision, the underlying data, analytics and interaction to ensure clarity and buy-in on decision being made. In our experience the decision being made is often subtly different from the decision being asked for.
2. Design. We blueprint the decision architecture; explaining the sources of intelligence (both good and bad quality), the analytics we’ll deploy (often hybrids), the options for investment, timing and deployment. Finally, and perhaps most importantly, the blueprint ensures the lucidity of the narrative behind the decision.
3. Build. Only after design phase is signed-off do we start building; be it strategy engines, investment models, application development, visual design or content integration. During Build fluidity, velocity and quality are key.
4. Adapt. Critical to our agile project methodology is working with the client to bake in a transparent framework for testing our assumptions as we go. We actively look to reduce the DDBA cycle to a series of tight loops rather than a longer linear project, which enables us to adapt rapidly in response to shifting conditions, competitive intelligence and

All you software engineers will recognise the agile DNA. For those decision scientists you’ll recognise the OODA loop. The difference is that we’re applying not just to the engineering workstreams but also to the design and, critically, to the business, the decision and its market context as in our experience this evolves.

Since we started our sweet spot has been at the fusion of strategy, analytics and design; and we’ve been so lucky in that we get to work with some of the most interesting organisations in the world on fascinating trade-offs, exploiting unusual sources of intelligence and building adaptive strategies.

It occurred to us that our projects, and the way we like to work with others, has been underpinned by a core set of principles that we thought we’d like to share.

1. We’re practical. We’ll start with what we’ve got and build from there, rather than proposing grand schemes.
2. We iterate lots. We look to prototype and storyboard as much as we can, as early as we can to elicit feedback and understand what works, and what doesn’t. We call this approach ‘learn and adapt’ and it’s at the heart of everything we do – from the decision technologies we build, the visuals we design and the way we work with clients.
3. We care about design. Be it an investment model, a new product concept or race strategy engine we focus on the visual design as much as the decision science- in our eyes ‘beautiful things work better’. Analytics are fantastic tools, but decision makers must be able to explain their reasoning and communicate the narrative of their decision.
4. We keep it simple. We always look for appropriate technology that meets the needs of our clients, ensures adoption and offers a clear ROI. This is not always the most sophisticated solution. And being simple actually often opens up creative new sources of intelligence and ways of acting upon it.
5. We strive for that human touch. We believe that, irrespective of the smart information, algorithms and visualisations that we use, making decisions is still ultimately a human judgement. Consequently we strive to ‘be human’ and create tools that are amazing and irreplaceable, beautiful and easy, authoritative and authentic.

It’s a simple list, and one we hope will continue to evolve.