I was sorry to read, via Gary Cokins, of the recent passing of Jeremy Hope, who along with Robin Fraser pioneered the Beyond Budgeting movement and co-founded the Beyond Budgeting Round Table (BBRT). As Cokins summarises:
Their basic message was that the annual budgeting process is so broken and dysfunctional that the best solution is not to reform it but rather to abandon the process altogether. Their solution was to understand the underlying purposes of a budget and apply methods, like driver-based rolling financial forecasts, that fulfill the purposes of a budget.
Having spent a good part of the last twelve years as an enterprise budgeting and planning specialist I have a great deal of sympathy for this view. The underlying purposes of budgets are rarely clarified and distinguished from each other. As I’ve written about before, this leads to much wasteful confusion, both practical and linguistic:
In reality the budget is a hybrid because it serves two main purposes. It sets performance targets (goal setting) and limits the resources available to those pursuing them (planning). Both goal setting and planning are necessarily reliant on forecasts, although these underlying objective estimates are not always made explicit. Updated plans and targets – they are commonly revised within a financial year – are often referred to as “forecasts”.
The enterprise budget is an odd and hybrid beast. Many of its perversities and pathologies are familiar to everyone who’s worked in an organisation: arbitrariness, inflexibility, unresponsiveness to change, incentives to game the system (underplaying revenue potential while overstating costs), encouragement of ‘use it or lose it’ spending, disconnection from strategy. Then there is its being expressed in the language of accounting, which is not the natural language of most businesspeople. Finally, there is the sheer complexity of its enterprise coordination—the annual ‘march of a thousand spreadsheets’. Most of this coordination effort is in fact completely unnecessary. The bulk of any organisation’s expenditures are preordained. They’re either fixed, or circumscribed by its balance sheet. The planning (resource allocation) aspect of budgeting is thus fundamentally a top-down exercise. However, its goal setting aspirations lead to an insistence that budgets be built bottom up, painstakingly, by individual managers. The idea is that this generates ‘buy in’. Typically, however, the bottom-up aggregations never conform to the top-down constraints, so they get overridden during the budget finalisation process.
Despite all of this, the annual budget remains stubbornly embedded in the workings of most organisations—more understandably in government, where it fulfills a legislated purpose, than in the private sector. I attended a seminar with Jeremy Hope in Sydney, from memory in 2004, facilitated by the Institute of Chartered Accountants in Australia (ICAA). I remember asking Hope why it was that adoption of Beyond Budgeting’s principles was relatively rare. It was notable that the practitioners featured in Beyond Budgeting’s case studies (companies such as Toyota and Svenska Handelsbanken) had been using it successfully for decades. If the good news wasn’t new, why such resistance? His answer, in essence, was that the status quo, although widely acknowledged as inefficient, was so familiar that dismantling it was literally unimaginable for most budgeteers. Disrupting it was a long and uphill battle.
Beyond Budgeting is to budgeting as Lean Startup is to entrepreneurship and Analyst First is to Business Analytics. Each movement takes a first principles approach to diagnosing, in order to do away with, a set of wasteful habits of thought and practice which result from convention and are sustained by poor incentives.
Related Analyst First posts:
Eric Ries, Harvard Business School’s ‘Entrepreneur-in-Residence’ and the founder of Lean Startup, has been interviewed a number of times recently following the launch of his Lean Startup book. As we have argued before at Analyst First, the ‘unknown problem / unknown solution’ domain—in which and for which the Lean Startup approach was developed—reflects the world of Business Analytics. In the 12 to 18 months since giving the last interview we linked to, Ries has significantly enriched the ideas of Lean Startup. Two new interviews are highly recommended. The first is from the Commonwealth Club of California’s Inforum program. Details here and audio file here, or here. The second is from the ITConversations network’s Tech Nation program. Details and download here.
Ries takes it as axiomatic that enterpreneurs seek to create “institutions of lasting value” in an unstable environment. He defines a startup as any “human institution designed to create something new under conditions of uncertainty”. The role of the Lean Startup toolkit is to help entrepreneurs navigate the best path in this context. Most contemporary management tools have their heritage in twentieth century manufacturing and are based on forecasting and planning. They assume that the world is stable enough to be predicted such that plans can be reliably devised and executed. As Ries points out, and should be obvious, this assumption simply doesn’t hold in the environments in which many of us now work.
In the twenty-first century we can build almost anything that can be imagined. The challenge is not to build more stuff. It’s to build the right stuff. Most startups fail, says Ries, because they make the wrong things. The key activity of a startup should therefore be learning, not building. What creates value for a startup is it determining whether or not it’s on the path to a sustainable business.
Lean Startup is a scientific approach to new product development which treats everything a startup does as an experiment. The goal is to collect data (feedback about what customers want) with minimal cost, not to build to a pre-determined product specification (which assumes what customers want) with minimal cost. In service of this, the Lean Startup movement is developing ‘innovation accounting’, an attempt to revolutionise the existing accounting paradigm so that it can operate under conditions of uncertainty and instability. The current planning-based paradigm (are we on time, on budget?) is unable to distinguish between the threshold of success and the brink of failure. The core question being asked by innovation accounting is, instead: are the experiments the team’s doing affecting customer behaviour?
Clearly there are implications here for Business Analytics. We’ve often written here of the uncertainty inherent in data-driven analysis, and of the unsuitability of the default IT project plan-based, build-centric, waterfall approach to what is inescapably an exploratory and learning-oriented set of activities. Much of the Lean Startup approach translates directly into the Analytics Lab. However, the constraints faced by those attempting to innovate from within already established organisations (who Ries terms ‘intrapreneurs’) are not the same as those which frame the entrepreneurial enterprise. Entrepreneurs operate until the financial capital provided to them by venture capitalists runs out. The ‘lean’ in Lean Startup seeks to maximise the number of experiments they can run before this happens. The entrepreneurs described by Ries all enjoy a large and fundamentally interchangeable prospective customer base. Many unsuccessful experiments can be run on different user populations in search of a loyal base, essentially without consequences. Unhappy users don’t stick around. This is not the case for intrapreneurs. Within an organisation there are only a limited number of prospective analytics customers, and disappointing any one group leaves a legacy. The scarce resource for intrapreneurs is political capital.
Related Analyst First posts:
Is Big Data a Bubble?
In case you’re in a hurry: Of course it is. And that is good.
Quentin Hardy in The New York Times Bits blog summarises the state of play regarding the business world’s interest in and utilisation of big data. As he recognises, big data is really about “the benefits we will gain by cleverly sifting through it to find and exploit new patterns and relationships.” Big data drives up the obviousness of the need for analytics. Its often invoked qualities of volume, velocity, and variety mean that we can’t pretend to analyse it without statistical and machine learning methods.
Big data analytics at the present moment, however, is characterised by uncertainty. What is big data, exactly? What’s its value? How is it analysed? Which technologies should be used? Which standards will prevail? Where to invest? There is, Hardy finds:
a common problem in the Big Data proposition: Often people won’t know exactly what hidden pattern they are looking for, or what the value they extract may be, and therefore it will be impossible to know how much to invest in the technology. Odds are that the initial benefits, as it was with Google’s Adwords algorithm, will lead to a frenzy of investments and marketing pitches, until we find the logical limits of the technology. It will be the place just before everybody lost their shirts.
This is a common characteristic of technology that its champions do not like to talk about, but it is why we have so many bubbles in this industry. Technologists build or discover something great, like railroads or radio or the Internet. The change is so important, often world-changing, that it is hard to value, so people overshoot toward the infinite. When it turns out to be merely huge, there is a crash, in railroad bonds, or RCA stock, or Pets.com. Perhaps Big Data is next, on its way to changing the world.
Such is technology. Hence the level of uncertainty, and also reasons for optimism:
There are an uncountable number of data-mining start-ups in the field: MapReduce and NoSQL for managing the stuff; and the open-source R statistical programming language, for making predictions about what is likely to happen next, based on what has happened before. Established companies in the business, like SAS Institute or SAP, will probably purchase or make alliances with a lot of these smaller companies.
Expect to see a lot more before it all gets sorted out.
TDWI’s recent Big Data Analytics report by Philip Russom, based on 325 sets of responses to a May 2011 survey, echoes this and provides a far more comprehensive overview of how businesses are coping with the realities of big data. One measure of how unsettled the field is right now is that, in response to the question ‘Which of the following best characterizes your familiarity with big data analytics and how you name it?’, 65% of the survey’s respondents replied, ‘I know what you mean, but I don’t have a formal name for it.’
The report is recommended in full, but in case you’re still in a hurry, its margin summaries can be skimmed through in a matter of minutes.
Related Analyst First posts:
About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
Tags in a CloudAIPIO analyst first Analyst First Chapters analytics analytics is not IT arms race environments big data business analytics business intelligence cargo cults collective forecasting commodity and open source tools complexity data decision automation decision support educated buyer EMC-greenplum forecasting HBR holy trinity human infrastructure incentives intelligence model of analytics investing in data lean startup literacy management culture MBAnalytics operational analytics organisational-political considerations Philip Russom Philip Tetlock prediction markets presales R Robin Hanson Strategic Analytics tacit data TDWI Tom Davenport uncertainty uneducated buyer vendors why analyst first