Six decades into the computer revolution, four decades since the invention of the microprocessor, and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works and can be widely delivered at global scale.
That’s Marc Andreessen, venture capitalist and Netscape co-founder, writing in the Wall Street Journal. The piece could just as easily be titled ‘Why Analytics Is Eating The World’. If you substitute “analytics” for “software” throughout his argument largely holds. Many of the businesses cited by Andreessen are not just software-centric, but analytics-centric as well: Google, Amazon, Netflix, Pandora, Facebook, LinkedIn. Such companies compete in arms race environments for extremistan market dominance.
In some industries, particularly those with a heavy real-world component such as oil and gas, the software revolution is primarily an opportunity for incumbents. But in many industries, new software ideas will result in the rise of new Silicon Valley-style start-ups that invade existing industries with impunity. Over the next 10 years, the battles between incumbents and software-powered insurgents will be epic. Joseph Schumpeter, the economist who coined the term “creative destruction,” would be proud.
Two of the incumbents mentioned are Wal-Mart and FedEx—both successful adopters of analytics. Of insurgencies:
Perhaps the single most dramatic example of this phenomenon of software eating a traditional business is the suicide of Borders and corresponding rise of Amazon. In 2001, Borders agreed to hand over its online business to Amazon under the theory that online book sales were non-strategic and unimportant.
Today, the world’s largest bookseller, Amazon, is a software company—its core capability is its amazing software engine for selling virtually everything online, no retail stores necessary. On top of that, while Borders was thrashing in the throes of impending bankruptcy, Amazon rearranged its web site to promote its Kindle digital books over physical books for the first time. Now even the books themselves are software.
Inciting innovation and driving disruption through software—and analytics—is not, however, without its challenges. Andreessen:
[M]any people in the U.S. and around the world lack the education and skills required to participate in the great new companies coming out of the software revolution. This is a tragedy since every company I work with is absolutely starved for talent. Qualified software engineers, managers, marketers and salespeople in Silicon Valley can rack up dozens of high-paying, high-upside job offers any time they want, while national unemployment and underemployment is sky high. This problem is even worse than it looks because many workers in existing industries will be stranded on the wrong side of software-based disruption and may never be able to work in their fields again. There’s no way through this problem other than education, and we have a long way to go.
This echoes two of Analyst First’s core contentions. First, that analytics is first and foremost about human infrastructure. Second, that although it is increasingly a core business literacy, analytics is at the same time beyond the reach of a growing number of workers:
The problem is, basic literacy and arithmetic numeracy is pretty much where it appears to have stopped for all but a new technological elite of scribes. This includes way too many people whose job it is to develop strategy, see “the big picture”, produce “evidence based policy”, hear the arguments of quantitatively skilled advisors or in many other ways interact with, and manage a data-rich world, of changing, poorly understood circumstances, vast uncertainty and with powerful analysis tools just a click away.
This is basically the condition of most people interacting with data in the modern world. These are the people who think that BI=Analytics=Reporting. These are the people who cannot read an XY graph, or trust any data summary more complex than an average. These are the people who when shown any kind of report, dashboard or graph ask to see the raw numbers because they are on firmer ground there, even if the numbers are millions of transactions and no useful inference can be drawn from eyeballing them.
Related Analyst First posts:
My interpretation of Tom Davenport’s recent work is that he’s addressing the relative reluctance of knowledge workers in established businesses to adopt and compete on analytics. In a recent LinkedIn discussion of the former post on TDWI’s Business Intelligence and Data Warehousing Discussion Group, Vincent Granville observed that:
Many industries still have little exposure to business analytics: government, military, education, transportation (except airlines), security, health care, entertainment, mining, etc. Some still stick to “old analytics”: insurances, banks, pharmaceuticals.
In response, two other discussants–Dorothy Hewitt-Sanchez and Phil Bailey–reflected on the difficulty of achieving business change in such environments.
All organisational change ultimately comes from ‘within’ in the sense that a business either changes itself or dies. But the impetus for change can come from either the inside or the outside. The outside impetus for change is some form of existential threat, typically from competition–e.g. “we’re losing market share; we need to change”. Note that all of the examples cited by Vincent are sectors with significant capital overheads, or lots of regulation, or both. These mean high barriers to entry and a fair amount of insulation from competition–that is, a buffer against having to change. Also note how many of the prominent competing on analytics success stories are, by contrast, in the online sector (Amazon and Netflix, for example).
Change can alternatively come from the inside–e.g. “we could do things better; let’s try to change”. Arguably that’s what performance management is about. But as Davenport acknowledges–by focusing on the prospect of knowledge worker resistance to additional structure–most of us, most of the time, don’t relish disruptions to the status quo. The fragility of inside change comes from the fact that the imperative is inherently weaker and less clear. The status quo is not demonstratably problematic. Proactivity, compared to reactivity, is a luxury response.
As legislated monopolies, government agencies are an interesting midpoint between inside and outside change. Unlike businesses, their existence is for all intents and purposes guaranteed, and they don’t have competitors. But they are subject to various forms of outside change. New policies force organisational change. Those agencies responsible for law and order, security, revenue collection, and other fundamental conditions of sovereignty have adversaries–who, like competitors, are a type of threat. That said, most change in the public sector is the product of internal initiative.
Vendors, consultants, and other service providers are adept at seeing the ‘need’ for Business Analytics. However, not all need expresses itself as demand. Demand is need that has been recognised, prioritised over other needs, and accordingly resourced. Outside change propels need into demand more powerfully than inside change.
Related Analyst First posts:
If you’ve attended more than a few Business Analytics conferences you’ll have seen numerous non-vendor presentations which fall into one of two categories.
The Honeymoon Presentation chronicles the experiences of a solution buyer. It’s often co-presented or sponsored by a software vendor, and is very common at vendor-organised forums such as user conferences. It covers the identification of a business problem, the formation of a project team, the creation of a business case, the development of business requirements, the challenges of stakeholder management, the discovery of trade-offs, and the resulting criteria and process by which a supplier was selected. It closes with ambitions and plans for the future. It may not be explicitly stated by the presenter, but nothing has been rolled out yet. That is, it reflects the perspective of an organisation in the early stages of Business Analytics, having followed the default, top-down, project-based, IT-centric model.
The Proud Parent Presentation comes from this same perspective but is delivered after a project phase has been successfully completed. As such it will typically relay much of the above narrative, adding an overview and some screenshots of the system that has been implemented and closing with some ‘lessons learned’ in the process. Most Proud Parent presentations are fairly interchangeable in terms of the generic outcomes described. In the BI context, typical outcome claims are things like “now our business users have more information at their fingertips” and “for the first time we have one version of the truth”. What varies most are the characteristics of the presenting organisation: industry, size, products, customers, and so on.
There are two types of presentation you most want to see but never do. The first is the Failure Presentation. Rather than being a story of general success sprinkled with a few tips and tricks picked up along the way, it’s about the hard lessons that only painful reflection on resounding failure can teach. Then there’s the Arms Race Presentation, in which an organisation reveals the inner workings of how it competes on analytics, manages its strategic threats, and stays a step ahead of its adversaries. I have never seen either of these presentations at public conferences, but they do exist. They’re the most informative, but for obvious reasons the least likely to see the light of day on the conference circuit.
Related Analyst First posts:
The Analyst First view is that strategic analytics is in many respects easier than operational analytics. In part, operational analytics is hard because motivating and coordinating humans is hard. For typical operational analytics applications to consistently work end-to-end (e.g. driving up customer acquisition, retention and value via predictive modelling and campaign management) and to be able to prove and articulate their value-add, they require the coordination and cooperation of, at a minimum, people in each of the following organisational functions:
- Data Warehousing and BI
- Call Centre
- Product Management
The dependent set of business processes are difficult to execute. They are inherently brittle due to their many moving parts. But they’re also difficult to coordinate because they’re human-centric processes. The monitoring and performance management needs of humans are demanding and resistant to automation. The maintenance of human capital is far more mercurial and challenging than the maintenance of physical or information capital. This has implications for competition. It decreases the attractiveness of competing on analytics—particularly operational analytics—relative to alternative competitive frontiers.
Google is a competing on analytics business through and through. But its recent purchase of Motorola Mobile, according to many analyses, was about arming it to attack its competitors in the courtroom using its lawyers rather than in the marketplace using its engineers. Motorola Mobile’s thousands of patents provide it with new ammunition in its patent arms race with Apple, Microsoft, and others in the mobile telephony hardware sector. Its move into the political lobbying game was similarly explained five years ago.
What makes lawsuits and lobbying more attractive than analytics—to a company built on analytics? The law and the legislature are like analytics in many respects: complex domains, information based, and the province of highly qualified, experienced and intelligent specialists. However, far less of their complexity is contingent on the effective coordination and performance management of human activities inside an organisation. Operational analytics frequently fails because a business is divided against itself. Lawyers and lobbyists, on the other hand, can represent a large, complex, multinational business as a single, unified entity. The human coordination effort is far simpler and much less fragile.
Like any other competitive processes, lawsuits and lobbying campaigns can be more effective if they’re informed by analytics. But the sort of analytics that can do this will be of a more bespoke, tactical, or strategic nature, and less amenable to standard practices and operationalisation as IT processes.
Related Analyst First posts:
The New Yorker recently ran a fascinating profile of Ray Dalio, the founder of Bridgewater Associates, the world’s richest hedge fund. From an Analyst First point of view the piece offers a window into the human infrastructure of an arms race environment. Bridgewater is a culture committed to making and learning from its mistakes:
“Our greatest power is that we know that we don’t know and we are open to being wrong and learning.”
In his Principles, Dalio declares that acknowledging errors, studying them, and learning from them is the key to success. He writes, “Pain + Reflection = Progress.” Bridgewater puts this equation into action by organizing lengthy assessment sessions, in which employees must discuss their mistakes.
“What we’re trying to have is a place where there are no ego barriers, no emotional reactions to mistakes. . . . If we could eliminate all those reactions, we’d learn so much faster.”
Part of Bridgewater’s human infrastructure is a commitment to radical transparency. Some of its key items of electronic infrastructure are therefore video and tape recorders:
Like virtually all meetings at Bridgewater, this one was taped. Dalio says that the tapes—some audio, some video—provide an objective record of what has been said; they can be used for training purposes, and they allow Bridgewater’s employees to keep up with what is going on at the firm, including his discussions with senior colleagues. “They get to see all of my mistakes,” Dalio told me.
One rule of radical transparency is that Bridgewater employees refrain from saying behind a person’s back anything that they wouldn’t say to his face.
This means that management’s misgivings about a particular employee’s suitability for promotion are discussed openly with him, and recorded. (He doesn’t get the promotion.)
James Comey, the firm’s top lawyer… [took] a while to get used to dealing with Dalio. “When Ray sent me an e-mail saying, ‘I think what you said today doesn’t make sense,’ I tended to think, What does he really mean? Where’s he coming from? And what is my play? Who are my allies? All of the things you think about in the outside world. It took me three months to realize that when Ray says, ‘I think you are wrong,’ he really means ‘I think you are wrong.’ He’s not trying to provoke you, or anything else.”
“What is a typical organization?” [Dalio] asked me one day. “A typical organization is one where people are walking around saying, ‘This is stupid, this doesn’t make sense,’ behind each other’s backs.”
The article is also illuminating in its discussion of Bridgewater’s analysis and trading philosophies, which reflect its acceptance of uncertainty:
[T]he Pure Alpha fund typically has in place about thirty or forty different trades. “I’m always trying to figure out my probability of knowing,” Dalio said. “Given that I’m never sure, I don’t want to have any concentrated bets.” Such thinking runs counter to the conventional wisdom in the hedge-fund industry, which is that the only way to score big is to bet the house.
Many economists start at the top and work down. They look at aggregate statistics—inflation, unemployment, the money supply—and figure out what the numbers mean for particular industries, such as autos or tech. Dalio does things the other way around. In any market that interests him, he identifies the buyers and sellers, estimates how much they are likely to demand and supply, and then looks at whether his findings are already reflected in the market price. If not, there may be money to be made.
Bridgewater is more a qualitative than a quantitative trading fund. In this context, its decision support systems are interesting:
To guide its investments, Bridgewater has put together hundreds of “decision rules.” These are the financial analogue of Dalio’s Principles. He used to write them down and keep them in a ring binder. Today, they are encoded in Bridgewater’s computers. Some of these indicators are very general. One of them says that if inflation-adjusted interest rates decline in a given country, its currency is likely to decline. Others are more specific. One says that, over the long run, the price of gold approximates the total amount of money in circulation divided by the size of the gold stock. If the market price of gold moves a long way from this level, it may indicate a buying or selling opportunity.
In any given market, Bridgewater may have a dozen or more different indicators. However, even when most or all of the indicators are pointing in a certain direction, Dalio doesn’t rely solely on software. Unless he and Jensen and Prince agree that a certain trade makes sense, the firm doesn’t make it. While this inevitably introduces an element of human judgment to the investment process, Dalio insists it is still driven by the rules-based framework he has built up over thirty years. “When I’m thinking, ‘What is going on today?,’ I also need to make the connection to ‘How does what is happening today fit into our framework for making this decision?’ ’’ he said. Ultimately, he says, it is the commitment to systematic analysis and systematic investment that distinguishes Bridgewater from other hedge funds.
In other words, Bridgewater runs on human judgement augmented by decision support, not decision automation. It recognises that decision support leads to higher value decisions but in practice makes decision making harder, not easier. As a recent Analyst First post argued, new information is not always “actionable”:
Comey was initially struck by how long it took Bridgewater to make decisions, because of the ceaseless internal debates. “I said, ‘Lordy, we have to put tops on bottoms. Let’s get something done,’ ” Comey recalled. But he added, laughing, “The mind control is working. I’ve come to believe that all the probing actually reduces inefficiencies over the long run, because it prevents bad decisions from being made.”
Dalio on ownership:
“I don’t want Bridgewater to go public or have it controlled by anybody outside the firm,” he said. “I think people who do that tend to mess up the firm.”
On the nature of competition in financial markets:
[Dalio] regards it as self-evident that all social systems obey nature’s laws, and that individual participants get rewarded or punished according to how far they operate in harmony with those laws. He views the financial markets as simply another social system, which determines payoffs and punishments in a like manner. “You have to be accurate,” he says. “Otherwise, you are going to pay. Alpha is zero sum. In order to earn more than the market return, you have to take money from somebody else.
And finally, on the global economy:
Dalio believes that some heavily indebted countries, including the United States, will eventually opt for printing money as a way to deal with their debts, which will lead to a collapse in their currency and in their bond markets. “There hasn’t been a case in history where they haven’t eventually printed money and devalued their currency,” he said. Other developed countries, particularly those tied to the euro and thus to the European Central Bank, don’t have the option of printing money and are destined to undergo “classic depressions”.
Related Analyst First posts:
This year will see the cinema release of Moneyball, the story of the Oakland A’s baseball team’s successful use of analytics under general manager Billy Beane, based on Michael Lewis’ 2003 book of the same name:
Several themes Lewis explored in the book include: insiders vs. outsiders (established traditionalists vs. upstart proponents of Sabermetrics), the democratization of information causing a flattening of hierarchies, and the ruthless drive for efficiency that capitalism demands. The book also touches on Oakland’s underlying economic need to stay ahead of the curve; as other teams begin mirroring Beane’s strategies to evaluate offensive talent, diminishing the Athletics’ advantage, Oakland begins looking for other undervalued baseball skills such as defensive capabilities.
Professional sporting leagues typify arms race environments. It’s in this context that Daryl Morey, General Manager of the Houston Rockets basketball team and another sports analytics proponent, argues that ‘Success Comes From Better Data, Not Better Analysis’ at HBR:
I see a world teeming with really good analysts. Fresh analytical faces are minted each year and sports teams are hiring them in larger numbers. If talented analysts are becoming plentiful, however, then it follows that analysts cannot be the key to creating a consistent winner, as a sustainable competitive edge requires that you have something valuable AND irreplaceable… The answer is better data… Raw numbers, not the people and programs that attempt to make sense of them.
Data vs Analysts is a false dichotomy. Morey invokes it presumably to provoke. His description of Google makes this clearer:
Smart companies such as Google believe they need savants to crunch those numbers and find the connections that regular humans could not. But my experience, and what I’m hearing from more organizations (sports and non), shows that real advantage comes from unique data that no one else has.
Google’s belief in good analysts is not in dispute. It’s Chief Economist, Hal Varian, argues that competing organisations ideally want a ‘monopoly on left shoes when right shoes are free. That today, data is ubiquitous and cheap and analysis is the complementary scarce factor.’ But Google, of course, has masses of proprietary data that no one else has. I can’t get its dataset on me, for example. It doesn’t help its cause to talk this up in public given its dependence on its users’ trust and concerns about privacy. Doubtless Morey’s downplaying of analysts also serves PR and competitive ends.
It’s obvious that an analyst can’t do much without data, just as data alone is useless without an analyst. It’s also obvious that, given the same data, the better analyst will produce the competitive advantage. In an arms race environment, any such advantage will eventually become operationalised as common or ‘best practice’, and the search for a new competitive edge will shift focus. Competitive advantage comes from sustained uniqueness. It’s agnostic with respect to source.
Nor are analysts passive with respect to data. One of the key functions of an analyst is to determine what new data would be valuable, and to derive, source, or find other ways to create it. Morey:
For obvious reasons, I cannot reveal what data the Houston Rockets track but to track the significant data we gather we use a very large set of temporary labor that helps us develop these data sets that we hope will create an advantage over time.
It was no doubt the Rockets’ analysts who specified that new data, and it will be them, in the coming seasons, validating and testing it to assess its value. The analyst is always first.
Related Analyst First posts:
That’s the title of today’s Harvard Business Review Management Tip of the Day, which is worth reproducing in full:
Differentiating your company based on products or cost is near impossible these days, especially in crowded industries. Instead, pull ahead of the pack by using data-collection technology and analysis to get value from all of your business processes. Analytics let you discern not only what your customers want, but how much they’re willing to pay and what keeps them loyal. It also arms your employees with the evidence and tools they need to make sound decisions. Start by championing analytics from the top. Acknowledge and endorse the changes in culture, process, and skills that analytics competition requires. Be sure that you understand the theory behind various quantitative methods so you can recognize their limitations. If necessary, bring in experts who can advise on how to best apply analytics to your business.
Related Analyst First posts:
Welcome to A1′s very first podcast.
This is a relatively quick (less than 30 mins) overview of what Analyst First is all about, and why Human Infrastructure matters so much.
This is a recording of the presentation I gave to the Intelligence 2011 conference, which is the annual conference of the Australian Institute of Professional Intelligence Officers (AIPIO), as part of their very apt “The Analyst vs the IT” stream.
Steve Miller at Information Management interviews Revolution Analytics CEO Norman Nie. Nie makes the important point that when Business Analytics is central to an organisation it won’t be acknowledged:
One of the biggest challenges of working in our industry is that it’s difficult to find public customers. Nobody wants to share their “secret sauce” with competitors, so the vast majority of companies that we work with wish to remain anonymous.
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