All employees within an organization – regardless of title, function, or location – have insights that can help an organization better understand its risks. But their insights often go unheard. In a recent extreme example, the crew of Deep Water Horizon expressed significant concerns before the explosion – but they report not being listened to.

Unfortunately risk management is often treated as the responsibility of “risk management” – not of the entire organization. For example, according to Deloitte’s Enterprise Risk Management Benchmark Survey, 67% of respondents reported only providing risk management training to specialists in specific risk management functions.

Social BI is an effective, low cost way for organizations to expand risk management beyond the risk function. Like business intelligence, Social BI aggregates intelligence from multiple sources and presents a summary analysis to decision makers. But while traditional BI provides reams of data based on sophisticated models, Social BI aggregates insights contributed by people. These people-driven insights include all of the informal knowledge that is not captured by traditional BI systems.

With Social BI, you’ll get real-time risk assessments – free from bias – that represent what your employees think is likely to happen. You’ll learn about changes that affect your risk long before this information would trickle up through the standard chain of command. You’ll hear from a diversity of perspectives. You’ll identify where there is a lack of alignment between different parts of the organization. And you’ll learn what your people think should to be done to mitigate risk.

You might be thinking, “This sounds good, but would employees actually participate?” In our work, we’ve learned that many employees are hungry for ways to help their companies address key business questions. In feedback surveys, participants consistently identify the following reasons for contributing to Crowdcast: 1) opportunity to share their insights with senior management, 2) testing their knowledge, and 3) learning from others.

If you’re looking for innovative approaches to creating a risk-aware culture and improving your understanding of key risks, consider incorporating Social BI into your ERM program.

There are key decisions made in corporations every day. In the past, executives would rely exclusively on data-driven systems. These were aggregated into Business Intelligence (BI) platforms, showing key data from all of the systems in the organization. The problem was that these systems were purely data-driven, and therefore backwards looking. They had no ability to incorporate real-time, rich, insightful knowledge – human knowledge.

The fact is that data systems simply cannot keep pace with the rapidly changing business landscape. Whether you’re trying to track time-to-market for a product, determining competitor actions, or forecasting sales of a new product, your people have important information and context to share. They talk about it in the cafeteria and in the hallways, but it’s not in your BI systems.

This is where social BI comes in. Like traditional BI, Social BI aggregates intelligence from throughout your company and presents a quantitative summary analysis to decision makers. However, in Social BI, the resource is your most valuable one – your employees and partners. This fundamentally changes the sources executives use to make business decisions by mining previously unavailable insight from across the organization to achieve unprecedented accuracy in business planning and forecasting.

Social BI empowers employees to share and socialize vital insights on key metrics. It enables leaders to tap the social intelligence of their workplaces to improve business intelligence. It drives better-informed and ultimately more profitable business decisions.

Mary kindly did a quick write-up of a panel at which I spoke.  Called ‘Strategic Analytics for E2.0,’ it focussed on using data to understand how social networks work within the enterprise and how to optimize their activities.  There are two ways to interpret this effort.  One, which is the approach most of my co-panelists took, is that social activities in the enterprise are passively creating a massive amount of data.  This data, if mined properly and presented to the right people, can create an enormous intelligence source never before accessible.  This allows for one kind of more agile enterprise.

The other, and this more closely aligns with Crowdcast’s point-of-view, is that in a more social enterprise, it is natural to harness the tacit knowledge of your employees and partners.  It is now possible to find the right people of whom to ask key business questions, and to take their social intelligence and create the new wave of business intelligence from it.  It is possible to surface knowledge where it exists, rather than relying on the org chart and isolated pockets of social knowledge.

Mary summarized the discussion so nicely that I won’t repeat it here.  Instead, I suggest that you check out her post.

But before we get the blog cooking again, I want to take a moment to highlight the great press we’ve enjoyed as a result of our efforts.  The way the world views you is so much more telling than how you view yourself, so here are some great themes that the press pulled from our conversations.

• Robin Wauters at TechCrunch summarized our offering nicely, saying, “Crowdcast’s solutions, composed of a SaaS software platform, consulting and support services – aim to bridge the gap between traditional business intelligence and enterprise social network applications. The idea is for businesses to align their employees with the purpose of the company, bringing all their insights, plans and experience together in one place, all in order to create “insanely accurate” business predictions and outcomes.
• VentureBeat’s Anthony Ha highlighted that “Crowdcast is all about finding the knowledge in your workforce.”
• Jeff Nolan at Enterprise Irregulars zeroed in on one of our key differentiators, saying, “Where Crowdcast appears to have departed from previous efforts to implement crowdsourcing inside the enterprise is that rather than being an ideas-like site where anything can get posted and then promoted/demoted by community members, they are building games that are designed to reveal outcomes. In other words, they form every interaction around a tightly focused question that requires an answer and through virtual currency create competition in the form of betting that narrows the result to what is considered the most probable outcome, which is then measured according to what actually occurs over time.”
• VentureWire’s Ty McMahan really nailed our point-of-view, when he said, “Crowdcast’s vision is for employees to participate in sharing and socializing insight on what’s planned and what the outcome of business activities will be. The company wants to change the sources executives use to make business decisions by mining previously unavailable insight from across the organization.
• Alex Williams at ReadWrite notes that our “$6 million from Menlo Ventures in a deal that bodes well for a new generation of companies with crowdsourcing and business intelligence offerings.“
• Mike Vizard at IT Business Edge was excited by a promising future, where “longer term… an organization can soon start to map out who knows what within their organization.”
• Amanda Coolong over at This Week In had me on for a great interview.  Thanks Amanda!  My favorite quote was from Dave Linthicom, “… the info is there, they know it’s there, they see tidbits of it, but they can’t mine it for it’s value. So, if you’re working on that problem, kudos to you because you’re going to make a billion dollars.”

PEHub, VatorNews, Silicontap, and others helped us spread the message far and wide.  We look forward to talking more as Crowdcast continues to evolve and champion the message that Your People Know.

Thanks, everyone.

I really like this notion. It fits nicely with the tenets of collective intelligence and social business intelligence approaches to decision making. In order to be able to adapt, a business has to be able to have an accurate and up-to-date read on what’s really going on, which is exactly the insight such applications deliver.

Thing is, forecasting is still vital. General Patton‘s famous quote comes to mind: “A good plan, violently executed now is better than a perfect plan next week.” So you start executing to a plan. At the same time, you enable everyone involved to continuously share information about how things are tracking. So long as the collective wisdom of the people in the trenches lines up with the master plan, life’s (probably) good. But when the two start to diverge, it may be time to adapt.

The current leader in the market is Samuel Driessen, an Information Architect at Océ, a 22,000-employee provider of digital document management technology and services. In recognition of his performance, he has been awarded an all-access pass to the upcoming E2.0 Conference in Boston. Many thanks to Steve Wylie of TechWeb for sponsoring this prize.

The actual results for the forecasts in the Adoption Index Prediction Market won’t be known until early June. So what does it mean that Samuel is currently in first place? In essence, he’s a trend spotter. If you want to know what people think will happen with E2.0, talk to Samuel. In June, we’ll find out if he’s equally as good at calling actual outcomes.

What does Samuel think the future holds? “To me, I’m not as focused on the Web 2.0 applications as I am on the underlying concepts. These concepts are here to stay. They will fundamentally change the way we interact in this world, how businesses will run internally, and how they’ll interface with customers. Openness, transparency, and network effects are here to stay.”

Samuel is dedicated to instilling these concepts at Océ. He’s proud of the value he’s created for Océ by implementing E2.0 tools such as wikis, blogging, microblogging, and social bookmarking. “It’s really interesting to see how people are finding and helping each other as a result of these technologies.”

We particularly like Samuel’s comparison of structured information processes (e.g., ERP systems) and unstructured information processes (e.g., microblogging). Samuel thinks prediction markets have the potential to bridge the gap between these two types of processes. The “Adoption Index market gave me a great chance to see what the power of a prediction market is.”

It will be a couple more months before we know whether Samuel will keep his first place position. In the meantime, we appreciated the opportunity to hear from such a thoughtful member of the E2.0 community.

Operational risks take many forms. Brand managers bringing new products to market face market risk: If we build it, will they come? Project and operations managers think about execution risk: Will we be able to build it in time and to spec?

All of these risks have some interesting properties. They are all forward looking metrics. Product ship dates and unit or revenue projections for a new product are examples. They are quantifiable and objective. Your employees and partners, though their points of view may differ, probably have knowledge about them. And the true outcome of the event (or whether it took place at all) becomes knowable in the not-too-distant future.

It so happens that these are exactly the criteria we use to determine whether a metric is suitable for crowdcasting. This, coupled with the fact that risk management is moving to the fore (IBM, in a study of 1900 CFOs in a variety of verticals, found that 83% of them are either advisors or decision makers in risk management. “The New Value Integrator”, IBM Global Business Services, 2010), is part of the reason SAP has decided to integrate Crowdcast into their Governance, Risk, and Compliance product suite.

We believe that this is a potent combination. These developments have gotten the attention of the media (ITBusinessEdge) and analyst community (Will Jan of Aberdeen wrote about it here and here). Importantly, we’re starting to see customer traction. There is also a nice big picture story taking shape on the integration of structured and unstructured data, which I will cover in a later post.

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Some implementations of prediction markets compute probabilities of outcomes. To use Dr. Kambil’s example, one could ask, “Will the DJIA end above 10,000 before the end of the year?” Market participants who are bullish about the Dow buy shares. Those who are bearish sell. As trades come in, the market mechanism adjusts the probability of the event actually taking place.

While this is pretty interesting, getting to a collective forecast that gives you a probability distribution of possible outcomes, rather than just a point forecast, of the DJIA itself is awkward. To do it, you’d have to launch different markets around buckets of outcomes — as in, DJIA in 8,000-9,000, DJIA in 9,000-10,000, DJIA in 10,000-11,000, and so on — and then reason about the probabilities of each.

Crowdcast builds probability distributions automatically. This has two important implications. First, it enables a simple user interface and finer grained expression of beliefs. Rather than asking people to choose predefined buckets, they can select a precise range, as wide or narrow as they wish. And second, it supports some great applications for business. For instance, you can get alerts when the likelihood of hitting your target ship date falls below 50%.

Dr. Kambil also discusses the information dissemination “feature” of prediction markets. Prediction markets, like the public stock market for instance, not only collect information, but they also distribute it. While this is great for some applications — surely you’d want to know the price (and other indicators) of Apple stock before you bought some — it’s a problem for others.

In the enterprise, it is often the case that the more valuable and important a metric, the more secret it is. Revenues or earnings per share are but two examples. We’ve struggled with this reality for some time. Initially, our take was that we should simply concentrate our efforts on problems characterized by “public” metrics. Then we implemented access control lists, which enabled our customers to publish questions about sensitive metrics to a subset of the participants.

While this worked for some applications, the overall approach just didn’t sit well. There is knowledge in the enterprise about revenues and plenty of other sensitive metrics. And we’re all about harnessing knowledge. Won’t let the cat out of the bag yet, but we’ve cracked it! Details very soon.