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.

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.

Please register for the event here.

The goal of the Summit is to bring together leaders in the industry to discuss innovation and financial planning methods that are key to future business success. Presentations during the conference focus on new technologies and solutions, and how these developments are revolutionizing the modern business world.

For those of you following from home, Mat’s presentation, titled “The Game of Risk: Leveraging Collective Intelligence for Successful Product Introductions,” spoke to the challenges associated with planning and bringing a new product to market.

According to IDC, market leaders get 43% of their profits from new products. To ensure success, companies must have reliable insight into risks as well as opportunities. Mat discussed ways companies can leverage the wisdom of their employees to produce timely, unbiased, and accurate forecasts that inform key decisions around new product introduction.

If you are interested in more details around the conference (ongoing until tomorrow) – check out all the ways to follow the event here.

Next month, we will be leading a webinar series with Peer Insight around harnessing collective intelligence in the enterprise. Stay tuned for more info and updates on how to sign up here on the blog.

For more information on our partnership, click here.

The difference between Data and Insight was the topic of a 5 minute TED U talk by Monitor 360′s Doug Randall. He told the story of the US Government having access to reams of data, but relying on on the ground, crowd-sourced insights to combine with that data to make decisions related to military strategy. Algorithms can not replace the value of human instinct. The key to culling the insights is asking the right questions and tapping into the right “crowd.”

So nice to see the concepts that we’ve obsessed over and worked to champion make a showing at an amazing event like TED.

The new mech was designed by our very own Leslie Fine and two UC Berkeley PhDs and implemented using MATLAB.  When it came time to hook it into the Crowdcast platform, we decided to use the MATLAB runtime rather than port the algorithms over to Ruby.  Among other benefits, this path would make incorporating mech changes much more efficient.  On the flip side, project logistics became quite hairy.  In addition to our dev guys here in San Francisco, we’d need to cozy up to the folks at MathWorks (authors of MATLAB) in Massachusetts and get much more tightly coupled to our math guys in Berkeley and a telecommuting systems integration consultant.

We finally got it working, but only after a painful false start.  Initially we managed the project the way we’d done it at other companies — Gantt charts and meetings.  That failed miserably.  Milestones slipped left and right.  People were stressed and unhappy.  Then we started to manage the project using Crowdcast.  The change to overall performance was astounding.  Will have more to say about this later.  First, here’s where Crowdcast came in.

Rev two of the project entailed creating a series of crowdcasts around our milestones.  Crowdcasts are accurate, unbiased predictions that incorporate insights of a diverse group of people.  Crowdcasts aggregate two components: “bets” on the outcome and a comments that explain participants’ rationales.

One of the crowdcasts was around the beta release date of the new mech.  Huned created the crowdcast and invited everyone involved to participate.  He specified a target date, which is not made available to participants, so that he could monitor performance over time.  In particular, the system would alert him if thecrowdcast moved too far outside of his target.

About a month back when the project was in full swing I had a chat with our integration consultant.  He’d just started load testing the system, but it kept falling over due to bugs in the MATLAB runtime (in fairness to MathWorks, we were pushing the envelope on the capabilities of some of MATLAB’s features).  I believed that I had rare and relevant information to share — the beta release was at risk because of MATLAB bugs — so I rushed to my desk to place a bet.

At the time, the crowd — all participants who’d shared their insights on this crowdcast before me — believed that beta would happen on January 15 +/- 7 days.  Given my information, I thought it’d be at least a week later, so I selected a range of January 20-31 and bet $5000 of my virtual money.  I also left a comment explaining what my point of view.  Relative to crowd beliefs, I had about a 25% chance of being correct, so the system offered me a payoff of about $19,200.  If someone were to place the same bet just after me, the odds would now be higher, and so the payoff offered lower.  Neat thing about this is that the system rewards those who not only have accurate information, but are early to reveal it.

Behind the scenes, Crowdcast infers my perception of the probabilities of various outcomes by looking at my bet range, wager amount, and total portfolio value. It then updates the crowd curve using this information. All this heavy lifting enables us to do some really amazing things. For instance, we can “slice and dice” crowdcasts, to analyze sentiment by department or region. In the next post, I’ll talk about how these capabilities helped us identify potential problems and course-correct to navigate around them.