January saw Obama’s inauguration, and a reflection on Bush’s analytic legacy, a discussion on whether “gut feelings” have any basis in science, a dashboard showing the best companies to work for in the US, and a quick demonstration that Malcom Gladwell was right.

February’s posts discussed the power of having the right data, and that dashboards have been around for a long, long time, but if they have poor data it’s like putting lipstick on a pig, and that it would be a crime not to include analytics in applications — but also that we’re all really bad at making decisions.

March’s posts were about BI in Bahrain, Brussels, and on the Bosphorus, making tough decisions, BI incompetency centers, scandalous bankers making more money having a shower than the rest of us make in a year, the wonderful Innovation Center and Social Network Analyzer, and more problems with bad data.

April’s posts covered more news on the Social Network Analyzer, the importance of taking account risk in business intelligence, and how BusinessObjects users think they know everything.

May included a trip to Australia with presentations on why BI projects fail and what to do about it, BI competency centers, and transforming BI; the launch of Wolfram Alpha and the inevitable problem of semantics, the launch of BusinessObjects Explorer and a not-for-profit analytics example.

June saw posts on everything you ever wanted to know about Explorer, BI market shares, Gartner and collaborative decision-making, dispenser analytics, the end of LucidEra, and the start of Recovery Act analytics.

July saw some more great examples of government BI transparency, a fun opportunity to test your decision-making skills, predictions about the end of relational databases and augmented BI reality.

August saw presentations in Belgium and South Africa, the launch of SAP’s sustainability dashboards, and a discussion of whether anybody cares about BI market share.

September’s posts included the first BI Fails and BI Briefs, including my opinions on BI futures, why it’s important to have more than one KPI, and the most annoying business words of the year.

October’s posts included how to incorporate live twitter into PowerPoint using Xcelsius, the problems of manual analysis and justice, and highlights of SAP TechEd in Vienna.

November’s posts included how to use Twitter to surf through traffic, the death of OLAP, and tracking baby’s KPIs

December saw Explorer for the iphone, the release of a new version of Xcelsius, some Season’s Greetings showing off the new features, and a great roundup of posts for the year.

Thank you for reading, and I look forward to 2010!

Enterprise social network analysis is clearly an essential part of getting a real return on investment on Web 2.0 technologies within organizations — something that has so far proved difficult.

Today, the “way organizations work” typically has little to do with the official department structures. Organizations are becoming increasingly networked and collaborative, with flat hierarchies, multiple reporting relationships, and a focus on cross-functional initiatives.

Tools like the social network analyzer can help illuminate the “real” organizational structures that are driving the business — and this is essential if we’re to deliver on the promise of translating high-level strategy into low-level execution through aligned objectives.

My company, SAP, has been undergoing extensive reorganization over the last few years — partly because it’s simply the nature of the technology business, but also because of the large BusinessObjects acquistion, and as a reaction to the recent economic downturn.

We have implemented the social network analyzer prototype within our own organization, and users have reported that it’s an extremely valuable tool for discovering links with co-workers. If you find yourself on a call with someone you haven’t yet met, you can not only look up their traditional employee profile, but also find out what other links exist between you, such as the common contacts you both work with. 

As with any new technology, getting the full benefits will require changes to way companies work. In particular, there will have to clear ownership of “network relationshp management” within organizations: ensuring that all the right people in the right teams are working together as they should.

This is perhaps a great opportunity for the human resources role to be able to really show the value of people and their interactions to the overall functioning of the company.

Enterprise social network analytics is a step towards the future of “business user applications”, and we should expect to see variants of this type of functionality embedded in almost every corporate application.

When I was growing up, my father always insisted that “business was all about relationships” (I didn’t pay much attention, sadly). But relationships can be complicated, so if you want to have a head start figuring out how your company works, download the prototype and start giving us feedback on how to make it better!

(OK, so I know puns are the lowest form of wit… apologies)

Thanks to Michael Thompson for the (real) customer quote!

All those financial wizards were making a fortune based on the money they were “creating” — money that has since turned out to be largely fictitious. Top hedge fund managers were earning more in the time it took for an average New York City cab ride (8 minutes) than the average American made in a year.

As I said with a cartoon at the time: “who’s being taken for a ride?!”

Top hedge fund managers made over $657M in 2006. You can use this handy calculator to work out how long it took them to make more than you did all year — the time to brush their teeth? Take a shower? Commute?

(model below — you will probably have to connect directly to the site to see it)

Unbelievably, the financial industry apparently STILL thinks that million-dollar bonuses are perfectly reasonable for work they do (for example, it’s argued that it’s needed to retain the “best and brightest” at AIG).

Benchmarking is a great practice — but you have to know when to update your benchmarks!

Sources: Pay statistics, time usage stats, NY cab ride, commute, TV watching. Model built using Crystal Xcelsius.

Today, most corporate decision-making happens in meetings and conference calls, with little support from technology. As a process, it functions, but most of us would be hard-pressed to call it optimized.

in many ways, corporate decision-making resembles the manual, time-consuming operational processes that have been replaced by automated, connected business applications.  Isn’t it time that we applied some of the lessons we’ve learned while implementing ERP to the executive boardroom?

The payoff is huge: organizations make thousands of decisions every day. A little better data, and a little better analysis behind every decision can make a big difference.

Just as most technology discussions don’t talk about the decision-making process, most discussions about decision-making don’t mention technology. Here’s a case in point: SAP’s Don Bulmer recently posted on the subject of Leadership: Making the Tough Decisions. It was a great article, but we could also talk about the technology behind the principles he laid out.

So here’s Don’s three basic steps to making tough decisions, followed by my comments on how technology could – and should – help more in the future.

Understand what is the core issue, and separate facts from opinions

First, it is important to understand the ‘core issue’ associated with the business challenge that you are dealing with and gather as many facts and insights surrounding the issue as possible. At this stage in the process, it is important to separate ‘facts’ from‘opinions.’

As a leader, you are often presented with ‘facts’ that are presented to support a specific argument or point of view – which by default make them adverse. This is not to say that these adverse or ‘position-based’ facts are not valid, but they seldom represent a fully informed view of the core issue that needs to be addressed.

Any decision you make is only as good as the information you base it on. Accurate, transparent decision-making requires a strategic approach to information across the organization. If everybody comes to the meeting with their own spreadsheets and numbers defending their point of view, you’ll end up spending more time arguing about the data than about the decision itself. A “single view of the truth” across the organization, with consistent, reliable information, makes it much easier to separate fact from opinion.

Look at the decision from all angles and perspectives

Second, it is important to look at your decision options from all angles and perspectives. Many tough business decisions will have a broad effect on your business which might include: brand; reputation; relationships with employees, customers, partners, governments, etc. It is very common to overlook the total impact that a tough decision will have on your business. This is why it is important to consider many options – as you weigh the impact and trade-offs that most important decisions will require.

As a leader, this is where intuition and experience play a big role.

No computer can help you figure out all the angles of a decision, but technology can help. For example, profitability and costing analysis can help ensure that everybody involved in a decision understands the real drivers of the business: what is value-added activity, and what is not? What should you be spending time on, and what is less important? And predictive analysis can help determine what factors have historically had the most influence on the numbers at hand, and determine what might happen in the future.

Ask lots of questions of lots of people

Third, it is important to ask a lot of questions. This will help you to understand the options on the table as you make sense of the facts and sift through potential agendas/motives of people involved. Asking the right questions also helps you to measure the risk and impact that your decision will have on your business and with key relationships.

You may even consider speaking with trusted representatives from affected internal or external groups (customers, employees, partners,etc.) to get their perspective. This interaction will also help secure support and buy-in (by all parties) of your final decision once you are ready to communicate.

The ‘tougher’ the decision the more challenges you can expect to face as you communicate and implement. You will need to be prepared to help affected parties understand the rationale behind the decision and even share some of the alternatives considered and why you chose a different path.

This is the key area that technology is poised to help more with in the future. Today, there’s relatively little support within Business Intelligence industry for the process of gathering and sifting information from different people involved in decisions. Interesting technology exists today, but is not yet integrated into a whole solution. For example:

• Antivia has done some very interesting work in the area of “BI 2.0” and letting people collaborate around reports and information within a BI platform
• Panonica had some interesting collaboration software, with tight links to BI, back in the early 2000s, but was clearly too ahead of its time, and has since been discontinued
• ExpertChoice and others have supported collaborative decision-making, but without the BI angle.

The next generation of Business Intelligence will combine the best of these different technology approaches to better support decision-making, not just information-gathering. For more information about how technology can help with decision-making, see The 5 Ingredients of Good Decision-Making, based on some research done by the Economist Intelligence Unit.

Other posts related to decisions:

• Bad Decisions: Just Blame Evolution?
• More Milk Please, Ermintrude! A Classic Decision Trap?
• Is There Such a Thing as Good Gut Decisions?
• Will Computers Ever Help With Decisions? (No)
• Intestine-Based Decision-Making