- Here’s how smartphones, tablets and huge databases will upend market research http://t.co/s0SOPpbWJv via @gigaom ->
- Welcome to the golden age of enterprise IT – and get used to it: It’ll be here for a while http://t.co/sUak2zbVpI via @gigaom ->
- When a defense contractor gets hacked repeatedly, you know cybersecurity is a problem http://t.co/4lzLuxTuvT via @gigaom ->
- Myth: Eric Brewer on Why Banks are BASE Not ACID – Availability Is Revenue http://t.co/xFPitSpjmb ->
- Taylor Wilson: My radical plan for small nuclear fission reactors http://t.co/pLFblduRCW #TED ->
- How Ray Kurzweil Will Help Google Make the Ultimate AI Brain | Wired Business | http://t.co/gEB50KPNx6 http://t.co/XcyqCyfL7l ->
- As traditional PC sales and chipmakers slump, ARM rakes in the cash http://t.co/hfUnCBvac6 ->
- DARPA tools for designing vehicles. http://t.co/hyEiDIRPm8 ->
- Tankcraft: Building a DARPA tank online for fun and profit http://t.co/kq0OU7eWA2 ->
- Nathan Myhrvold: Could this laser zap malaria? http://t.co/6m6HS2m8mw #TED ->
- MLbase: A User-friendly System for Distributed Machine learning https://t.co/YYEDavSyvm ->
- Return of the Borg: How Twitter Rebuilt Google’s Secret Weapon | Wired Enterprise | http://t.co/gEB50KPNx6 http://t.co/vlpFUOfWML ->
We don’t know why the internal numbers of the Romney campaign were so far off, but I don’t think there’s an excuse. The fact is that Nate Silver, Princeton and others used models that once again predicted states correctly (Nate scored 100%), plus Senate and House races too. Reality was not only available, it was free.
This is one of the human factors of analytics: reality is not always welcome. You can beg that management not shoot the messenger, but practice ducking anyway.
I know lame BI when I see it. BI is lame when it show KPIs without insight into why they’re failing or succeeding. BI is lame when it shows sales figures without enough context to judge performance. BI is lame when it shows the same Top 10 Customers every day when it should show the Top 10 Customer Calls You Need To Make ASAP.
But I’ve never stopped and considered what separates lame BI from quality BI. Curt Monash, in his excellent blog, DBMS2, identifies 2 axes of Business Intelligence: operational and root cause. Into the quadrants he fit the descriptive names that belong there. That gives a matrix like so:
What this also shows is that lame BI has little to do with the product used. Real-time means “in time to make a difference”–Curt Monash uses the phrase “human real-time”–but most businesses can measure that in minutes or hours: long enough for BI to play a pivotal role. Some products do give an advantage. QlikView is excellent for investigative BI and its rapid development model makes it easy to prototype, test for value and deploy a working BI tool.
Lame BI is a product of something else. Politics leads to lame BI. Expose a KPI in a dashboard and if it goes the wrong direction someone gets blamed. Building the real-time or tactical BI to address the issue is someone else’s problem. Somehow this passes for “managing on results.”
Wishful thinking or inexperience is another cause of lame BI. People believe that having a dashboard at their fingertips, whenever, wherever, will improve management. This promise is what continues to sell lame BI. Hopefully they don’t run out of money or lose faith before getting the BI they really need.
Data quality is a major cause of lame BI. KPI’s are looking where the light is. To look at root cause in a tactical app requires that you have captured the cause. Real-time alerting requires that some actionable fact be available on a shorter timeframe than the KPI is measured.
BI is a primary interface to business data. It is often the only interface to data spanning business departments. Therefore, BI is the last stop and the most important place to add context, gain insight and influence business decisions for better or worse. Lame BI is wasting money, time and opportunity. Don’t let another lame BI tool get built in your company.
Validation from IBM. Combine BPM, social communication and mobile interfaces. Ok, now just add 2-way business intelligence and some BigMetaData concepts for a revolutionary product.
Great company, great examples.
eBay makes a quick decision with bottom line results using simple surveys.
“This used to be a job for outside specialists. Now basically anyone inside Thomson Reuters can do this,” says Nicole Gagnon, a senior director of market research.