Successful business intelligence has always been about negotiating the right tradeoffs between the needs of individual business people and the needs of the organization as a whole. In 2001, Bernard Liautaud, founder of self-service BI pioneer BusinessObjects wrote a book called eBusiness Intelligence: Turning Information Into Knowledge Into Profit that discussed these tradeoffs. He drew the analogy with systems…
Our brains are made up of special cells called nerve cells. A nerve cell has a bulb-like cell body attached to a long nerve fiber, which is like a wire, and it actually carries very tiny electrical signals. These signals are the information that tell us what we feel, what we think, what we see, what we touch. They also tell our bodies how to move and how to function. All this information is carried by nerve fibers to and from our brains. There is so much activity going on as we process, think, reflect and analyze information. Everything we see, smell, hear, taste and feel is collected and processed to navigate us through our lives. Our world is a virtual database that feeds our brain and is analyzed by our very own intelligence.
Analysts today have a vested interest in the quality of their data. This would include where it comes from, its historical completeness and information clarity. In some organizations, blaming IT is no longer acceptable when the analyst is unable to do her job. Many analysts are involved in setting the precedents for the future of big data initiatives due to their responsibility to support the decision-making process.
Did you know:
Only 12% of organizations are satisfied with the software they use to create and apply analytics,
71% of them use spreadsheets for analytics, a higher percentage than any other tool,
and 67% said spreadsheets cause problems in their use of analytics.
Once again we find the pervasive use of spreadsheets to perform functions they were never meant to do.
Let’s apply the x-ray vision concept to data analysis. Possessing the ability to see through the layers of data would be amazing! Imagine having “Green” colored glasses that allows the user to quickly see through data. Yes that is it, Business Intelligence “X-Ray” vision glasses! Today this technology is not available yet, (although Google Glasses is getting pretty close), but the amazing visualization does exist which allows the end-user quick access to pertinent data.
Anyone who knows what this photo depicts? Hint: it’s the father of self-service….
What do you find people misunderstand about self-service BI’s concept?
That it’s a very hard thing to define concretely in terms of technology implementation. The underlying business need is a very broad concept that covers a very wide range of different types of technologies and information uses, and that the distinctions between “reports,” “dashboards”, “data discovery,” etc are blurry — and the need for “business information” covers a lot more than what is stored in traditional databases (documents, external news feeds, etc.)
There’s no truly self-service BI solution.
GigaOM Research recently published an interview with database rock star Michael Stonebraker on “the impending battle of the database elephants,” covering his thoughts on the disruption in the database market.
This blog includes the excerpts I thought were most interesting:
Winning is the only measurement that counts most in sports, but what if your team does not win the championship? That is a guaranteed outcome for all but one team in each league every season.
Some teams suffer from decade-long droughts. In the absence of a championship, fan experience is the ultimate measure of success.
No magic prescription can prevent all losses or disappointments, but the basic formula to deliver a world-class fan experience almost always starts with decisions born out of data.
Here’s the real business benefit of in-memory processing.
If you are an analytics professional, then cloud analytics is in your future — if you’re not already doing it.
I’ve talked to lots of companies that claim that cloud analytics isn’t for them. But they’re wrong, not least because their marketing department is almost certainly already using some form of cloud analytics today (to track ads or social sentiment).
What will tip people towards using more cloud analytics?