The 'gut factor' and taking decisions by gut, at different levels, was there and is in vogue and will continue to be there as along as human beings and this planet are going to coexist ! 
 
 
 
The point that needs to be minutely noted is that the most effective and efficient CEOs are supposed to be good marketers as well. While it appears that marketers operate from the gut( may be because of their proximity with customers/consumers and their 'feeling' the pulse of the market/people ) and the CEOs heavily lean on use of data is incorrect and unfounded. 
 
 
 
The gut is an 'inherent factor' in individuals which gets fine tuned by proper inputs and exposure, over a period of time. 
 
 
 
It is anybody's take that those who have better inputs - MIS, RIS, requiste related data, feel of the pulse of clients/customers and proper data and analytics - are invariably better placed; and they go by their 'inherent gut' due to the polishing of the 'gut factor quotient'. It is not that it 'just happens'; it happens by solid intution and this kind of a thing is an ongoing process for people who are open minded and receptive. 
 
 
 
If you go by Steve Bennet and the research from Accenture, every big corporate entity will need an 'inhouse Cequity', which is an impractical proposition for many corporate houses for many understandable reasons ! 
 
 
 
>> Can you say hell with analytics ? You need guts to say that but, in the days to come ( it has already started happening !) you need data and analytics and more data and more precise analytics to take decision on 'guts' !!  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

I got your point. Delay caused can be hazardous for any business while making decisions. But there is a great scope to embed tools and softwares based on day to day business analytics need. Many times it happens that people who claim as expert in analytics from IT don't have the right mix of business and analytics knowledge. How about adding analytics skills within Business analyst and middle management community? I strongly believe in Analytics, it does solve unknown ambiguity.

In fact, these were the type of reactions that we were waiting to hear from database users.  
 
As one who has been involved with database creation and management since 1993, I was wondering all the time why database users are not raising their voice against the limitations imposed by the current architecture of RDBMS usage.  
 
The way in which data is stored inside conventional RDBMS architecture imposes severe limitations on how well and easily one can make use of large volumes of complex data  
 
It was in 1993, I conceived and got implemented India' s first online business information database codenamed BISNET, which was inaugurated by the then Prime Minister Narasimha Rao on September 28, that year.  
 
While its nation wide usage was hampered by the delay in the implementation and the final scrapping of the INET project of the Deptt. of Telecom (it was supposed to be India's first country wide data network), there was another database problem that became a major cause for concern.  
 
After reaching a level of about 500,000 records, the database performance began to fall exponentially. We were forced to go for advanced hardware. It is then we realized that with the growth in the volume and complexity of the database, there was a need for more than proportionate augmentation of the processor capacity. The biggest pain was in running complex queries for extracting useful knowledge out of the database.  
 
These are issues that set me on the hunt for an alternative architecture. Since 1997, I have been running a self funded R&D project that has yielded handsome dividends. We have implemented our technology in commercial projects and proven that all the pains and perils of using traditional database management systems can be easily overcome by using an alternative approach  
 
The innovativeness of our approach is not only in the way in which every single data can be analysed in its holistic context but also in the use of single standardised procedures for each analysis. Users need not look for help either for writing complicated queries or generating custom reports. These reports can be more incisive than what OLAP other BI systems can produce.  
 
1.Currently, our data output can have up to six by six matrix of row and column sub divisions. This means that data cells can be presented with each each column subdivided in to six levels of hierarchical relationships like group, company, unit, division, team and employee each row in to six levels of hierarchies such as year, month, week, day, hour and minute.  
 
2.The matrix formation is dynamic. The divisions within columns and rows at all levels are generated depending upon the data available. The number of subdivisions at any level will be automatically adjusted to the exact number of divisions required. For example, if one regulation has five sub regulations while another has only two, the first column will have three sub divisions while the next one would have only two. If some of these sub regulations had clauses and sub clauses while some others do not have then there will be subdivisions appearing only under the sub regulations that have clauses and sub clauses The output matrix has also two options. First a full blown matrix wherein all the subdivisions of rows and columns will be displayed irrespective of whether data exists for the cells appearing at each matrix intersection. In the second option only those column and row sub divisions, where data exists at intersection cell will be displayed. No empty columns or rows appear or cells appear in the output.  
 
So there are now alternatives that can help business managers better. But then IT departments are too entrenched by traditional managers who would not venture out to look for out of the box solutions

I think it is also important to state that reporting alone should not be considered analytics. I tend to say that reporting is just the first (and important) step in analytic discovery. What is does is forces the organisation to ask larger business questions that establish solid base for analytic project development (big or small). I completely agree with your point on "embeding analytics into day-to-day business process". I would go even further - key is to embed the measurement to that process first.