This is a review of a chapter from ""Competing on Analytics"-
Analytical competitor is an organization that uses analytics extensively and systematically to outthink and out-execute the competition. 
  1. Analytics supported a strategic, distinctive capability. Exploitation of existing measurements and exploration of new measurements. 
  2. The approach to and management of analytics was enterprise-wide. One version of the truth.
  3. Senior management was committed to the use of analytics. "In God we trust; all other bring data"
  4. The company made a significant bet on analytics based competition.

My Thoughts: Difficult to create a culture of analytics, when it is risky proposition to invest and spend time and resources. One issue I have seen is a prior attempt and investment in analytics that failed. But, with new technology that makes it cheaper and increases the likelihood of success- the organization could have made a move to improve, but felt married and committed to the investment in the older, more difficult technology. 

5 Stages of Analytical Competition
  1. Analytically impaired. Objective: Get accurate data to improve operations.
  2. Localized analytics. Objective: Use analytics to improve one or more functional activities.
  3. Analytical aspirations. Objective: Use analytics to improve a distinctive capability.
  4. Analytical companies. Objective: Build broad analytic capability- analytics for differentiation.
  5. Analytical competitors. Objective: Analytical master- fully competing on analytics.

My Thoughts: It is important in an organization that each stage be a successful experience in analytics. There are a lot of moving parts- and a lot of complexity that can go wrong- and interesting analytics can be a difficult process. Making sure you select the right: software, hardware, technical people who can deliver the data, technical people to install software and hardware, analytical people who can analyze the data, and managers who can allow the time spent on all of the above activities. This is not all, then you need to believe and act on the data, and implement the findings. Test the results and review the impact and then improve the analytics. I suppose this is why the first 4 attributes are important- because many things can go wrong. 

Although, I would add a thought here and a plug for Bern Medical. Many of these issues are what has helped lead us to where we are with Bern Medical, our solution, our business model and distribution model. It has molded us, so that we can take away a lot of these risks to practices and billing companies.


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