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Moving Up the Analytics Maturity Ladder                                  81


             Implications

             So why does all this matter? Three reasons. First, the real near‐term work for CAOs involves
             recognizing where the company/division you serve falls on the maturity curve and building
             a team suited to work at that level. For example, you will not have done your job if you build
             a team with great depth in AI when the company is struggling to establish basic control.
             Your data scientists will grow frustrated and leave for greener pastures, and the company
             will continue to struggle. There is a hard lesson here – everyone wants to work on the latest
             and greatest. But you have to match the focus of your team to the organization’s maturity,
             not the other way around.
               Second, the midterm and the work there is even tougher. You have to move your company
             up a level to derive greater insights from numbers (Kenett 2008, 2017). This is no easy task
             and you may fail. But take heart and ask yourself who is better qualified to lead such an effort
             than you. Indeed, we would argue that data scientists are uniquely qualified in this regard. For
             example, early in this chapter, we opined that there is no organized data to analyze when the
             company is in firefighting mode. This is not quite true – the simple observation about how
             many fires there are to fight can be revealing. Of course, you will need to fully integrate yourself
             into the business, including all of its politics, to find this out. So what? Everything important
             is political. Get on with it.
               Finally, there is data quality. Most companies, even level five manufacturers, are at the
             firefighting or inspection levels when it comes to data quality. Those who’ve gotten to the
             process level enjoy far better data at far lower costs. Look here for opportunity. Ultimately,
             the goal is to reach the learning and discovery maturity level, where data science and data
             scientists reach their full potential.
               Thus, the real work of CAOs involves establishing a team suited for the organization’s
             current level of maturity in the short term and leading efforts to move up the maturity ladder
             in the longer term.
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