|image courtesy: Mediabistro.com
So much at stake, I feel, every time I do a project.
It takes lot of experience and training to be a great analyst. Convincing business managers is such an important task in executing analytical projects.
You should thank yourselves working with such people because they are the one who brings sense into all the complex modeling you might do. The common denominator is that every one is a smart person and can figure out whether some thing makes sense or not.
However, it requires a great set of tool box, practical wisdom to interpret human behavior, and focus on the requirements.
In the end, this discussion is all about the value of analytics not really about the analyst’s mistakes, though it comes through the analysts.
Here is a simple example: The response analysis lift chart looked flat, when the results came from the campaign. Everyone was getting anxious and wriggling the palms, because a lot of brand image and the customer value is dependent on the promise of the success of the model and the pilot campaign that the client was willing to invest in.
One needs to figure out looking at the detailed levels of the data to see what was going on and looking at the result in the right way showed that the results were dot on. The point is you can not look at the data in the run of the mill text approach because in that situation it will not bring out the results for correct interpretation. You might end up working in all kinds of interesting new data situations and the consumer dynamics and market dynamics might not have been captured well in the model and yet the analytical methods will yield a way to analyze the data to get the right interpretation or differential story.
The raising importance of analytics and every directional time and money invested in analytics brings out an important question.
Many stories of my past experience nagged me, ok: what could be total life time opportunity value of a failed analytics project? What happens if couple of them happen before the analyst gets fired.
I felt it is mostly attributable to the analyst. Some times, the analyst has to be maniacally following the reasoning to protect not only one self but also the organization.
So i recast the question, what is the cost of an incompetent analyst to an organization? Because most of the damage will be done in the early stages of the employment of an analyst, the estimate I am going to quote is relevant only for the first three years of employment and in a way censored list of mistakes, not to be influenced by all the mistakes one might commit in the long tail tenure distribution of the analyst to be relevant for the hiring organization.
This is important because there are many innocent looking strategic judgement/activity errors and directional mistakes that would be led by the input or the works of an analyst. By the time the manager comes to realize the mistake the time would have passed and the loss would be sculpted in the brand value of the analyst, the owning department, and the organization.
The loss due to such analysts points out to more than $500,000, above and beyond the net labor cost of retaining the analyst for three years. The estimate is much higher if it is a consulting company and the total value of the relationship.
So how can companies protect against this invisible-looking loss?
– Make sure the analysts belong to a best of breed; not even Ph.D is enough to assure the breed quality. It is really the overall personal attitude to life, work ethics, team behavior, interest in continuous learning, and ability to focus on work needs, and the ability to connect the data intelligence with human behavior intelligence – no wonder one can say that these things automatically happens if you have passion in your field; in this case it is all about data sciences.
– During the interview time, ask questions that would help you understand on the following
– What kind of invisible strategic errors one is likely to make, how to spot them, and how to redress the expected event so that expected event does not happen
– Communicate and understand how strategic invisible errors can happen
– Play the game of estimating what would have happened, had it not for that mistake, during the interview
Now imagine what would be the opportunity lost if a leader has some incompetent dimension that was not well understood and the remaining management does not redress the issue. Tough one.
Ok, once an analyst is hired how do you redress the needs of the analyst to keep strengthening the factors? You need a third party who will be a overseer of such talents and who will personalize the redress process. It is worth it. Even a good analyst will benefit by this and contribute back to the organization in 100s of 1000s of dollars, every year directly to the profits of an organization.
Just some thoughts to point:
– Get a third party to keep certifying decision analytics of analysts
– Get them enrolled in online training programs of renowned universities or institutes, if these are managers who have to create and manage analytics teams
– Hire the right talented people
– Above all, train the analyst and sport a great team attitude for people to have fun together.
Ask them to specialize in an applied area of analytics and measure their progress
I am getting responses from people saying that it can be millions of dollars – of course depending on how big the company is, it can be billions especially if it is a highly strategic insights projects.
Think of the Netflix case, which has struggled in the last 9 months in trying to come out with the right strategic decisions to price the offers to consumers and also the strategic relationship with content providers. (update as of April 2012)
They lost $12 billions market value messing around with difficult choices, not $500k per analyst; this may not be a problem of the analyst; perhaps it was managerial.
PS1: Some one asked how would you estimate more scientifically?
One way to start.
Use a sample of non-competent analysts who ended up getting fired. Use measures such as direct and indirect costs of retaining the analyst and the net profit from the ROI calculations. Audit the various reports that provide data on QA problem, modeling problem, interpretation problem, application problem, client communications, application domain area… regarding the analysts, … and the simulated net profit had it been done with out those problems and the net difference between the simulated net profit vs. net profit estimated on the basis of actual work delivered will give the opportunity lost. Or you can compare between the non-competent ones and the competent ones. Note that the data is going to be censored. I have not even touched the brand value.
One has to make lot of simplifying assumptions, as you can see.
Also, the above points are in relation to fortune opportunity lost from 5000 companies in USA and similar comparable segments around the world in dollar terms.
Well, to conclude and rest this controversial highly subjective piece I found this very interesting and hilarious.
From Data Monster & Insight Monster