Selling Data Mining to Management

sellingDMPreparing data and building data mining models are two very well documented steps of analytics projects. However, whatever interesting your results are, they are useless if no action is taken. Thus, the step from analytics to action is a crucial one in any analytics project. Imagine you have the best data and found the best model of all time. You need to industrialize the data mining solution to make your company benefits from them. Often, you will first need to sell your project to the management.

I recently read three interesting articles on this topic. The first one, Selling Information Governance to Business Leaders, by Sunil Soares, gives four tips explaining the value of analytics to business leaders. The first tip, focusing on business outcome, is extremely important. You may have a perfect data mining application, predicting your target with 99% of accuracy. If you can’t transform this 99% accuracy into a usable and industrialized solution for the company, the project will not bring any ROI for the company.

The second paper, Selling a Data Mining Project to Management is written by Casey Klimasauskas. It proposes an approach for selling a new data mining project to management. Part of the strategy deals with developing relationships with the stakeholders from the very beginning of the project. This minimizes the risk of future objections and increase the supporters of the project.

Finally, What the C-suite should know about analytics, is an interesting article written by Kishore S. Swaminathan. Found in the sascom magazine (but originally from Accenture), the article lists five areas to focus on in order to bring analytics to the management. The advices given in this article are very relevant. I particularly liked the 10 characteristics of an analytic leader. For more information, I provided the links to these three articles below.

Selling Information Governance to Business Leaders

Selling a Data Mining Project to Management

What the C-suite should know about analytics


Recommended Reading

Comments Icon5 comments found on “Selling Data Mining to Management

  1. Hi Sandro,
    At the beginning of my career as data miner I thought that the problem in convincing managers to finance “data mining projects” was related to new technologies and methodologies, and as you know, the “new” things scare the managers!.
    Now (after years spent dealing with managers) I understood that what really scares the managers is related to the intrinsic uncertainty and fuzziness of these kind of project that doesn’t help managers in determine the ROI.
    So, in my opinion, the best way to obtain money for such projects is to provide them a prototype model of the solution.
    I usually build a model with tools and languages “data-oriented” that allow the analysis of the expected results of the solution in production environment.
    Of course a solution for a problem is not enough to convince your managers! The solution should be always provided with accurate estimation of the regions where your solution works, and also an estimation of the limits of your solution, that is, where your solution will fail.
    A manager is reluctant in financing such project because of the uncertainty: the prototypes, the risk analysis help him to reduce the degree of uncertainty.
    …The manager is happy when everything is under control: reduce his uncertainty and you get the money 🙂

  2. Thanks for sharing your experience with management. The prototype or proof of concept (PoC) is definitely a must-have for selling a data mining project to management. One have to pay attention that from a PoC until industrialization of a project, there is a big gap. This is even more the case in data mining compared to other fields (such as BI for example).

  3. Hi Sandro,
    I’m with you: the complexity of data mining project lays in the gap between prototype and industrialized solution.
    In this point of the workflow the data miner should be much more focused, and ask to himself:
    –> Does the solution reflect the real scenario in production? (test of significance)
    –> what happens if the the domain in production is not static?
    –> what are the conditions where my solution will fail? (FMEA model)
    –> How much robust and scalable is my solution? (slight modification of the original scenario could require a completely different solution… and managers hate this!).

  4. Nice site you have here, most of the information is pretty accurate. I’m going to send your link to some of my friends if that’s okay with you.Thanks for sharing…

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