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Predictive Analytics: When to use or not to use a consultant?

There are two circumstances that you should use a consultant:

1. When the consultant has both the domain knowledge and exact modeling experience: There are times that the consultant come to you and sell you the ideas of modeling something. Look for exact experience. Like an academic researchers, the most challenging task is to get the data-set, not the idea. Dataset is the execution. In the world of Big Data analytics - whoever owns the data has the command, especially in the commerical world. Data is like Gold in the new world.

2. You are not trusted with your ideas but you have lots of budget to spear: If the consultant has the respect and that is the only way to move forward, then ask for the budget and push for the project. Even though the project could fail if the consultant does not meet 1. above. You can always shoot down the consultant and do it again in the second iteration. At least you know where the attacks are within the organization and know how to avoid it.

When you should not:

1. You do not know how to model: Learn it dude! There is no short-cut to learning. Your organization needs to learn it, even yourself. Leverage every single person of your organization that has any glimps of experiences in dealing with the data. Combine that quant dude with a domain expert, let them fight and muddle through the journey. The organization needs it. So does you to learn how it bring value exactly to different internal clients.

2. You wish to kick-start the practice: There are lots of internal lobbying. If you fail to do it, you will not get the budget required or acceptance even if you have the consultant.

:-) Wish you luck in next year. If you are those frustrated dude that find that you are only talking about Big Data, not actually doing it. It takes time. At least it takes me more than 2 years.

From someone who failed and failed and finally moving forward.

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Comment by Jeffrey Ng on January 13, 2015 at 9:56pm

Hi Ammanuel, I have further thought about it, perhaps it may help:

Comment by Jeffrey Ng on January 6, 2015 at 5:04am

For internal lobbying - can try to find some existing works that can be enhanced by predictive Analytics - less time, less human intervention. It can be a project that no one want to do it but can be easier delivered if using machine; or a work that are time consuming but important. Machine is good at solving those problems too.

Always pitching for ideas - I pitched approx 25 ideas and got 2 in. But that's how I started it.

Comment by Emmanuel on January 4, 2015 at 7:03pm

I believe I am stuck somewhere in between  starting and learning. I have kick started the personal journey into Predictive Analytics and I am less than a year into that journey but still no major project in my bag.

I can appreciate the justification to have a Predictive Analytic project started, Thanks for your blog as a medium in sharing your journey as a form of inspiration to aspiring practitioners like me. I really am that frustrated dude who really is doing more thinking, learning and talking than actually getting stuff done.

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