From my experience, I see the industry has some sort of apprehensions..Not sure, what though. Would appreciate industry experts to share light on the topic and suggest how/what should be the approach to reach target prospects? Any reference would be helpful.
I've found talented data scientists in several other countries who do just as good as ones I've worked with in Silicon Valley. It is not a position one can automate away, of course - one needs to know more about the type of data that needs grooming and what types of skills best fit it (DSP vs analytics vs whatever buzz word that relates best to the science at hand). Can you speak more about your goals?
Answered 10 years ago
There is a lot of growth in data analytics, data visualization, data science and machine learning.
The main driver of this is that companies are starting to track a lot of data now that they have the technology available and the ability to gather it. This results in a huge increase in data science work.
One of the main challenges companies face is that they are not sure how great a data science firm is. So I would recommend:
1. Make sure to get reviews from your existing clients and introduce your prospects to past clients to give them a better understanding of your work.
2. Check out Kaggle.com and try completing a few projects there as that will help you gain credibility and access clients
3. Check out VenturePact.com and try signing up there are as getting a third party to vet your firm will help increase your credibility. Also VenturePact will help you connect with clients.
Answered 10 years ago
Not a simple enough topic to start from scratch. I remember studying the topic of artificial intelligence for some time and also did not know where to start. This information portal https://www.aiperspectives.com/ helped me a lot. Just great articles on this topic. Try reading, I'm sure you will learn something new for yourself.
Answered 4 years ago
An engagement model is a framework, which defines collaboration between client and analytics outsourcing partner in terms of the level of control, responsibility, and a base for further development. By bringing together, the expertise from outside an organisation can innovate and go beyond what their current analytics team can offer. This is a simple model that allows the company to extend the existing in-house staff with workers from an outsourcing partner. It reduces cost and time by taking the required technical and domain expertise from the outsourcing vendor. These data science expertise that the client lacks are provided by the outsourcing vendor on a fixed price time-bound activity.
You can read more here: https://analyticsindiamag.com/why-you-should-outsource-analytics-in-2020/
Besides if you do have any questions give me a call: https://clarity.fm/joy-brotonath
Answered 4 years ago
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