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16 Rules that Helped C-Suite Trust Analytics

Guest blog by Manmit Shrimali.

Data science, deep learning, citizen scientists, data lake, big data, AI, machine learning, hadoop, Spark, deep learning....Yes, I get these and breathe them every single day. But let me ask you...Have you been asked any of following questions....

1. As a data scientist, how do you bridge a gap between algorithms and business needs?

2. On one hand, there is no shortage of data but on another hand, organizations are still struggling in terms of producing consistent results. If data science can predict, prescribe, and optimize, why few sectors like retailers are not able to react with the right offer at the right time (although they claim to do so?  

3. If data science is so powerful, why executives still need to be sold? Why there is a culture battle?

4. Why pumping millions of dollars in data lakes and automated software (aiming to replace data science) are producing cookie-cutter insights?

Above are few of the questions I am constantly being asked by few of the fortune companies who have no shortage of data or algorithms. The answer was simple - it is not what you do differently but how you do things differently. It is how you look at the problem, business, and customers. It requires a mindset that challenges the status quo and pushes us - the analytics drivers to come out of our comfort zone. Looking back at all the projects that produced measurable ROI in terms of revenue and margins, I conclude 16 rules that every data scientists should follow. Adhering to these rules, helped me and my team speak the same language as the business users, helped build trust on analytics, and eventually, change the mindset. 

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Comment by Donna Scheck on June 5, 2017 at 5:26am

This is a great list: I have lived nearly every point! However, I think that the greatest challenge to data science is the organizational culture: it's hard for execs focused on quarter-to-quarter results to be patient while data teams try to draw insight from the convoluted, messy, nasty data that they are give to work with. To the group: What methods have worked to deliver a solid, quick "win" when decisions have to be made and you haven't had enough time to deal with all the data issues? How do you foster 'patience' in the organization when it comes to data science solutions?

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