- Transforming data into graphics to gain buy-in
- DAVENPORT - Enterprise Analytics
- Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
- More Books by Thomas H. Davenport
- How companies are using big data and analytics | McKinsey
In part, and to do that well, you have to have good training programs, you have to have very specific forms of interaction with the senior team. And you also have to be a part of the organization that actually drives the strategy for the company.
Transforming data into graphics to gain buy-in
Murli Buluswar: I have found that focusing on the fundamentals of why science was created, what our aspirations are, and how being part of this team will shape the professional evolution of the team members has been pretty profound in attracting the caliber of talent that we care about. And then, of course, comes the even harder part of living that promise on a day-in, day-out basis.
Yes, money is important. Because no matter where you are, most people—especially people in the data-science function—have the ability to get a 20 to 30 percent increase in their compensation, should they choose to make a move. My intent is not to try and reduce that gap. Focusing on that, to me, is an absolutely critical enabler to attracting the caliber of talent that I need and, for that matter, anyone else would need.
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Victor Nilson: Talent is everything, right? Talent is the differentiator. The right talent will go find the right technologies; the right talent will go solve the problems out there. We have the legacy advanced techniques from the labs, we have the emerging Silicon Valley. But we also have mainstream talent across the country, where we have very advanced engineers, we have managers of all levels, and we want to develop their talent even further.
DAVENPORT - Enterprise Analytics
It might be just a one-week boot camp, or it might be advanced, PhD-level data science. But we want to continue to develop that talent for those who have the aptitude and interest in it. We want to make sure that they can develop their skills and then tie that together with the tools to maximize their productivity. Zoher Karu: Talent is critical along any data and analytics journey.
And analytics talent by itself is no longer sufficient, in my opinion. We cannot have people with singular skills. And the way I build out my organization is I look for people with a major and a minor. You can major in analytics, but you can minor in marketing strategy. Otherwise, the pure data scientist will not be able to talk to the database administrator, who will not be able to talk to the market-research person, who which will not be able to talk to the email-channel owner, for example.
You need to make sound business decisions, based on analytics, that can scale.
Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
He mentions a few models such as attrition and response modeling. I liked how imaginative Franks take is for emphasizing the customer segments that businesses can develop, such as this comment:. Consider a segment called Dreamers that has been derived purely from browsing behavior. Dreamers repeatedly put an item in their baskets but then abandon them.
More Books by Thomas H. Davenport
Dreamers often add and abandon the same item many times…So what can you do after finding them? One option is to look at what the customers are abandoning. Another solid segment is Chapter 12 Engaging Analytical Talent. It gives a brief overview of how to set assignment objectives that shows your organization understands analytical talent:. Arming analysts with crucial information about the business is one way to keep analytics talent engaged. Privacy issues are noted in Chapter 4, but advocates should read Chapter 13, Governance for Analytics.
How companies are using big data and analytics | McKinsey
Stacy Blanchard and Robert Morson lay out the process for establishing analytic management, the processes that ultimately protect data as much as it extracts value:. Establishing governance is a mix of science and art, where the specific power dynamics within the organization play a significant role. There is no single right governance model for analytics, but a number of good principles and practices are commonly found among the organization with high-performing analytical capabilities.
Concepts, while meant for large organizations, can still fit a medium sized business, such as guiding principles and understanding why governance is important. Later chapters present cases of large enterprises. A few note the impact of analytics on specific industries, such as retail Sears and pharmaceutical Merck.
Again, this is a book meant for managers of large organizations. But for small businesses looking to grow, it can give an overview that encourages a deeper appreciation for detailed books like Web Analytics 2. Analytics, in general, forces a business to look critically at how it operates. Books like this one will provide the right framework for managing those operations for your best business performance.