Friday Find is my collection of interesting articles that I felt worth sharing from the week. An increasing amount of my personal research focus lately has been around Data and how companies are using data to enable change as well as the cultural change needed within a company to support this data evolution. This is an natural movement up the stack from the IoT / Connected Device infrastructure work I have been doing for the past 3 years.
As a company grows from small to large and evolves, there are, in fact, no more than two things it must do:
1. Upgrade its algorithms to process data
2. Get more data
Li Guofei’s article A Total Rethinking of Tencent’s Strategy
Translated in Jeff Ding’s ChinAI Newsletter
This quote comes from an interest article out of China (via Jeff Ding’s ChinAI Newsletter). I find Jeff’s newsletter and this article interesting as part of my investing focus on Chinese companies. But the article explains clearly a number of general aspects around data, like the impact that information overload has:
In the era of information overload, the search efficiency of “people in search of information” continues to decline, and the distribution method of “information in search of people” has become more popular. Good algorithms can improve the precision of content distribution and increase user stickiness, thus significantly improving the ability of the advertisement to be converted (into sales/cash).
Li Guofei’s article A Total Rethinking of Tencent’s Strategy
Translated in Jeff Ding’s ChinAI Newsletter
We all encounter this daily in our lives, unless you don’t use Facebook, Google Search, or a growling list of free services; remember, if you’re not paying for the product…you (or your data) are the product.
But the flip side of this is how to effectively use data for maximum [cost effective] results in our businesses. For larger enterprises, this is an evolution of thinking and operating that can’t be driven from the top down but enabled and supported from the top with bottom up adoption. Something that a recent McKinsey article on Why Data Culture Matters covered.
The experience of these leaders, and our own, suggests that you can’t import data culture and you can’t impose it. Most of all, you can’t segregate it. You develop a data culture by moving beyond specialists and skunkworks, with the goal of achieving deep business engagement, creating employee pull, and cultivating a sense of purpose, so that data can support your operations instead of the other way around.
Why Data Culture Matters
McKinsey Quarterly
I saw this first hand at one of my clients where I helped them implement a big data as a service offering on their private cloud (I was representing the private cloud vendor). There was a general consensus from leadership that big data was key to the company, a research project was lead by IT to understand who was doing that today down at the BU level, that feed into spinning up a private cloud service to make it easier and cheaper for the current BU’s doing big data, with the longer term goal being that others could learn from these examples and quickly and cheaply experiment with big data in their own BUs. Unfortunately, I stopped working with that client before I could see if that longer term vision was ever realized (when I left, they were not investing in the enablement infrastructure for that vision).
What has been your exposure to big data usage and strategy within your organization? Leave a comment on how what you’re seeing/experiencing relates to the above aspects of big data and data culture.
