leveraging data as a strategic asset part 2

Data is the new oil! But truly data-driven cultures require an overarching data culture that encompasses a few fundamental elements, such as high-quality data, broad access and data literacy as well as appropriate data-driven decision-making processes. This is the second part of this note posted a few weeks ago.
 
Leveraging Data as a Strategic Asset (Part 2)
 

• Data literacy: It’s also super important to ensure basic data literacy at every level in the organization – best achieved via data science training - data is not there to bolster (or undermine) existing decisions, but to help inform future ones. 
• A good first step is to enhance basic skills in descriptive statistics like knowing mean, percentiles, range, standard deviation, etc. and highlighting when they are or are not appropriate give the shape of the underlying data. For example, when data are highly skewed, as in-house prices or income, the median is the appropriate metric with which to summarize the data, not the mean. Just training people to make fewer assumptions, to plot and examine the data and to use appropriate summary metrics would be a big win. And then a quick introduction to computational data mining and machine learning approaches to extract insights from data, as well as create data products using recommendation engines and other predictive models will go a long way.

 
Another win can come from data visualization skills. It doesn’t make any sense to spend a huge amount of effort on data collection and analysis, only to fail, and lessen the data’s impact, at the finish line. Just a small amount of data visualization training goes a long way and can greatly enhance people’s presentation skills and make insights clearer, more digestible and ultimately likely to be used. Too often, charts are full of visual junk and unnecessary clutter and annotations that detract from the key point. Or, inappropriate chart types are used — such as multiple pie charts each with a large number of segments — or, a color scheme is chosen that makes it near impossible to interpret.
 

• Decision making: Data can only make an impact if it is actually incorporated in the decision-making process. An organization can have quality, timely and relevant data and skilled analysts who generate masterful reports with carefully crafted and presented insights and recommendations. However, if that report sits unopened on a desk, or unread in an inbox, or the decision maker has already made up his mind what action he or she is going to take, regardless of what the data shows, then all the efforts are worthless, and more often than not, too often organizations have a prevailing culture where intuition is valued or there is a lack of accountability. One way to avoid this is to cultivate a culture of objective experimentation, such as A/B testing. In those scenarios, whether it be a change to website design or marketing messaging, you control for as much as possible, determine the success metrics and required sample sizes, change that one thing and let the experiment run. The key here is to have a clear analysis plan and set out the success metric and any predictions before the experiments run. That is also true of any pilot program.

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​My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. 
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Head of Digital Product
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