This editorial was originally publiched on July 24, 2014. It is being republished as Steve is out of town.
Most of us that are data professionals think the best way to make decisions is to use data to justify some course of action. We look for patterns in data, some guidance that the information we have will lead us to make the best choice for our organization. Google has talked about making data driven decisions as a part of their success and they think more organizations should do this. Any number of other companies also use data to power their BI systems and dashboards that help their employees make better choices.
That seems in contrast to this piece from the Harvard Business Review that says that great decisions don't start with data. It talks about using stories and emotions, with a few key facts sprinkled in, to help sell ideas and get decisions made. On one hand I agree that stories help to sell decisions, but I often have found that successful salespeople use this technique to deceive and convince by plucking emotional heartstrings, and using relatively little data.
In my mind, the best way to make decisions is to go with your instincts, but while examining and understanding the data. You can't discard data, especially when it presents strong patterns. However data can be deceiving when we don't carefully examine the ways in which it's put together. An average doesn't always reflect the actual value of a set of numbers, especially when we don't also understand the range, standard deviation, and count of values.
We also have to realize where we do and don't have experience and expertise in some subject. We should certainly look to data to guide us and perhaps even justify our decisions, but we can't forget that the human brain is still an important part of any computational exercise. We need employees that you use their judgement, in collaboration with data, to make the best decision for our organizations.
Visualization is often the first step in analyzing data. Python makes visualization easy. In this article, Robert Sheldon demonstrates how to generate multiple charts from one dataset using Python with SQL Server Machine Learning Services. More »
If you were asked what the benefits of DevOps are, you could probably name two or three straight away. Maybe four or five. But – and here’s the thing – what if the person down the corridor was asked the same question? Someone who works in the same place, but does a different job. More »
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Yesterday's Question of the Day
(by Steve Jones):
I've got some data that contains US postal codes. This data is stored in a numeric field. If I use the FORMAT() function, what format string should I use to ensure that any leading zeros are replaced with a real 0 and the rest of the numeric values are returned correctly? What string is the second parameter of the FORMAT() function.
) AS a (n)
SELECT TOP 10
The formatting string of 0 with n's will return a leading zero to pad the length if there are not enough digits and the actual digit otherwise.
union with constant values
I'm doing this:
select 'EVY', 'Everyone', ...
where ... and @Grouping = 'false'
That @Grouping = false still returns the row if the value is true....
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