You've made a good start. I tend to query each column separately as I'm doing a conversion using templates, especially since value distributions are extremely helpful. Each data type has its quirks and custom aspects to look at. For example, for datetime data, I like to know if it is strictly dates or dates with times. And I like to know if the data fits in a smalldatetime. Knowing empty strings vs. nulls is important too.

Below is a template I use for imported datetime data interrogation

-- interrogate DateTime column

-- to see if it is a Time column, nullable, or an expected range

select

COUNT(*) as CountRows,

sum(case when <colname,,> is null then 1 else 0 end) as NullsCount,

sum(case when CONVERT(varchar(12), <colname,,>, 114) = '00:00:00:000' then 1 else 0 end) as DateNoTimeCount,

sum(case when CONVERT(varchar(12), <colname,,>, 114) > '00:00:00:000' then 1 else 0 end) as DateTimeCount,

sum(case when <colname,,> < '1/1/1900' then 1 else 0 end) as Before1900Count,

sum(case when <colname,,> between '1/1/1900' and '1/1/2000' then 1 else 0 end) as BetweenCount,

sum(case when <colname,,> >= '1/1/2000' then 1 else 0 end) as After2000Count

from

<tablename,,>

-- most frequent values

select top 100

<colname,,>,

COUNT(*) as frequencyCount

from

<tablename,,>

group by

<colname,,>

having

COUNT(*) > 1

order by

COUNT(*) desc