The cross-correlation can be computed BUT it is another thing to test it's significance.
It is usually interpreted as if
- there no outliers in either series
- each series is normally distributed
- the correlation is invariant over time
It is important to know the assumptions underlying a statistical test or computation. Your attempt to interpret a value of.2 could be the result of one or more of these assumptions are violated by the data.
If either of these is not true then standard tests of significance will be flawed.
Extract from J.M. Potts (1991) Statistical methods for the comparison of spatial patterns in meteorological variables. Unpublished PhD thesis, University of Kent at Canterbury
[Suppose that X and Y are independent normal random variables. Then, in the absence of temporal autocorrelation, the correlation coefficient, r, between random samples of size n from X and Y has a probability density function f(r) = ((1 - r^2)^0.5(n-4)) / B(0.5,0.5(n-2)) The distribution has mean zero and a variance of (n-1)^-1. However, the distribution is affected by the autocorrelation in X and Y, which increases the variance of the distribution and so gives rise to spurious large correlations. This problem was recognised for time series as early as 1926 by Yule in his presidential address to the Royal Statistical Society. In the discussion of the address which followed, Edgeworth asked 'What about space ? Are there not nonsense correlations in space ?' (Yule, 1926). These questions are still being addressed by statisticians (and climate researchers !).]
Apparently the Google folks (and others !) are unaware of these caveats or at least don't mention them as they promote spurious statistics AFAIK .
Perhaps they should reread http://www.math.mcgill.ca/dstephens/OldCourses/204-2007/Handouts/Yule1926.pdf or https://www.jstor.org/stable/2341482?seq=1#page_scan_tab_contents
Since you have daily data there may be day-of-the-week-effects or month-of-the-year effects or holiday effects or day-of-the month effects et al . Only your data knows for sure and you should listen to it.
Most textbooks (if not all !) present correlation using data that is cross-sectional where that is no auto-correlation between successive values of X or Y without cautioning the reader/student in the explicit terms that I have used here.