diff --git a/CHANGELOG.md b/CHANGELOG.md
index 35e21b91538..2c7cafc2d35 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -174,6 +174,7 @@
 - PR #4493 Skip legacy testing in CI
 - PR #4524 Updating `__setitem__` for DataFrame to use scalar scatter
 - PR #4534 Disable deprecation warnings as errors.
+- PR #4506 Check for multi-dimensional data in column/Series creation
 
 ## Bug Fixes
 
diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index b3d822510e9..710545c2a05 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -1160,37 +1160,9 @@ def as_column(arbitrary, nan_as_null=None, dtype=None, length=None):
                 col = col.set_mask(mask)
         elif np.issubdtype(col.dtype, np.datetime64):
             if nan_as_null or (mask is None and nan_as_null is None):
-                null = column_empty_like(col, masked=True, newsize=1)
-                col = libcudfxx.replace.replace(
-                    col,
-                    as_column(
-                        Buffer(
-                            np.array([np.datetime64("NaT")], dtype=col.dtype)
-                        ),
-                        dtype=col.dtype,
-                    ),
-                    null,
-                )
+                col = utils.time_col_replace_nulls(col)
         return col
 
-    elif isinstance(arbitrary, np.ndarray):
-        # CUDF assumes values are always contiguous
-        if not arbitrary.flags["C_CONTIGUOUS"]:
-            arbitrary = np.ascontiguousarray(arbitrary)
-
-        if dtype is not None:
-            arbitrary = arbitrary.astype(dtype)
-
-        if arbitrary.dtype.kind == "M":
-            data = datetime.DatetimeColumn.from_numpy(arbitrary)
-
-        elif arbitrary.dtype.kind in ("O", "U"):
-            data = as_column(
-                pa.Array.from_pandas(arbitrary), dtype=arbitrary.dtype
-            )
-        else:
-            data = as_column(cupy.asarray(arbitrary), nan_as_null=nan_as_null)
-
     elif isinstance(arbitrary, pa.Array):
         if isinstance(arbitrary, pa.StringArray):
             pa_size, pa_offset, nbuf, obuf, sbuf = buffers_from_pyarrow(
@@ -1357,6 +1329,49 @@ def as_column(arbitrary, nan_as_null=None, dtype=None, length=None):
             elif np.issubdtype(data.dtype, np.datetime64):
                 data = data.fillna(np.datetime64("NaT"))
 
+    elif hasattr(arbitrary, "__array_interface__"):
+        # CUDF assumes values are always contiguous
+        desc = arbitrary.__array_interface__
+        shape = desc["shape"]
+        arb_dtype = np.dtype(desc["typestr"])
+        # CUDF assumes values are always contiguous
+        if len(shape) > 1:
+            raise ValueError("Data must be 1-dimensional")
+
+        arbitrary = np.asarray(arbitrary)
+        if not arbitrary.flags["C_CONTIGUOUS"]:
+            arbitrary = np.ascontiguousarray(arbitrary)
+
+        if dtype is not None:
+            arbitrary = arbitrary.astype(dtype)
+
+        if arb_dtype.kind == "M":
+
+            time_unit, _ = np.datetime_data(arbitrary.dtype)
+            cast_dtype = time_unit in ("D", "W", "M", "Y")
+
+            if cast_dtype:
+                arbitrary = arbitrary.astype(np.dtype("datetime64[s]"))
+
+            buffer = Buffer(arbitrary)
+            mask = None
+            if nan_as_null:
+                data = as_column(
+                    buffer, dtype=arbitrary.dtype, nan_as_null=nan_as_null
+                )
+                data = utils.time_col_replace_nulls(data)
+                mask = data.mask
+
+            data = datetime.DatetimeColumn(
+                data=buffer, mask=mask, dtype=arbitrary.dtype
+            )
+        elif arb_dtype.kind in ("O", "U"):
+            data = as_column(
+                pa.Array.from_pandas(arbitrary), dtype=arbitrary.dtype
+            )
+        else:
+            data = as_column(cupy.asarray(arbitrary), nan_as_null=nan_as_null)
+
     elif isinstance(arbitrary, memoryview):
         data = as_column(
             np.asarray(arbitrary), dtype=dtype, nan_as_null=nan_as_null
diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py
index 5639e1dabe4..b1278c0d766 100644
--- a/python/cudf/cudf/core/column/datetime.py
+++ b/python/cudf/cudf/core/column/datetime.py
@@ -4,11 +4,10 @@
 import pandas as pd
 import pyarrow as pa
 
-import cudf
 import cudf._lib as libcudf
 import cudf._libxx as libcudfxx
 from cudf.core.buffer import Buffer
-from cudf.core.column import as_column, column
+from cudf.core.column import column
 from cudf.utils import utils
 from cudf.utils.dtypes import is_scalar, np_to_pa_dtype
 
@@ -54,47 +53,6 @@ def __contains__(self, item):
             return False
         return item.astype("int_") in self.as_numerical
 
-    @classmethod
-    def from_numpy(cls, array):
-        cast_dtype = array.dtype.type == np.int64
-        if array.dtype.kind == "M":
-            time_unit, _ = np.datetime_data(array.dtype)
-            cast_dtype = time_unit in ("D", "W", "M", "Y") or (
-                len(array) > 0
-                and (
-                    isinstance(array[0], str)
-                    or isinstance(array[0], dt.datetime)
-                )
-            )
-        elif not cast_dtype:
-            raise ValueError(
-                ("Cannot infer datetime dtype " + "from np.array dtype `%s`")
-                % (array.dtype)
-            )
-
-        if cast_dtype:
-            array = array.astype(np.dtype("datetime64[s]"))
-        assert array.dtype.itemsize == 8
-
-        mask = None
-        if np.any(np.isnat(array)):
-            null = cudf.core.column.column_empty_like(
-                array, masked=True, newsize=1
-            )
-            col = libcudfxx.replace.replace(
-                as_column(Buffer(array), dtype=array.dtype),
-                as_column(
-                    Buffer(
-                        np.array([np.datetime64("NaT")], dtype=array.dtype)
-                    ),
-                    dtype=array.dtype,
-                ),
-                null,
-            )
-            mask = col.mask
-
-        return cls(data=Buffer(array), mask=mask, dtype=array.dtype)
-
     @property
     def time_unit(self):
         return self._time_unit
diff --git a/python/cudf/cudf/core/index.py b/python/cudf/cudf/core/index.py
index 0a9f2e84483..ca2b951478f 100644
--- a/python/cudf/cudf/core/index.py
+++ b/python/cudf/cudf/core/index.py
@@ -852,13 +852,11 @@ def __init__(self, values, **kwargs):
         # and then just dispatch upstream
         kwargs = _setdefault_name(values, kwargs)
         if isinstance(values, np.ndarray) and values.dtype.kind == "M":
-            values = DatetimeColumn.from_numpy(values)
+            values = column.as_column(values)
         elif isinstance(values, pd.DatetimeIndex):
-            values = DatetimeColumn.from_numpy(values.values)
+            values = column.as_column(values.values)
         elif isinstance(values, (list, tuple)):
-            values = DatetimeColumn.from_numpy(
-                np.array(values, dtype="<M8[ms]")
-            )
+            values = column.as_column(np.array(values, dtype="<M8[ms]"))
         super(DatetimeIndex, self).__init__(values, **kwargs)
 
     @property
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
index 1c9e36fc50e..981dcc7a340 100644
--- a/python/cudf/cudf/tests/test_column.py
+++ b/python/cudf/cudf/tests/test_column.py
@@ -63,3 +63,18 @@ def test_column_offset_and_size(pandas_input, offset, size):
     expect = pandas_input.iloc[slicer].reset_index(drop=True)
 
     assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.array([[23, 68, 2, 38, 9, 83, 72, 6, 98, 30]]),
+        np.array([[1, 2], [7, 6]]),
+    ],
+)
+def test_column_series_multi_dim(data):
+    with pytest.raises(ValueError):
+        cudf.Series(data)
+
+    with pytest.raises(ValueError):
+        cudf.core.column.as_column(data)
diff --git a/python/cudf/cudf/utils/utils.py b/python/cudf/cudf/utils/utils.py
index c0ad5869507..4d0f5734325 100644
--- a/python/cudf/cudf/utils/utils.py
+++ b/python/cudf/cudf/utils/utils.py
@@ -414,3 +414,19 @@ def to_nested_dict(d):
     Convert the given dictionary with tuple keys to a NestedOrderedDict.
     """
     return NestedOrderedDict(d)
+
+
+def time_col_replace_nulls(input_col):
+    from cudf.core.column import column_empty_like, as_column
+    import cudf._libxx.replace as replace
+
+    null = column_empty_like(input_col, masked=True, newsize=1)
+    out_col = replace.replace(
+        input_col,
+        as_column(
+            Buffer(np.array([np.datetime64("NaT")], dtype=input_col.dtype)),
+            dtype=input_col.dtype,
+        ),
+        null,
+    )
+    return out_col
diff --git a/python/dask_cudf/dask_cudf/backends.py b/python/dask_cudf/dask_cudf/backends.py
index fd223a07668..9132b71002b 100644
--- a/python/dask_cudf/dask_cudf/backends.py
+++ b/python/dask_cudf/dask_cudf/backends.py
@@ -20,7 +20,7 @@ def _nonempty_index(idx):
     elif isinstance(idx, cudf.core.index.DatetimeIndex):
         start = "1970-01-01"
         data = np.array([start, "1970-01-02"], dtype=idx.dtype)
-        values = cudf.core.column.DatetimeColumn.from_numpy(data)
+        values = cudf.core.column.as_column(data)
         return cudf.core.index.DatetimeIndex(values, name=idx.name)
     elif isinstance(idx, cudf.core.index.StringIndex):
         return cudf.core.index.StringIndex(["cat", "dog"], name=idx.name)