guide: absorb What is DVC? into other existing docs, et al.

content/docs/user-guide/what-is-dvc/questions.md → content/docs/how-to/best-practices.md

@@ -1,9 +1,9 @@
-# Common DVC Questions
+# Best Practices
 
-Some of these questions are easy to answer individually. Data scientists,
-engineers, or managers may already knows or can easily find answers to some of
-them. However, the variety of answers and approaches makes data science
-collaboration a nightmare. **A systematic approach is required.**
+Data scientists, engineers, or managers may already know or can easily find
+answers to some of these questions. However, the variety of answers and
+approaches makes data science collaboration a nightmare. **A systematic approach
+is required.**
 
 ## Source code and data versioning
 

content/docs/sidebar.json

@@ -86,7 +86,7 @@
         "slug": "what-is-dvc",
         "label": "What is DVC?",
         "source": "what-is-dvc/index.md",
-        "children": ["questions", "core-features", "related-technologies"]
+        "children": ["core-features", "related-technologies"]
       },
       {
         "label": "DVC Files and Directories",
@@ -140,6 +140,16 @@
       }
     ]
   },
+  {
+    "slug": "how-to",
+    "source": false,
+    "children": [
+      {
+        "slug": "best-practices",
+        "source": "how-to/best-practices.md"
+      }
+    ]
+  },
   {
     "slug": "command-reference",
     "source": "command-reference/index.md",

content/docs/user-guide/index.md

@@ -8,19 +8,16 @@ more parts of DVC, to more advanced and specific things you can do. We also
 include a few guides related to contributing to
 [this open-source project](https://github.com/iterative/dvc).
 
-Please choose from the navigation sidebar to the left, or click the `Next`
-button below ↘
-
 ## What is DVC?
 
 Even with all the success we've seen today in machine learning (ML),
 specifically deep learning and its applications in business, the data science
 community still lacks good practices for organizing their projects and
-effectively collaborating across their varied ML projects. This is a critical
-challenge: we need to evolve towards ML algorithms and methods no longer being
-tribal knowledge and making them easy to implement, reuse, and manage.
+collaborating effectively. This is a critical challenge: we need to evolve
+towards ML algorithms and methods no longer being tribal knowledge and making
+them easy to implement, reuse, and manage.
 
-To make progress, many areas of the ML experimentation process need to be
-formalized. Common questions need to be answered in an unified, principled way.
+---
 
-See also these [Common DVC Questions](/doc/user-guide/what-is-dvc/questions)).
+Please choose from the navigation sidebar to the left, or click the `Next`
+button below ↘