Good practices for manual modifications of data
1.  Do not modify the original data

Having the original data source intact is important.  You may find that updates you make to the data are not valid.  You may also find a more efficient way to make updates and you will want to regression test those updates.

Always work with a copy of the data, and add columns/properties/metadata that includes any processed corrections.

Example, if your data is a .csv file that includes a city name that contains several misspellings:

1. Copy the .csv to a new file
2. Add a column for containing "adjusted_city_name"
3. Copy data from the city_name column to the 
   adjusted_city_name column and make corrections in that column.


2. Document proposed changes

Any changes you want to make to data should be documented so that they can be replicated moving forward.

Version control and timestamp the document every time you change it.  That will help in troubleshooting at a later date.

Be explicit.  Do not simply state "correct capitalization problems", state "ensure that the first letter of each city name begins with a capital letter and the remaining letters are lower-case."

Update the document with references to any automation routines that have been built to manage data cleansing.

3. Decide on a standard data cleansing technology

Whether you use perl, python, java, a particular utility, a manual process or something else is not the issue.  The issue is that in the future you want to hand the data cleansing process to somebody else.  If they have to know 12 different data cleansing technologies, delegating the cleansing procedure will be very difficult.

4. Standardize the workflow

There should be a standard way to handle new data.  Ideally, it will be as simple as dropping a file in a specific location and a predictable automated process cleanses it and hands off a cleansed set of data to the next processing step.

5. Make as few changes as is absolutely necessary

It's always better to have a fault tolerant analysis than one that makes assumptions about the data.  

6. Avoid manual updates

It's always tempting, but people are error-prone and again it makes delegation difficult.

Notes on manual processing

To more completely address the original question as to whether there's a "good" way to do manual processing, I would say no, there is not.  My answer is based on experience and is not one that I make arbitrarily.

I have had more than one project lose days of time due to a client insisting that a manual data cleansing process was just fine and could be handled internally.  You do not want your projects to be dependent on a single individual accomplishing a judgement based task of varying scale.

It's much better to have that individual build and document a rule set based on what they would do than to have them manually cleanse data.  (And then automating that rule set)

If automation fails you in the end or is simply not possible, the ability to delegate that rule set to others without domain specific knowledge is vital.

In the end, routines to do something like correct city names can be applied to other data sets.