Data Preparation Checklist: 5 Questions to Ask

The first and most crucial step to data analysis is data preparation. While enterprises may invest billions on gathering and analyzing streams of data with technology, it may not always reap dividends– with ineffective data preparation as the biggest hurdle.

It may sound easy, but data preparation involves a series of steps like data integration, profiling, data cleaning, data governance, etc. that make it tedious and time-consuming. Plus, data preparation is expensive. Given the fact that data preparation is a tough and elaborate process, data preparation must be done efficiently.

So what makes up a strong data preparation strategy? We looked at standard processes around the industry to bring you this 5-point checklist that is unmissable when preparing data for analysis. 

1. What is the end goal of data analysis?

Evaluating business requirements and aim of data analysis is key. Meaning, they need to determine what kind of business problems they are evaluating and what kind of answers they are seeking. By doing so, they can easily identify the type of analysis needed to extract valuable insights. This step saves hard, manual work and helps generate the best results.  

2. Where is the data housed?

After determining your needs and goals, it is essential to identify the data sources to get all the relevant data. This could be a series of spreadsheets or larger databases, data lakes, data warehouse or cloud sources. The data can also be collected from myriad departments.

The questions that are needed to be asked at this stage are: 

• What kind of data sources do you work with?
• Do you need to take external sources into the account?
• Are the required permissions or credentials for data access available?

3. Does data require manipulation?

Data, at times, needs to be transformed or manipulated prior to analysis. This can be a possible case when multiple datasets or tables employ different formats for the same information, or when incoming data is not consistent or consists of duplicate information. Large volumes of data may also need to be consolidated further by creating new tables on top of the existing ones.

At this stage, you need to ask:

• Can we use the data as-is or is there a need for transformation?
• In case some type of inconsistencies or redundant values are present, what should be done to clean data? Should the approach to cleaning be systematic or instinctive?
• Are summary tables needed?
• Does the data have to be joined with tables we are working with or combined to create a new table? 

4. How can the data be connected? 

When you are working with a lot of data sources and tables, modeling the data in a way that allows dashboard users to quickly receive answers for ad hoc queries by connecting related fields in different tables becomes necessary.

The type of relationship shared by entities in your data model will determine the types of queries your future analysis will be able to answer as well as the efficiency with which it does so.

In many cases, you may need to create an amalgamation of data in a secondary environment which will serve as your analytical database.

Some of the most important questions needed to be asked here are:

• What kind of relationship will be formed once the fields are connected?
• Will you be able to append data sources and make changes to the data model?
• Is it possible to make the relationship simpler without impacting performance?
• When data is imported, what should be its frequency?
• What kind of impact will it have on the production environment?

5. How can the results be verified? 

At the end of data preparation, you need to make sure that the result is accurate. 

To verify results, you need to ask:

• Do calculations in analytical atmosphere fetch the same results as the calculations performed manually on the data? 
• Do the final results make sense on a general level?

After you are done with data preparation and its stages mentioned in the checklist, i.e. you have identified data, transformed it, established data model, moved the data into a database and verified results, you can begin the analysis to generate accurate insights.

With this 5-step process to generate better insights supported by an effective data preparation process, you would be able to tap into fresh business opportunities easily with better decision-making.