Accidental Analysts®: What are they doing with my data?

Eileen McDaniel, Ph.D. and Stephen McDaniel
This article was originally published in late 2013 in
The Data Warehouse Institute FlashPoint Newsletter

Earlier this year, we presented this topic in a talk to an independent group of data professionals. When we noticed it was mistakenly promoted as “Accidental Analysts: What are they doing TO my data?”, we had to laugh! Unintentional typo or not, we’ve found that data warehousing specialists often wonder what businesspeople are doing on their end. Who are accidental analysts, how do they analyze data, and what aspects of the data warehouse can data professionals evaluate and improve upon so that they are set up for success instead of frustration?

Who are accidental analysts?

Accidental_Analyst_in_hurry_Many business analysts either lack formal education in data analysis or took courses that didn’t fully prepare them for the challenges of real-world data analysis. They are asked to quickly answer business questions so that managers, colleagues and clients are able to identify and implement a plan of action. After teaching analysts in many organizations from all skill levels and backgrounds, we discovered that a major obstacle to obtaining good results is that many are uncertain of the steps to take when analyzing their data. They need a plan of attack, regardless of the analysis software that they are using!

 

The Seven C’s of Data Analysis

The_7Cs_of_Data_Analysis_Copyright_Freakalytics_LLC_605_175

The scientific method has been used by scientists for hundreds of years to design and analyze experiments. In our training and books, we adapted this method to fit business analysis, targeting typical questions that accidental analysts answer and emphasizing common areas of difficulty. This framework is The Seven C’s of Data Analysis. Many experienced analysts tell us these steps are similar to those that they naturally follow when analyzing data. Even better, these steps have given them a guide to directly mentor accidental analysts.

Here is an overview of the step-by-step method:

The First C: Choose Your Questions. The step most often overlooked as analysts are excited to begin working with the data. However, it is crucial to keeping the analysis on track and arriving at actionable results.

The Second, Third and Fourth C’s: Collect Your Data, Check Out Your Data and Clean Up Your Data. After these steps, analysts are relatively confident that their data are in good form and understand basic information about their data items (sometimes they even answer the original question!).

The Fifth and Sixth C’s: Chart Your Analysis and Customize Your Analysis. These steps involve core techniques and best practices of visual analytics, including matching the appropriate graph or table to the question at hand and arranging or selecting data to look at specific areas of interest.

The Seventh C: Communicate Your Results. Presenting the insights from the analysis so others can learn about opportunities and risks and take action.

We focus on the first four steps in this article.

 

How can data professionals help accidental analysts be more successful?

Accidental_Analyst_in_confidentThe First C: Choose Your Questions. Data professionals are regularly approached by analysts who have not yet fully defined the question they are researching. This can be quite a challenge! The data needed obviously depends on the question, so it is essential to figure out the ultimate goal of the analysis. Helping analysts understand that their questions radically affect the data retrieved can minimize frustration for you and them. Finally, work with them to avoid scoping the question so that it is limited by the available data–there may be data in the organization that your data team isn’t aware of or can acquire in the future.

The Second C: Collect Your Data. This typically is the most frustrating and time-consuming step of an analysis. It is intimidating to an accidental analyst, so refactoring your data infrastructure to minimize these activities will help your team be more successful. Also, if possible, provide them with the tools and knowledge to perform these activities.

Multiple joins
Complex calculations
Cross-platform queries
Combining OLAP, relational and local files
Critical data in people’s heads and spreadsheets!
Third-party and cloud data sources

The Third C: Check Out Your Data. Even a good data platform can have many issues with regards to this step. Without business context, data teams are limited in their ability to find anomalous outcomes in the data. Leverage the work of accidental analysts for long-term improvements.

Unrealistic quality expected
Data coding confusion
Need help with rules/data history
Leverage feedback to data warehouse/sources

The Fourth C: Clean Up Your Data. Analysts frequently clean data locally because they are just plain wrong otherwise! Try not to criticize them, learn from it and better understand the context.

Time is limited
Source systems can’t be changed
Various business assumptions/scenarios
Opportunity to find systemic issues
Often the reason they move data to Excel or Access!

Other areas in which you can support accidental analysts include: access to data update status, centralized locations for file uploads and indexing frequently-used data tables.

 

Order “The Accidental Analyst
Amazon

Bios

Eileen is author of the book The Accidental Analyst® and Director of Analytic Communications. Stephen is an independent advisor to Chief Data Officers. Both write and teach Accidental Analyst courses, are on the Faculty at INFORMS and can be reached at Freakalytics.com

Photos from Flickr.com –
rights reserved by herlitz_pbs and International Information Program. (IIP)