Quality Book Shelf – Data Story

Every quality professional needs to read Data Story: Explain Data and Inspire Action through Story by Nancy Duarte.

This book does an amazing job of giving you the tools of transforming a boring management review into a compelling narrative. Following the step-by-step recommendations will give you a blueprint for effective telling the story of your organizations quality maturity and help you execute into action.

For example, this table is the start of an amazing section about crafting a narrative that then goes into an amazing discussion on structuring a slide presentation to get this done.

 Argumentative Writing (Logical Approach)Persuasive Writing (Emotional Appeal)Writing a Recommendation (Blend of Both)
PurposeConstruct compelling evidence that your viewpoint is backed by the truth and is factualPersuade the audience to agree with your perspective and take action on your viewpointUse the data available, plus intuition, to form a point of view that requires action from your organization
ApproachDeliver information from both sides of the issue by choosing one side as valid and causing others to doubt the counterclaimDeliver information and opinions on only one side of the issue, and develop a strong connection with a target audienceDevelop a story supported by evidence ad also include any counterarguments your audience may have, so tat they feel you have considered their perspective
AppealsUse logical appears to support claims with solid examples, expert opinions, data, and facts. The goal is to be right, not necessarily take actionUse emotional appeals to convince others of your opinion and feelings, so the audience will move forward on your perspectiveStructure the appeal as a story, support your recommendation with data and solid evidence that sticks by adding meaning
ToneProfessional, tactful, logicalPersonal, passionate, emotionalAppropriate tone based on the audience

Another great takeaway is when Nancy presents results of her extensive analysis on word patterns in speeches, right down to the choice of effective verbs, conjunctions, adjectives, adverbs, interjections, and rhetorical questions. The choice of “process or performance verbs” is connected to whether the recommended course of action is continuity, change or termination.

This is a book that keeps giving.

I found it so invaluable that I bought a copy for everyone on my team.

Data Process Mapping

In a presentation on practical applications of data integrity for laboratories at the March 2019 MHRA Laboratories Symposium held in London, UK, MHRA Lead GCP and GLP Inspector Jason Wakelin-Smith highlighted the important role data process mapping plays in understanding these challenges and moving down the DI pathway.

He pointed out that understanding of processes and systems, which data maps facilitate, is a key theme in MHRA’s GxP data integrity guidance, finalized in March of 2018. The guidance is intended to be broadly applicable across the regulated practices, but excluding the medical device arena, which is regulated in Europe by third-party notified bodies.

IPQ. MHRA Inspectors are Advocating Data Mapping as a Key First Step on the Data Integrity Pilgrimage

Data process maps look at the entire data life-cycle from creation through storage (covering key components of create, modify and delete) and include all operations with both paper and electronic records.   Data maps are cross-functional diagrams (swim-lanes) and have the following sections:

  • Prep/Input
  • Data Creation
  • Data Manipulation (include delete)
  • Data  Use
  • Data Storage

Use a standard symbol for paper record, computer data and process step.

For computer data denote (usually by color) the level of controls:

  • Fully aligned with Part 11 and Data Integrity guidances
  • Gaps in compliance but remediation plan in place (this includes places where paper is considered “true copy”
  • Not compliant, no remediation plan

Data operations are depicted utilizing arrows.  The following data operations are probably most common, and are recommended for consistency:

  • Data Entry – input of process, meta data (e.g. lot ID, operator)
  • Data Store – archival location
  • Data Copy – transcription from another system or paper, transfer of data from one system to another, printing (Indicate if it is a manual process).
  • Data Edit – calculations, processing, reviews, unit changes  (Indicate if it is a manual process)
  • Data Move – movement of paper or electronic records

Data operation arrows should denote (again by color) the current controls in place:

  • Technical Controls – Validated Automated Process
  • Operational Controls – Manual Process with Review/Verified/Witness Requirements
  • No Controls – Automated process that is not validated or Manual process with no Review/Verified/Witness Considerations
Example data map

Understanding Data – A Core Quality Skill

A critical skill of a quality professional (of any professional), and a fundamental part of Quality 4.0, is managing data — knowing how to acquire good data, analyze it properly, follow the clues those analyses offer, explore the implications, and present results in a fair, compelling way.

As we build systems, validate computer systems, create processes we need to ensure the quality of data. Think about the data you generate, and continually work to make it better.

I am a big fan of tools like the Friday Afternoon Measurement to determine where data has problems.

Have the tools to decide what data stands out, use control charts and regression analysis. These tools will help you understand the data. “Looks Good To Me: Visualizations As Sanity Checks” by Michael Correll is a great overview of how data visualization can help us decide if the data we are gathering makes sense.

Then root cause analysis (another core capability) allows us to determine what is truly going wrong with our data.

Throughout all your engagements with data understand statistical significance, how to quantify whether a result is likely due to chance or from the factors you were measuring.

In the past it was enough to understand a pareto chart, and histogram, and maybe a basic control chart. Those days are long gone. What quality professionals need to bring to the table today is a deeper understanding of data and how to gather, analyze and determine relevance. Data integrity is a key concept, and to have integrity, you need to understand data.

Data, and all that jazz

As  we all try to figure out just exactly what Industry 4.0 and Quality 4.0 mean it is not an exaggeration to say “Data is your most valuable asset. Yet we all struggle to actually get a benefit from this data and data integrity is an area of intense regulatory concern.

To truly have value our data needs to be properly defined, relevant to the tasks at hand, structured such that it is easy to find and understand, and of high-enough quality that it can be trusted. Without that we just have noise.

Apply principles of good master data management and data integrity. Ensure systems are appropriately built and maintained.

Understand why data matters, how to pick the right metrics, and how to ask the right questions from data. Understand correlation vs. causation to be able to make decisions about when to act on analysis and when not to is critical.

In the 2013 article Keep Up with Your Quants, Thomas Davenport lists six questions that should be asked to evaluate conclusions obtained from data:

1. What was the source of your data?

2. How well do the sample data represent the population?

3. Does your data distribution include outliers? How did they affect the results?

4. What assumptions are behind your analysis? Might certain conditions render your assumptions and your model invalid?

5. Why did you decide on that particular analytical approach? What alternatives did you consider?

6. How likely is it that the independent variables are actually causing the changes in the dependent variable? Might other analyses establish causality more clearly?

Framing data, being able to ask the right questions, is critical to being able to use that data and make decisions. In the past it was adequate enough for a quality professional to have a familiarity with a few basic tools. Today it is critical to understand basic statistics. As Nate Silver advises in an interview with HBR. “The best training is almost always going to be hands on training,” he says. “Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”

Understanding data is a key ability and is necessary to thrive. It is time to truly contemplate the data ecosystem as a system and stop treating it as a specialized area of the organization.