Improving Donor Data Workflows Through Root Cause Analysis

As a Business Analyst who’s worked across multiple domains, I kept asking: "How can we analyze and improve processes while ensuring alignment with customer experience, automation opportunities, and real-world execution constraints?" So 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐚 𝐧𝐞𝐰 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 & 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 called 𝐓𝐑𝐀𝐂𝐄—designed for Business Analysts, by a Business Analyst. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝐓𝐡𝐞 𝐓𝐑𝐀𝐂𝐄 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 A structured 5-step approach to analyze, redesign, and implement better business processes. ✅ T - Touchpoint Mapping Map every customer, system, and employee interaction throughout the process. ⏩ Why? Because pain points often lie hidden between handoffs and touchpoints. 🔸 Example: While improving a claims process in insurance, we mapped the customer journey and discovered that 4 out of 7 delays occurred during internal handoffs—not external approvals. ✅ R - Root Cause Discovery Go beyond symptoms. Use tools like 5 Whys, Fishbone diagrams, or even process mining to get to the bottom of inefficiencies. 🔸 Example: A healthcare provider noticed repeated data entry errors. Root cause? The patient registration interface required double entry into two systems due to poor integration. ✅ A - Automation & Adaptability Assessment Assess which parts of the process can be automated (RPA, AI, workflow engines), and how adaptable the process is to scalability, policy changes, or compliance. 🔸 Example: In a telecom project, we flagged a manual SIM activation step as a bottleneck. After RPA automation, processing time dropped by 85%. ✅ C - Change Impact Analysis Evaluate how proposed changes will impact stakeholders, systems, SLAs, and compliance. Build readiness through a Change Impact Matrix. 🔸 Example: In a bank’s loan onboarding process, changing document verification impacted 4 systems and 3 departments. Early impact analysis helped us prep all affected users and avoid go-live delays. ✅ E - Execution Blueprint Create a visual and documented blueprint of the improved process: • Swimlane diagrams • RACI matrix • System handoffs • Success metrics 🔸 Example: For a logistics firm, we redesigned the inventory return workflow. The execution blueprint became the training, UAT, and SOP foundation, saving 2 weeks of rollout effort. 𝐖𝐡𝐲 𝐓𝐑𝐀𝐂𝐄 𝐖𝐨𝐫𝐤𝐬: ✔️ Human-centric (starts at touchpoints) ✔️ Analytical (root cause and impact driven) ✔️ Future-ready (focus on automation and adaptability) ✔️ Grounded in BA tools (flows, matrices, UAT, change analysis) ✔️ Outcome-focused (delivers real, implementable blueprints) 𝐎𝐯𝐞𝐫 𝐭𝐨 𝐘𝐨𝐮: Would you try TRACE in your next process improvement initiative? 𝐋𝐞𝐚𝐫𝐧 𝐁𝐏𝐌𝐍 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐟𝐫𝐨𝐦 𝐦𝐞: https://lnkd.in/eYHriqm3 BA Helpline

Root Cause Analysis (RCA) by using 1M to 10M analysis. 1 - Man (Human Factors): The foundation of the analysis, focusing on human-related factors such as operator skills, training, physical and mental state, experience level, and human error potential. This includes things like fatigue, competency, attention to detail, and adherence to procedures. 2M - Machine: Adds equipment-related factors to the analysis. This covers all aspects of machinery and tools including: - Equipment condition and age - Maintenance history - Operating capacity and limitations - Calibration status - Tool wear and reliability - Machine settings and adjustments 3M - Material: Examines all inputs and raw materials used in the process: - Quality of RM - Material specifications - Storage conditions - Supplier reliability - Material variability - Handling and transportation 4M - Method: Analyzes the processes and procedures being used: - Standard operating procedures - Work instructions - Process parameters - Production schedules - Workflow design - Best practices implementation 5M - Measurement: Focuses on how data is collected and monitored: - Measuring instruments and their accuracy - Calibration systems - Data collection methods - Quality control parameters - Testing procedures - Statistical process control 6M - Mother Nature (Environment): Considers environmental factors that could impact the process: - Temperature and humidity - Lighting conditions - Workplace layout - Cleanliness - Environmental controls - Weather impacts 7M - Money: Examines financial aspects affecting quality: - Budget constraints - Resource allocation - Cost of quality - Investment in improvements - Financial priorities - Cost-cutting impacts 8M - Management: Evaluates leadership and organizational factors: - Decision-making processes - Communication channels - Policy implementation - Resource planning - Leadership style - Organizational structure 9M - Maintenance: Focuses on upkeep and preservation activities: - Preventive maintenance schedules - Repair procedures - Spare parts management - Equipment lifecycle - Maintenance training - Documentation 10M - Motivation: The final layer examining psychological and cultural factors: - Employee engagement - Recognition systems - Work culture - Team morale - Incentive programs - Job satisfaction This comprehensive framework allows for increasingly detailed analysis of potential root causes, with each "M" adding another dimension to consider. It's particularly valuable because it: - A structured approach to problem-solving - Ensures no major factors are overlooked - Helps identify interconnections between different factors - Supports systematic improvement efforts - Can be applied to both proactive and reactive problem-solving The power of this framework lies in its scalability - you can start with the basic 1M or 2M analysis for simpler issues and expand to include more factors as needed for more complex problems.

When a company begins its DQ journey, one of the initial steps is to establish a DQ Issue Management and Resolution system (DQ IMR). DQ/DG tools often include one. Custom systems are frequently created. It can also be implemented in JIRA, ServiceNow, SharePoint, or Excel. One of the fields that should exist in such systems is the Root Cause Analysis field. However, this field can be tricky to populate. For example, if you encounter a data transformation error and are trying to find the root causes: - Why did the transformation error occur? Because the transformation logic was incorrect. (Why #1) - Why was the transformation logic incorrect? Because the requirements were not clearly defined. (Why #2) - Why were the requirements not clearly defined? Because of poor communication between teams. (Why #3) - Why was there poor communication? Because there are no regular meetings or updates. (Why #4) - Why are there no regular meetings? Because of a lack of structured project management. (Why #5) Which of these 5 whys should we populate the field with? I typically capture the true “Root Cause” in this field (the last why). This field would contain very general root causes like “lack of training,” “lack of data literacy,” “no clearly defined data standards,” “no semantic standards,” “no structured processes,” “poor stewardship processes,” etc. I also prefer standardizing the list with predefined values and an option for “other root causes.” This helps with reporting—we cannot fix everything at once, so spotting the most frequently occurring root cause is important. What do you think? Do you use free text or predefined lists for root causes? If you use predefined lists, is there any 'typical' root cause missing from the list?

Think you know the problem? The DMAIC Analyze Phase proves it. Root cause analysis ensures you’re fixing the right issue. Fix problems with data, not guesswork. DMAIC is a five-step process to solve business issues. The Analyze Phase: What’s the Goal? → Find the real root causes → Validate insights with data → Prioritize key problems What You Need to Start → Performance data as a baseline → Key process metrics → A solid data collection plan → A measurement system report What You’ll Deliver → Clear root cause analysis → Prioritized list of problems → Data-backed hypothesis test results → Updated process maps Tools That Help Basic tools: → Fishbone diagram → 5 Whys method → Scatter plots → Pareto charts Advanced tools: → Hypothesis testing → ANOVA (statistical analysis) → FMEA (Failure Mode Analysis) → Multivari charts Tips for Success → Keep asking “Why?” until you find the root cause → Test multiple hypotheses → Use visuals like charts and maps → Get input from cross-functional teams Who Needs to Be Involved? → Process owners → Team members → Managers → Quality engineers → Customers How to Know You Did It Right → Root causes are confirmed → Data supports every conclusion → The team agrees on key issues → The action plan is updated → The fixes align with business goals Share ♻️ to help your network and follow Sergio D’Amico for more insights on continuous improvement and organizational excellence. 📌P.S. Fixing the wrong problem costs time and money. Validate your data before you act.

Couple of years back, I posted this message on LinkedIn. As years passed by and me matured in practicing Data Governance, the question arises sometimes in my mind is, “Do I still stick to the points that I mentioned in this post?”. The answer is yes. 1.      When we profile our data every single day and monitor the results through a dashboard or other channels, we can easily sense and identify the repeating data quality issues. We should accumulate and store the profiling results so as to grab that sense. 2.      When we notice the repeating data quality issues, we get the feel to investigate the root causes for them. We simply do not want to sit and monitor the issues happening regularly. We are urged to develop the root cause diagrams (Ishikawa or Fishbone) and analyze the causes. 3.      One of the root causes could be the gaps in the business processes. In my experience, mostly integrity and consistency related data quality issues unearth the gaps in the business processes and systems. When there is common data element between systems and if it is missing in one of the systems, that causes a broken relationship and that possibly shows the gap in the business processes. A change in one system in the value of the data element should be reflected in other systems. Thus, from the profiling results, we could feel the gaps in the business processes, workflows and systems across. 4.      One other root cause could be missing standards for data elements. Common examples could be person names and date values. A standard should be established for dates, as it can be represented in multiple formats. Multiple formats need conversion overheads during processing, analyses and reporting. Person names could be another big challenge as some names get shortened or abbreviated, thus, causing inconsistencies. If we keep seeing such inconsistencies, it is time to establish standards and more importantly, standards need to be enforced. 5.      Once we do profile daily, collect results and identify issues, what would be next thing to do? We would require sharing with business stakeholders, as the system and data owned by them. We need to have stewards and custodians. As part of the Data Governance program, the roles of stewards and custodians need to be established, and the responsibilities assigned. Identified data quality issues need to be assigned to them through stewardship process to get them fixed. 6.      The regular findings on inconsistencies on the critical data elements bring out the need for single source of truth. Customer data element is the common example and if there are multiple sources that generate customer data, there is a strong possibility of inconsistencies and incorrect information. A strong need arises to have highly trustable and centralized version of such data, and the need is unavoidable. Data Quality plays a vital role for the success of Data Governance when profiling is done regularly. #datagovernance #datamanagement #dataquality