“All good evaluation starts with a utilization focus. We need to ask who needs to learn what, for what purpose, by when, and to what level of certainty?”
However, in an era of big data, machine learning, and data analytics, and with rapid decreases in the cost and speed of data collection, data clutter seems almost inevitable – and possibly unavoidable. Anyone who has ever sat in front of a massive Excel spreadsheet filled with dozens of indicators, cross-checking them with the original proposal, separate work plans, monitoring reports and Evaluation SOWs may experience the same feelings as individuals facing an overwhelmingly physically cluttered home: anxiety, confusion, disarray, stress. When looking at a needs assessment design, a logical framework, an M&E Plan, a full M&E system, or tackling routine monitoring or evaluation design, you may recognize the signs that a particular project or program might not just have some organizational issues, but data clutter issues.
5 Signs of a Data Clutter Problem
We might have a problem. How do I convince my team to reduce data clutter?
The best way to harness big data and new data analytics opportunities is to first bring focus to your project’s current clutter. In order to harness the power of exciting analytical and evaluation methods, we must first have the ability to develop the right questions to produce usable, actionable insights.
Practically, reducing data clutter will help your project save money, save time, and improve the focus and quality of the fewer pieces done.
How to Recognize a Data Clutter Problem
Davidson, E.J. (2012). Actionable Evaluation Basics. Real Evaluation Ltd.
Merriam Webster Dictionary. https://www.merriam-webster.com/dictionary/clutter