, 13 tweets, 7 min read Read on Twitter
#bigdata is promising but not a panacea. We can miss obvious problems if we
rely only on big data. To enhance power of big data, we need to combine it with #thickdata - non-measurable, qualitative insights.

Sharing my IBM report, download 👇

deepblue.lib.umich.edu/handle/2027.42…
Limits of #bigdata 1⃣

Having lots of data can be overwhelming or have little utility
if the data are thin—that is, they lack meaning for users or fail to capture issues that matter most.
Limits of #bigdata 2⃣

Organizations may sometimes have big data on things that do not really
matter and yet lack data on issues that matter most. 🤔
SO WHAT TO DO? 👉 Combine #bigdata with thick data

If big data reflects volume, velocity, and variety for items that can be counted,
thick data concerns information about the significance, meaning, and connections that humans assign to services or technologies.
Any debate about the merits of big data versus thick data (or quantitative versus qualitative research) presents a false dichotomy. Both are necessary, and each serves unique purposes at different stages of the problem-solving process.
Usually we ASSUME we know what the problem is, what citizens or customers care most about...

In reality, these assumptions could be 💯 WRONG 😬

Leading to projects & designs that don't work in practice
Surveys that don't make sense to users
Wasted effort, confusing results
So #thickdata is necessary for Step 1⃣ of any problem-solving
👉 Identify problems that matter to users

Don't ASSUME, instead ASK 💬

See world through users' eyes 👀 What others care about may not be what you care about because our life experiences are so different.
Taking ⏲️ to understand what users REALLY care about is important in any setting

- Parking
- Shopping
- Rural livelihood projects #globaldev
- Generating indicators
- Public communication campaigns
- And of course, social science research!!!
Thick data is also necessary for Step 3⃣
👉 Test & refine solutions on small scale B4 rolling out on large scale

Small-scale failures are great for learning
BUT
Large-scale failures (untested before full 🚀) are just costly
Combining #bigdata and #thickdata at appropriate stages of problem-solving = MIXED ANALYTICS

The 4⃣🍀 advantages of mixed analytics 👉
1. Relevance
2. Ownership & empowerment
3. Enhanced communication
4. Mid-course corrections
6⃣ takeaway lessons of combining #bigdata + #thickdata

💡💡💡💡💡💡
Audio summary (first 10 minutes) 👇 with thanks to @charlesjkenny 4 hosting me on @CGDev Sounds Robotic Series

On Lesson 6⃣ - the best solutions are not always high-tech - see my favorite quote from @bijurao of @wb_research #bigdata #thickdata
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