Part 2: 4 reasons why we suck at data storytelling
Friska Wirya
I shift resistance into resilience, results & ROI | Top 50 Change Management Thought Leader | TEDx Speaker | #1 Best-Selling Author "The Future Fit Organisation"
This post follows an earlier article I penned highlighting the importance of data storytelling in influencing strategic decisions. So why our shameful track record of telling stories with data? I suggest the following...
- It all starts from education. Wanna bet faculty members presiding over quantitative courses aren't particularly skilled at storytelling? They place more weight on methodologies and formulas than to “waste time” on storytelling approaches. Have you heard feedback that engineering and computer science grads lack interpersonal and communication skills? I'm sure you have. This is why universities around the world (including my alma mater) have incorporated communication skills into their curriculum.
2. Humans can be hard work, and numbers don't talk back. The analytically inclined among us gravitate toward structured, logical, static fields like physics, math and statistics, preferring to interact with facts and figures over human beings. Yep, before you start jumping up and down, I realise this is a sweeping generalisation and there are inevitably a few analysts who won't fall into this bucket. Can we just acknowledge that telling compelling stories isn't second nature to most quantitative analysts? Great.
3. Skewed cost/benefit analysis of investing in storytelling. Some believe creating an engaging story is a less valuable investment of time compared to technical capability and data analysis.
Fact: many people can tell good stories, but only a select few can run a logistical regression model, use convolution methods to understand the behaviour of sums and random variables, and describe oscillatory phenomena arising in mechanical and electrical systems.
Quantitative analysis maximises the output of an analyst's time and intellectual horsepower. No arguments here. But relying on others to translate analytical results into compelling stories has its own downfalls, in addition to being labourious to oversee and manage.
4. I don't have a square to spare. It requires a substantial chunk of analysts’ time, energy and brain capacity to think creatively about how to tell a good story with data. An analytically-inclined contact shared he and his team regularly committed as much as 60% of their time towards devising the optimal way to communicate the results of his analysis to key stakeholders. We're all leading busy lives. I get it. Many analysts will be reluctant - or even have that much time - to devote to the issue.
I've articulated why storytelling with data is critical to bringing about a significant step change in organisational performance, and several reasons why it doesn't happen often.
Got it? Good.
Suggestions on improving the persuasiveness of your data-based stories will be in the next post.
"Without data, you're just another person with an opinion." W E Deming
Like what you read? More of my musings below...
- 8 reasons why being a change manager makes you mentally tough
- 5 things NOT to do in a digital transformation
- Part 1: 4 reasons why data-based story is key (but we suck at it!)
- Part 1: 7 signs of a toxic workplace
- Part 2: 7 signs of a toxic workplace
- Servant leadership's role in imparting change
- 10 leadership lessons from the front line of policing
- 5 infuriating stakeholders and how to deal with them
- 3 ambivert advantages - a change manager's insights
- 5 ways change management increases organisational 'health'
High Performance Teams ? Leadership ? Wellbeing ? GAICD
8 年Thanks for sharing Friska. Data is very important and many teams under utilize the data they have or simply don't collect it!
CTO, co- Founder at SharpCloud Software
8 年Great post Friska - so true. As a graduate engineer, I spent the first 15 years of my working life building analytical software that focussed almost entirely on creating data (Monte-Carlo analysis for Projects on time and cost - called Pertmaster - now part of Oracle). The models got bigger and more complicated, but not necessarily more useful – like me, technical users love to focus on the details (we called it 'analysis paralysis'), but often we would hear that decision makers (possibly being more political) would ignore the results. Sometimes the models were poor, but often it was because the results were poorly communicated or they lacked context (there is always more information that could be included in the models). I think that context and relationships are key for good storytelling. As I got a bit older (and possibly wiser) my co-founder and I realised the communicating your data was as important, if not more important than the data itself, and we had an sort of epiphany. We have spent the last few building a new visual way of storytelling around the data you already have for portfolios, risks, roadmaps etc. Keep up the great work on your posts, and if you have time check out SharpCloud.