CREATIVITY:

The good and evil of data, research, and brainstorming

Combining data and research with your creative process is a tricky and frustrating thing for too many people. In this episode, we will look at data, research and brainstorming to see how powerful they can be when used correctly, the reasons why they can completely kill creativity when used incorrectly and the best practices you should use so that all these techniques can strengthen your work.

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Today we are going to talk about a subject that I’ve gotten a ton of requests for and it recently won my Twitter poll for which episode I should work on next. Today we are going to talk about data and creativity. It is an interesting and often difficult time to be a creative as we have more data that influences and affects than we have ever had before. On the one hand all of this data gives us the ability to get insights that can make our work more effective and have a bigger impact than ever before. On the other hand it is also extremely easy to have that data kill a teams creativity with companies that will go ‘data blind’ which means that they lose the ability to think for themselves and they only trust data and what consumer tells them.

Its get even more complicated as data can come in a lot of different forms. Today we are going to look at the basics of data, pro’s con’s and best practices for data, research and brainstorming.

Qualitative vs. quantitative data

Lets start with talking about data and to do that, we need to start with the basics. A lot of creative people don’t know the basics which is just as much of a problem as those people who are blinded by the data because of their lack of creativity.

Quantitative data is

• Data in the form of numbers
• Uses a hypothesis
• Examine the relationship between variables (example put a poster in one pace, not in another and watch the behavior in both places)

Qualitative data is

• Data in form of words
• Explore not predict people’s views
• Explore the meaning of people’s experiences
• Descriptive about exploration

Data

Data by themselves is an inert, raw material.

Pros

• Is one source of truth that shows real results and behaviors
• Get place to find insights to go explore and research
• Many designers need a connection to reality and really understand how their experience are being used and really perform.

Cons

• If all you do is rely on data then you can go ‘data blind’ where you follow that data without question which is a huge mistake.
• Number can be manipulated to tell any story.

Best practices

• Data is just one dimension you need to work with on any project.
• You should start with the needs and insights of people rather than leading with data.
• You can gain insights through the combination of qualitative design research and exploratory data analysis then validate the data results or what you think is the underlying behavior with research with real people.
• This hybrid approach can radically change the user experience for the better and be a true differentiator.

Research

I define research as the methods and practices you use to talk with real customers. I think this is a highly effective but misunderstood and underused tool for creativity.

Pros

• Puts consumers at the center of your work
• Makes creativity a lot less debatable by removing one opinion vs. another

Cons

• Required a strong and talented researcher to be sure their work isn’t biased or leading the research to a set conclusion.

Best practices

• Creativity can be highly subjective so use research to help ground that work and settle a dispute with testing and research on actual consumers.
• Get a diverse group of people to test so you have multiple perspectives
• Concentrate on the amateurs and the professionals as they will often generate the best insights and unmet needs.
• ‘Try to break your ideas not just validate them.

Brainstorming

I define brainstorming as start with research that leads to an insight that you create multiple brainstorm starters to explore different approaches and lines of thinking.

Pros

• Can create real innovation and differentiated thinking that will break with the traditional business as usual
• Can create real organizational change is done correctly as you can bring a lot of different groups to the table to overcome long-standing barriers and problems

Cons

• Ideas can be disconnected from what consumer really want.
• Can drift and end up having a great idea that doesn’t solve the problem they were challenged with.

Best practices

• Ground your brainstorming in research, observation, and unique insights
• Follow my 7 rules for running a brainstorm from episode 18

One constant

The place where we too often go wrong is that we are forgetting where this data comes from – people. This is why things like human-centered design are so important. This data comes from people and is based on real people. In too many cases we are losing that connection back to real people or we think that the data is a direction unto itself. This is when we go data blind and the data becomes the thing that drives all of the thinking and kills creativity. You should start with the needs and insights of people rather than leading with data.

What we need to do

Bring everyone to the table with an equal voice. Ground your work in insights and unmet needs. Data scientists can use sketches or prototypes to get user feedback, just the way product designers do. Don’t be afraid to iterate. A pattern might lead you to look at the data in a particular way which then causes you to look at patterns in a completely new light. Start with a lot of skepticism, as the data could be dirty or missing fields, hence indicating patterns that are off-course.

There are pro’s and con’s to each of these approaches the best approach is the blend all of them. We have to find a balance between creativity and data. We need data so we can ground our work in real problems and have accountability for real results. But we also need vision and creativity to be able to create innovation. Like Henry Ford said if he had asked what people wanted when he was creating the car he would have created a faster horse.

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