How do you leverage data and analytics to inform and improve your creative problem-solving process?

In our day-to-day, data and analytics are like our compass for creative problem-solving. It starts with digging into the data to really get what's going on. Think of it like detective work – the data points are clues. To avoid going down the wrong path we also conduct expert interviews to ensure we deduct the right takeaways. Then, when brainstorming solutions, the data's there to back up our ideas or give them a nudge in the right direction. And hey, it's not just about coming up with ideas; it's about making sure they actually work. That's where continuous data checks come in, kind of like keeping a scorecard. This way, it's not just about being creative, but making sure that creativity hits the mark and really makes a difference.

En el proceso creativo de resolución de problemas, los datos y la información son herramientas fundamentales. En la etapa inicial de definición del problema, aprovechar los datos nos permite enfocar nuestras ideas hacia la raíz del problema, maximizando así la eficiencia en el proceso de ideación. Esto evita la pérdida de tiempo explorando áreas irrelevantes y nos ayuda a identificar dónde se concentra el mayor impacto.

Depending on the "problem" in question to be solved, you can also use aggregator software (or engage an aggregator vendor who will do the work for you) to monitor social conversations about a product, brand, topic or problem people share. Aggregator software sends out spiders on the Internet to find keywords, about specific topics in conversation threads.

En el proceso de generación de ideas, el uso inteligente de datos y referencias es esencial para potenciar la creatividad y la innovación. Al contar con información de referencia, como datos, casos de uso e historias de otros sectores o temas, podemos encontrar inspiración que nos ayude a desarrollar soluciones innovadoras. Estas referencias sirven como espejo, permitiéndonos adaptar y capitalizar el éxito de otros en nuestra propia industria. Además, nos brindan la oportunidad de aprender de los errores ya identificados y gestionados por otros, acelerando así nuestro proceso de aprendizaje y evitando obstáculos conocidos.

You must generate ideas, to then leverage data to improve upon the process. You want the system to accomplish the whole process of idea development for efficiency and effectiveness purposes. A great example of a system that enables idea sharing alongside offering analytics and functionality to improve the whole process is DirectSuggest's award-winning employee suggestion box app. DirectSuggest makes it simple for employees to make suggestions surrounding company created categories on any device with the assurance they automatically arrive to the proper decision-maker. Every employee can also collaborate on suggestions to enhance the quality of the idea. Analytics are also provided in real time to enhance the idea generation process.

Data can be read in different ways as they are statistics. To generate wonderful out-of-box ideas, I believe it is important to also give the opportunity to the group to use the data as they see fit. Innovating in the data analytics approach can lead to seeing trends that were not obvisous at first place. Put in place a process to generate ideas and a process to generate data analytics.

Selecting ideas can be based on different factors. The data can help us pinpoint the factors that are relevant for our market. How does the user want to be helped? How can we put more frictions in certain processes? etc. Money-related factors are not always the best but can be a starting point.

En la fase de prueba de ideas, el uso de datos y análisis es fundamental para evaluar la efectividad de nuestras soluciones. Sin mediciones y análisis, las pruebas carecen de sentido, ya que no podemos identificar lo que funciona y lo que no. Los datos nos brindan la oportunidad de revisar el proceso creativo inicial y detectar las diferencias entre la solución propuesta y la respuesta de los usuarios. Esto nos permite ajustar nuestro enfoque creativo para lograr una mejor alineación entre la solución y las necesidades del mercado, impulsando así el éxito de nuestros productos.

When testing the data, we need 3 to 5 KPI that we can follow to estimate the results of our actions. Like in the Taguchi approach, different factors can be tested at the same time but the KPI should be selected and selective to help read the results.

From the data, we can understand better the end-results and refine the idea accordingly. The whole process should be in an iterative approach when the results are used as input for the next iteration. As we can get stuck in a non-ending iterative approach, a clear goal can be defined in the first step with the problem.

In addition to continuously monitoring the on-going campaign through industry-standard tools like Google Analytics, keep using aggregation to maintain a finger on the pulse of online dialogue. Also, if budget allows, assign a team member to monitor social posts; they should remove untrue or misinformed comments and the most negative posts, and respond very selectively to legitimate questions, observations and positive feedback from the target audience.