How can Writing professionals improve problem solving through data analysis?

Data analysis is about uncovering layers of information to reveal insights at various levels. Personally I think Data analysis basics is the very root for any analysis. For example I was working a project that had to do with customer contact analysis at my previous role. I spent a huge chunk of time in preparing and organizing the data. This step helped me in realizing that some of the deliverables from the stakeholders were not going to be achievable because the available data did not have the data that will help me in achieving some of the insights the stakeholders wanted.

Writing professionals can enhance problem solving through data analysis in several ways. By analyzing audience data, they can identify trends and preferences, tailoring content to meet reader interests. Data-driven feedback helps refine writing styles and topics, ensuring content is relevant and engaging. Understanding market dynamics through analysis aids in positioning content effectively against competitors. Employing tools like Google Analytics or SEO platforms provides actionable insights, enabling writers to make informed decisions and improve their work's impact.

Puedes encontrar patrones en cualquier lugar, identificar necesidades, tipos de clientes (con alto o bajo umbral de acción...) los datos, son estadísticas y probabilidades, una matriz bien identificada, puede aportar mucho valor.

Observar donde se crea tendencia y donde se está quedando obsoleto los datos (periódicos Vs contenido digital), para explotar el mercado, es lo adecuado. Estar a la última y reciclarse, es ventajoso y los análisis te pueden dar mucho soporte.

Research at it core should be used to help with decision making. Data research is not different it allows the users to access more data from various resource on demand. The use of data analysis in research in my experience helps to gain data from history, industry standards, lessons learns, and recommendations based on the need of the researcher. Notable professions must utilize the tools available to them for effective use data process along several model for instance the DIKW model.

This area is a very key one in my job. I have had the opportunity of speaking and communicating findings of my analysis to stakeholders at very different levels. One key takeaway that has worked for me and would love to share is making your insights/findings to be very granular & in the simplest form possible. I assume everyone is non technical and ensure that key message is passed across effectively. Effective communication is also a very important one as I use very simple of communicating without including technical words. I also engage stakeholders with questions to make it interactive when presenting to ensure everyone is understanding the outcome so that everyone can improve in their different areas based on the findings.

Valoro que aquí entra en juego una técnica de manipulación, porque decir lo que se quiere escuchar, a través de la interpretación de datos, es algo un tanto desleal, no me gustaría que me tratarán así.

The challenges in data analysis can come in different forms but the one I would emphasize on is the importance of ethical considerations in data analytics and striking the right balance between utilizing data for insights and respecting privacy & ethical boundaries. Using AI chatbots improves efficiency and can be handful if used in the right way. Nowadays, alot of people make use of chatbots and forget to psedudomise the data which can lead to breaching of data privacy. One way organizations can reduce this is to have an inhouse chabot which employees can use without fear of breaching data privacy.

It is important to look beyond and behind the data which you have gathered and pieced together. Do not fall into the trap of presenting the data on the same level of your analysis or visualisation. Keep in mind your "call-to-action" or the end goal of your analysis. Most of the time, your stakeholders will ask "ok, you have shown me nice chart/data, SO WHAT?" this is the big question you will need to answer.

Tiene que haber un balance, siempre hay pautas de conducta que se repiten mucho, pero de las que se aprende verdaderamente, son de las 1 de cada 100, esa peque?a pauta que destaca al resto, analizar ese tipo de datos, son los que te pueden hacer destacar de verdad.

In my previous role, a major challenge faced by the analytics team was issue of data quality. Analysis tends to become more difficult and inaccurate when the data is of poor quality. Often, the accuracy and reliability of insights depend on the quality of the data being analyzed. Investing in robust data collection methods, ensuring data cleanliness, and implementing effective data governance practices can enhance the integrity of analysis. Every organization needs to have a standardized data because this will ultimately lead to more trustworthy and actionable insights.

La acción sin emoción, no sirve de nada, el motor que empuja a hacer las cosas bien, está en la pasión que se pone, en no esperar nada a cambio. Es muy importante para ti, sentirte realizado con lo que vives y haces.