How can data support your software consulting recommendations?

Data analytics is the process of collecting, analyzing, and interpreting data to generate insights, solve problems, and make decisions. As a consultant, you need to use data analytics to support your recommendations and communicate them effectively to your clients.

Data can significantly bolster software consulting recommendations by providing a concrete analysis of the current situation. In my experience, leveraging data analytics to evaluate the client's existing software systems, processes, and performance metrics uncovers inefficiencies, bottlenecks, and opportunities for improvement. This objective evidence supports tailored recommendations that directly address identified issues and align with the client's strategic goals. One thing I've found helpful is to present data in a way that highlights trends, patterns, and correlations, making the case for why certain changes are necessary. A mistake to avoid is making recommendations based solely on intuition or generic industry best practices.

Starting with understanding the context is key. Data without context can be highly misleading. Once the context is done, is this important to assess the accuracy and reliability of the dataset available.

Algunas ideas para analizar la situación actual y respaldar los datos antes de proponer cualquier cambio o mejora en el software del cliente podrían ser... - Utilizar datos históricos y análisis de tendencias en LinkedIn para comprender el estado actual del mercado y las necesidades de la industria relacionadas con el software en cuestión. - Examinar datos internos de la empresa cliente, como métricas de rendimiento del software, retroalimentación de los usuarios y problemas identificados, para comprender los desafíos actuales y las áreas de mejora.

É crucial iniciar compreendendo o contexto. Dados desprovidos de contexto podem facilmente induzir a equívocos. Após estabelecer o contexto, torna-se essencial para avaliar a precisão e a confiabilidade do conjunto de dados disponível.

Data can be a valuable support for software consulting recommendations in several ways. For example, by analyzing data on development costs, business benefits achieved, and other related factors, you can evaluate the ROI of your software and identify opportunities to maximize the value of your business' software investment. Or, using comparative data from competitors or the industry, you can evaluate the performance of your software against that of the competition and identify areas where you need to improve to remain competitive in the market. Ultimately, using data can provide a solid foundation for software consulting recommendations.

Objectives have to be different depending on if the analysis is on qualitative or quantitative data; each type serves a different purpose. Also, it is important to find a balance between a comprehensive scope and a realistic one; in the private sector being pragmatic is a must if you expect to come with solutions fast enough.

Una vez analizada la situación actual... - Analizar datos de la comunidad de LinkedIn y utilizar la inteligencia artificial para identificar las mejores prácticas y las tendencias emergentes que puedan ayudar a definir objetivos realistas y alcanzables. - Utilizar datos internos y externos para establecer métricas claras y medibles que guíen el éxito del proyecto de consultoría.

Os dados desempenham um papel crucial ao fornecer suporte valioso para recomendações de consultoria de software em diversas frentes. Ao analisar dados relativos aos custos de desenvolvimento, aos benefícios comerciais obtidos e outros fatores pertinentes, é possível avaliar o retorno sobre o investimento (ROI) do seu software e identificar oportunidades para maximizar o valor do investimento em software da sua empresa. Além disso, ao utilizar dados comparativos dos concorrentes ou da indústria, é viável avaliar o desempenho do seu software em relação ao da concorrência e identificar áreas que necessitam de aprimoramento para manter a competitividade no mercado.

I feel there should be steps 2A and 2B: in this article, we jump straight to solution design, whereas we should spend a lot of time to fully understand the root cause of the problem. Often surface-level responses to why something is an issue, are not covering all of the problem. Use techniques like "5x why" and the fishbone diagram to dig deep and find the root cause. Then you will be confident you're solving the right set of problems, and your countermeasures have a higher chance of success of achieving their desired effects.

Even though we are software experts, we should think on the solution as hypothetical: "What if we have a platform that can solve impact 1, impact 2 and impact 3) and create the value expected?. Then link the software /services components that we know fit that value sentence. Finally identidy the gaps and constraints of your available solutions to fit that requirement, or customize or pivot. Once solution is chosen, make sure to write it down on a diagram, showing a vision of how users will interact and how outputs will become to the expected results.

En relación al diseño de la solución: - Utilizar datos de la comunidad de LinkedIn para identificar expertos en el campo específico del software y colaborar con ellos para diseñar soluciones efectivas. - Emplear análisis de datos internos para comprender las limitaciones técnicas y las capacidades existentes del software, lo que ayuda a diseñar soluciones adaptadas a la situación única de la empresa cliente.

Start with the end in mind and get to the he design of the solution by breakdown the goal to the steps required to get to it. Once you have these steps, you can break those down into finer sub-steps to produce a more comprehensive design and plan.

Inicie com uma visão clara do objetivo final e, a partir dela, elabore o desenho da solução, delineando cada passo necessário para alcançá-lo. Uma vez definidos esses passos, é possível decompor cada um em sub-etapas mais específicas, contribuindo para a criação de um design e plano mais abrangentes.

Es imprescindible abordar el diseño del proyecto con datos del cliente para poder validar el mismo. El comportamiento de un software con datos ideales no será nunca fidedigno.

But it end the the plan, design and analysis is just as good as the people that implement it. So that the implementation follows suit with an excellent design, implementors need to have the right preparation and training. If you want to know more about Effective Training and Developement practices feel free to check my profile, I have developed an effective T&D model for PS organisations in the Software industry. If you have any questions let me know, will be more than happy to assist

I stress the importance of leveraging data to inform and support recommendations during the implementation phase. By analyzing relevant data points, such as user feedback, system performance metrics, and market trends, consultants can make informed decisions about the most suitable solutions for their clients. This data-driven approach ensures that recommendations are tailored to meet the specific needs and objectives of the project, leading to more successful outcomes. Additionally, ongoing data analysis throughout the implementation process allows consultants to identify potential challenges or areas for optimization, enabling them to adapt their strategies accordingly and ensure the long-term success of the solution.

Recibir feedback continuo del cliente, de las versiones entregadas es primordial para seguir una linea de desarrollo que garantice el éxito.

Una vez implementada la solución, deberemos evaluar los resultados: Analizaremos datos internos y externos para evaluar el impacto de la solución implementada en términos de rendimiento del software, satisfacción del usuario y otros indicadores relevantes. Por otro lado, los datos de la comunidad de LinkedIn para recopilar opiniones y retroalimentación de expertos en la industria sobre la efectividad de la solución y posibles áreas de mejora.

Após a implementação da solução, é crucial conduzir uma avaliação dos resultados alcançados: Internamente e externamente, analisaremos dados para avaliar o impacto da solução implementada, considerando o desempenho do software, a satisfação do usuário e outros indicadores pertinentes. Além disso, faremos uso da comunidade do LinkedIn para coletar opiniões e feedback de especialistas do setor, buscando insights sobre a eficácia da solução e identificando áreas potenciais para melhorias.

By Leveraging data, we can identify areas for further improvement and innovation in the client's software and business. Further, by analyzing results, we can recommend the next steps and opportunities, showcasing the potential of our solutions through scalability, adaptability, and integration capabilities. Additionally, we can propose ongoing consulting services tailored to the client's evolving needs, negotiating terms and conditions based on the demonstrated value of our partnership. This data-driven approach ensures not only continued success but also fosters a long-term, mutually beneficial relationship with the client.

Utilizar los datos recopilados durante todas las etapas del proceso para recomendar acciones futuras, como actualizaciones adicionales del software, integración de nuevas tecnologías o ajustes en la estrategia de negocio. Además, podemos compaginarlo usando la inteligencia artificial para prever posibles problemas futuros y recomendar medidas preventivas.

Shortly, you cannot prove your success if there is no relevant data. You must have all the necessary data of the current system and then collect the same data from the new one. Please don't do it right after going live; give it some time so the users get used to the new software and start to perform, rather than learn. Learning process takes time, and it is not fair for the new solution to be assessed with the one which already has passed the stage long ago. The next step should be pressing on everything new added to the new software, usage rates, response times, data quality, security etc. Use visual aids, keep them simple but nice. Prepare a detailed report but present the highlights. Share the detailed report some other time.