Convincing or wining over the stakeholders the following persuasion framework can be used: 1. Logos (Logic): presenting a rational and logical argument backed by proven evidence of data analytics worthiness 2. Pathos (Emotion): Demonstrating the willingness to listen and empathise with the stakeholders concern. This is a softer aspect which can be done through positive attitude, body language and constructive communication. Story telling of other organizations adaptation of data analytics and their journey can address the emotion and values of the stakeholders 3. Ethos (Credibility): Establishing own credibility while advocating for the adoption of data analytics is very crucial. Own real world experience, expertise and integrity is key

To win over skeptical stakeholders, ditch the jargon and paint a picture they can’t ignore. Start with a relatable story; how data saved a competitor from a costly mistake or unlocked surprising growth. Then, hit them with specifics: “Our sales dropped 10% last quarter; with analytics, we’d have spotted the trend early.” Visuals work wonders too; a dashboard showcasing potential wins or an interactive chart that speaks their language. Make it a partnership, not a pitch: “Here’s how this makes *your* goals easier to reach.” Stay grounded in their pain points, and you’ll turn "Why bother?" into "Tell me more!"

Convincing stakeholders of the value of data analytics requires a structured, step-by-step approach. First, align with internal clients to validate relevant KPIs, creating a shared foundation. Next, run a simple pilot to assess the initial impact on KPIs, using results as evidence of analytics’ value. Finally, build on proven improvements by proposing incremental projects, gradually expanding scope. This approach fosters trust, establishes authority, and demonstrates how data can drive strategic decisions and innovation, consistently backed by tangible results.

You need to determine the stakeholder's goals. Showing someone data doesn't mean much unless you know how to tell the story of the data. Find out what is important to the stakeholder and create the story and dashboard for them to show how they can make strategic decisions based off of the data.

O atuação dos stakeholders nos processos decisórios organizacionais é bastante dependente da qualidade das informações gerenciais disponibilizadas para estes atores. Neste aspecto, as técnicas de análise de dados corretas enriquecem e facilitam bastante a tomada de decisão das partes interessadas. Assim sendo, o uso dessas informações por parte dos stakeholders não é apenas uma questão acessória ou facultativa, mas algo fundamental para a atuação destes importantes personagens do ambiente organizacional.

Fala-se em “cenários”, data driven”, “dashboards” e, agora, em Inteligência Artificial. Mas como entregar esse conhecimento? Percebo que é imprescindível levantar e analisar dados. E esse levantamento é feito respondendo: qual a meta? ou qual a dor? Assim, a primeira ação para o convencimento (além dos argumentos comuns de inovação e de digitalização) seria propor um “As Is”, ou seja, levantar dados que permitam a empresa ou setor definir seu momento presente - à luz do negócio. Depois seria a elaboração do “To be”, a partir da meta / dor estabelecida. Por fim a Análise disso! Conduza uma conversa com o gestor e proponha essa análise e considere variáveis: pessoas, processos e a tecnologia a ser adotada. Esse pode ser um bom início!

Listen first. Understand what is underlying a customer’s hesitancy. Cost? Bias? Bad past experience? Political Pressure? What reason(s) does your customer have for not wanting to invest in information to make better decisions. Once you understand this you can shape a solution that addresses both their personal concern and the business need. Customers respond well when they see you have their interests in mind.

En mi opinión y experiencia el analisis de datos, siempre que estén bien extraidos y sean correctos, quitan toda subjetividad a la interpretacion que podemos dar en cualquier sentido. Los datos no son ni buenos, ni malos, simplemente nos determinan con exactitud donde estamos en aquello que estamos midiendo. Siempre he defendido que lo que no se mide no se puede mejorar, por lo que si queremos saber "donde estamos" necesitamos medir y si queremos mejorar, ese mismo calculo es el que debe decirnoslo. El problema que me he encontrado siempre mas que si son valiosos o no, es si son verdaderos o no. Gracias

Identify Stakeholder Goals: Align data analytics benefits with their priorities. Show Tangible Impact: Present case studies or pilot results that demonstrate success. Simplify the Message: Focus on clear metrics and ROI, avoiding jargon. Involve Them Early: Engage stakeholders in defining analytics questions. Offer Quick Wins: Propose small, low-risk projects with visible outcomes. This approach emphasizes relevance, minimizes risk and shows immediate value.