Struggling to align ERP data analytics with organizational goals for innovation?

To align ERP data analytics with organizational goals for innovation, I’d focus on ensuring the data collected is directly linked to key performance indicators that drive innovation. This means identifying the most relevant data points and refining analytics to provide actionable insights. I’d also encourage cross-department collaboration to ensure that data-driven decisions are aligned with both operational efficiency and the organization’s broader innovation goals. Regular reviews of analytics outcomes would help adjust strategies as needed to stay on track.

ERP analytics is going thru its 3rd wave now. Wave1 was Walmart-ian, where they replaced "inventory with information" & became hugely successful. It was product inventory centric, driven by data warehouse technology, pipelines & copies of data. Wave2 was Amazon-ian, driven by consumer fulfillment & faster cashflows. Information became the new currency & based on technologies like cloud, web, NBA, AI/ML. Wave3 is post-digital Prosumer, based on blurring of lines between producers & consumers. Technology components needed [STREAM]: 1) Secured authentication and authorization 2) Timely: On-time, at low cost 3) Relevant to the users role and context 4) Easy-to-action 5) API-driven: API-driven process 6) Micro-semantic insights

Si estás buscando alinear el análisis de datos de ERP con la innovación, una estrategia útil es centralizar y estandarizar los datos en todas las áreas del negocio. Esto te permitirá tener una base de datos confiable para tomar decisiones estratégicas. También, es fundamental contar con herramientas de visualización que permitan detectar patrones y tendencias. A partir de allí, podrías incorporar análisis predictivo para prever necesidades futuras y alinear tus esfuerzos con las metas de innovación. Considera también la retroalimentación de los equipos operativos para afinar la alineación entre tecnología y estrategia.

ERP analytics involves both relational and unstructured data from chat or social media for example. Demand predictions and sentiment analysis enable personalized and proactive customer relations and lean inventory.

Aligning ERP data analytics with organizational goals for innovation is essential yet challenging. Begin by clearly defining strategic objectives and linking data analytics initiatives directly to these goals. Encourage collaboration across departments to integrate diverse insights and foster a data-driven culture. Invest in training to improve data literacy, enabling employees to interpret analytics effectively. Regularly review and adjust your analytics strategies to stay aligned with changing business needs. Finally, leverage advanced analytics tools to extract actionable insights, driving innovative solutions and enhancing overall organizational performance.