A continuación, te explicamos cómo puedes impulsar la innovación en tus proyectos utilizando datos y análisis.

Data and analytics usually will not drive innovation. Innovation is driven by creativity, thinking outside the box and recognizing an opportunity when it presents itself.

Systematic investigation and experimental data analysis are crucial for driving data driven decisions and productive solutions. We found pristine Pareto Principle, the 80/20 rule - achieving 80% results from 20% efforts is an effective tool for fostering creative problem solving tasks. 🕸🦋🕸 #VisionCreatesValue

The biggest skill needed for analysing process data is the chemical process understanding and knowledge of a particular plant. Unit Process and Control engineers already have this so give them the ability to apply their knowledge quickly and easily so that they can advise operators and Unit and Site management how to operate the plant more efficiently now and in the future. What was the best operation achieved across hundreds of process variables and months to years of process history combined with LIMS, KPI and Emissions history? Construct the Operating Envelope (takes minutes using n-d geometry); look at the operation outside the envelope; explain why it was allowed to occur. Compare visually with your MPC Constraint Envelope.

Data analytics help you to take right decision and make right strategy. Its very important to take logical decision backed by data ( Be its past prduct failure, field data, competitor analysis).

We used to use Minitab. Now Power BI is the favorite. I’ve also had good luck with teammates who can pull together customer dashboards to communicate key business metrics. It’s all about what’s important to the business and customer and how to you show the data real time to be able to make decisions in a timely manner.

Recent favorites: - PowerBI co-pilot: simply typing on data insights requests, it'd generate line charts, etc. for you on the dashboard - Looker Studio: simple charts to be built out by drag-and-draw features - Streamlit: open source python package that offers customized UI & viz to be built, some python code with the most flexibility

There's one more software Orange that is quite useful for data visualization. It has a simple drag and drop module to see the desired data.

Foster an environment where creativity and brainstorming are encouraged. Create platforms for team members to share ideas freely and explore unconventional solutions.

Las herramientas son importante en la construcción, seguimientos de proyectos o procesos operacionales para estar dentro de los parámetros establecidos. Analizar la información reuniones tomar decisiones en función de base de datos finales en crucial.

Customer is "King" rhythms not only well in words but for service too. Sometimes Yes and a smile with fingers🤞 for requirement needs to be applied.

I would say 90% of the time start with a customer need in mind unless you are a research organization that has a specific goal in mind. Things like I want to make my customers experience of the product better or I want to improve our Customer Acquisition cost, improve retention metrics etc. Then start thinking about the various inputs and outputs that you need to measure, collect that data if available or instrument the required data collection. Tooling to ingest, analyze and visualize can come in various shapes and forms and what you use or pick is dependent entirely on your organizations situation.

Innovations have to address needs and customers have to be at the core of those needs. Otherwise the innovations run the risk of becoming great ideas with no real benefit. Encourage all areas of the organization to focus on customer needs and prioritize based on impact, resources and timeline. Eventually you want innovations to impact your position in the market and that can come from addressing needs by listening to external and also internal customers in all areas.

Customer or stakeholder insights is one aspect of an effective project. At times, customers get a “sticker shock” when they find out the cost and timeline that can deliver ALL that they want. Consequently there are communications and may be comprises. Assemble the best technological and people oriented team and work closely with the stakeholders (customers and finance folks).

It is critical to understand the customer needs for a successful innovation into a market. Some basic but fundamental questions charts a pathway to creating solutions that satisfies those needs. What are customers trying to achieve? What are customer trying to avoid? How important is it to customers? How satisfied are they with the current solutions available to them?

Here’s my 2 cents 1. Iterative Development: Break projects into small, manageable phases.Deliver value continuously with each iteration. 2. Data-Driven Decision Making: Gather and analyze performance data after each phase.Use data to identify successes and areas for improvement. 3. Continuous Evaluation and Adjustment: Regularly reflect on project progress and outcomes. Hold retrospectives to discuss improvements for the next iteration. 4. Culture of Experimentation: Encourage testing new ideas and approaches. Learn from both successes and failures to inform future actions. 5. Rapid Innovation: Adapt quickly to changing requirements and insights. Maintain flexibility to pivot based on evidence.

It's all about experimentation, learning and iteration. If you are not leveraging your data to make quick and assertive experiments with your product, you are probably not getting anywhere close to achieve your expected results. Data is a very powerful tool, but only if used correctly. Otherwise, it can become destructive and toxic. An agile (or iterative) approach, would help weed out bad or anecdotical data, and allow us to focus on the core findings, or to quickly correct our previous assumptions.

Alineado con las metodologías Ágiles y la dirección de proyectos, las reuniones semanales o mensuales permiten un espacio de gran valor para escuchar ideas innovadoras que mejoran los procesos, los datos y hechos recolectados en los avances de cada iteración o reunión son la mejor fuente para implantar mejora continua basada en lecciones aprendidas.

Los proyectos deben tomarse en pequeñas secciones para luego unirlas y formar un todo. La información de cada paso y progreso del proyecto nos dirá como estamos en función de lo predestinado y poder corregir las partidas afectadas. Importante comunicación transversal.

Adopt agile methodologies: Implement iterative development cycles (sprints) to continuously improve your project based on real-time data. Foster a culture of experimentation: Encourage rapid prototyping and testing of new ideas to quickly identify what works and what doesn’t.

It is important here to review all customer complaints, service data for warranty claims and other repetitive failure modes. This would shed light on areas of improvement, innovation needs and also risk mitigation.

Es importante la experiencia para evaluar las posibles contingencias, riesgos, tiempos perdidos, etc. Las tomas de decisión van amalgamados con información, datos, registros, reuniones, tomas de decisión y aplicar re- ingeniería si amerita.

Eine gute Integration von Risikomanagement geht weiter und implementiert funktionierende Begrenzungs- und Vermeidungsstrategien bereits in den Arbeitsalltag aller Projektmitarbeiter, indem bei der Abstimmung von Vorgehensweisen und bei Entscheidungen das, was schiefgehen kann, direkt berücksichtigt wird. Beispielsweise kann ein contingency plan oder ein expliziter Handshake an den Schnittstellen das Eintreten oder die Auswirkung der Risiken verringern. Täglich. Und nach einer Weile wird das sogar in den Daten sichtbar.

Eine gute Integration von Risikomanagement geht weiter und implementiert funktionierende Begrenzungs- und Vermeidungsstrategien bereits in den Arbeitsalltag aller Projektmitarbeiter, indem bei der Abstimmung von Vorgehensweisen und bei Entscheidungen das, was schiefgehen kann, direkt berücksichtigt wird. Beispielsweise kann ein contingency plan oder ein expliziter Handshake an den Schnittstellen das Eintreten oder die Auswirkung der Risiken verringern. Täglich. Und nach einer Weile wird das sogar in den Daten sichtbar.

Perform risk analysis: Use data to identify potential risks and their impact on your project. Develop mitigation strategies: Create data-driven plans to address and minimize risks before they become critical issues.

Like any other measurement, the future will be combination of random and systematic effects. We rely on weather predictions to tell us when to bring a coat or umbrella. Predictions aren't always right, but are often enough to be useful. We use past data to characterize the systematic effects so that we can better calibrate future predictions. One example is the use of drawing completion data as a means to predict if a project will be on schedule. We use reliability data to predict how long a component will last before failure. We use design maturity factors to predict the weight of a system. Organizations that predict the future more accurately will have a competitive advantage.

Data and analytics are essential for meaningful benchmarking, especially when developing the selected alternative/FEED stage. This allows for demonstration of the best quartile benchmarked performance and, if not, prompts the question of why not. It promotes innovation in design, construction and execution factors to improve the Key Performance Indicators to exceed the desired results and deliver a competitive edge. Benchmarking improvements should always be data-driven; however, innovative design can have qualitative, competitive edges that carry risk and require experienced judgment. Be realistic in schedule data. Look for innovative, doable scheduling, albeit as a stretch, as time carries costly risks and erodes a competitive edge.

Benchmark against competitors: Use competitive analysis to understand market positioning and identify areas where you can innovate. Track industry trends: Stay informed about the latest developments in your field to ensure your project remains relevant and ahead of the curve.

Utilize dados para identificar oportunidades únicas e desenvolver soluções que proporcionem um diferencial competitivo significativo no mercado.

The purpose is to do things in more effective way than earlier methods to get same or better outcomes from the projects with less cost and time by maintaining quality. So your lesson learned play important roles. New methods and oppotuntiies should be explored using modern trends of the time. Adoptibilty to change is key factor here project team should be taken on board and motivated to accept changes and transotions from old norms to new norms.

Data Governance: Ensure data quality, security, and compliance to maintain trust and integrity in your analytics processes. Collaboration and Communication: Promote cross-functional teamwork and transparent communication to integrate data insights seamlessly into your project workflows. Continuous Learning: Stay updated with the latest advancements in data analytics and encourage your team to develop their skills continuously.

If its innovation then rather than going the route of data to decisions, we should look at the reverse way and see what decisions are we trying to take and accordingly decide what analytics capability is needed and in turn whats the data requirement. This would help in true innovation breaking out of the conventional ways.

Explore novas fontes de dados, mantenha-se atualizado com as últimas tendências tecnológicas e esteja pronto para adaptar suas estratégias conforme o cenário evolui.

consider different factors that may affect your work. always think for the possible outcome and always think of the worst case scenario and make the possible countermeasure and corrective actions. always do the PDCA (plan-do-check-act).