Market Research’s Post

Top Executives do not care about experimentation, a/b-testing, personalization, etc. But what they care about are challenges that could be solved with experimentation and data. So I created this cheat sheet that translates the top 10 challenges of the top management into solutions that are accelerated by experimentation. Why accelerated? Because I am sure that data always helps you to - learn faster what works and what doesn't - so it increases efficiency - while it reduces risks The cheat sheet covers the following challenges: - Adapting to accelerated Technological Change - Economic Uncertainty and Market Volatility - Sustainability and CSR - Globalization and International Operations - War for Talent and Workforce Development - Innovation and Competitiveness - CRM, Customer Loyalty and Engagement - Regulatory Compliance and Governance - Supply Chain Management and Resilience - Health and Safety Regulations It show you for each challenge: - the role of experimentation - detailed advice how to implement it - possible metrics you should use - possible hurdles and how to overcome them - estimation of effort/complexity for each challenge - estimation of impact for each challenge - a short pitch to the c-suite So basically it might be a competitive advantage to implement experimentation into classical, top down monolithic challenges and waterfall projects instead of using this powerful method for button testing on landing pages. If you want to have the cheat sheet please like + comment this post, I'll send it to you as a Google Doc via DM.

As data grows exponentially year after year, it's becoming increasingly important for businesses to effectively capture, analyze, and utilize that data to make informed decisions. To thrive in this environment, organizations must embrace a culture of continuous learning and rapid adoption of insights, and seamlessly integrate modern technologies. However, businesses' most formidable challenge is the ability to adopt new forms of data and derive meaningful insights from it. To overcome this hurdle, organizations must ensure they possess a scalable, secure and, above all, agile data platform capable of accommodating diverse data sources and tools that drive innovation, deliver critical insights, address business challenges and foster growth and innovation. Check out this article to learn how embracing an agile data platform can be a key to business survival today. #dataplatform #businesssurvival #datamanagement #agiletransformation

Harnessing Data for Strategic Decision-Making in Operations Management In today’s fast-paced business environment, the ability to transform complex data into actionable insights is a game-changer. Continuous innovation and enhancement of data processing methodologies ensure that businesses receive the most accurate and insightful information possible. Here are three key trends in data analytics and operations management that are shaping industry standards: 1) Data-Driven Decision Making: Leveraging data analytics to inform strategic decisions is no longer optional; it's essential. By integrating advanced data processing techniques, businesses can predict market trends, optimize operations, and improve customer experiences. 2) The Role of Lean Six Sigma: Implementing Lean Six Sigma principles helps organizations streamline processes, reduce waste, and enhance quality. This methodology not only boosts efficiency but also fosters a culture of continuous improvement and innovation. 3) Agile Methodologies in Data Projects: Applying Scrum and other agile methodologies to data projects allows teams to be more flexible and responsive. This approach enhances collaboration, accelerates delivery times, and ensures that solutions are tailored to meet evolving business needs. By embracing these trends, companies can not only stay competitive but also set new benchmarks for excellence in their industries. What trends are you seeing in your field? How are you leveraging data to drive strategic decisions? Let’s discuss! #DataAnalytics #OperationsManagement #LeanSixSigma #ScrumMaster #BusinessInnovation #ContinuousImprovement #StrategicDecisionMaking

Measuring is an art. Like anything else complex, you have to continue to test and learn. Here are some things to consider. Strategic Alignment: Align the organization's strategic objectives and the measures of success. Ensure metrics used downstream (i.e. team) align, and will enable this success. Ask yourself, "why is this metric important to achieving that success." If you cannot answer it, get rid of it. Make the alignment visible through visual representations (i.e. dashboards) that interconnect metrics across the organizational hierarchy. Outcome Focused: Focus on both operational and business outcomes, ensuring a comprehensive visual representation (i.e. dashboard) to formulate hypothesis, run experiments, and develop concrete action plans. Balanced Approach: Have a holistic view of performance by monitoring various metrics aligned to different goals (Predictability, Time to Market, Quality) to drive informed trade-off decisions and recognize correlations. Data Diversity: Measure non-numerical (qualitative) and numerical (quantitative) attributes so that you can proactively determine the root cause of issues and get ahead of the solution. Actionable Indicators: Effectively use “after-the-event” (lagging indicators) and “in-progress” (leading indicators) measures for decision-making. Data Centralization: Establishing a central repository for metrics analysis serves to unify data and instill confidence in decision-makers, providing easy access to understandable data. Simplification: Standardizing the presentation of metrics fosters a shared understanding among stakeholders, enabling quick analysis for informed actions. Usage: Utilizing metric trends for in-depth analysis and insights enables ongoing improvement and informed decision-making. As an added bonus if you want to gauge how effective you are in measuring, take the Lean Agile Intelligence Inc. assessment. Link in the comments. #agile #agilecoaching #outcomes #metrics #businessagility

🚀 𝗡𝗲𝘄 𝗕𝗹𝗼𝗴 𝗔𝗹𝗲𝗿𝘁: 𝟯 𝗥𝗲𝗮𝘀𝗼𝗻𝘀 𝗬𝗼𝘂𝗿 𝗔𝗴𝗶𝗹𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗲𝘀 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 (𝗮𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗙𝗶𝘅 𝗧𝗵𝗲𝗺) 📊 In today's data-driven world, leveraging data effectively can be a game-changer for your organisation's performance and decision-making. Yet, many organisations face hidden barriers that prevent them from utilising data to its full potential. 🔍 In our latest blog post, we delve into: 𝗠𝗶𝗻𝗱𝘀𝗲𝘁: Cultivating a critical yet open approach to data. 𝗚𝗿𝗮𝘀𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻: Understanding where your organisation stands on the decision-making spectrum. 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆: Fostering a positive data culture that resonates with your staff. ✨ Ready to transform your organisations data journey? Read the full post to uncover these barriers and discover practical solutions to overcome them. Let's turn hidden obstacles into stepping stones for success! 👉 https://lnkd.in/eGkWj_VX #𝗗𝗮𝘁𝗮𝗗𝗿𝗶𝘃𝗲𝗻 #𝗗𝗮𝘁𝗮𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 #𝗧𝗲𝗮𝗺𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 #𝗗𝗮𝘁𝗮𝗖𝘂𝗹𝘁𝘂𝗿𝗲 #𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 #𝗗𝗮𝘁𝗮𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 #𝗕𝗹𝗼𝗴𝗣𝗼𝘀𝘁

Full-stack Telemetry: Insightful Data for Informed Decision-Making Full-stack telemetry is an integral component of contemporary software systems, providing a comprehensive view of the solution's health and performance. The slide presented illustrates the breadth of data that full-stack telemetry encompasses: Comprehensive Data Collection: Full-stack telemetry involves collecting data from all layers of the solution, including front-end interactions, back-end processes, and underlying infrastructure. Front-End Insights: It captures front-end usage data, page analytics, and exceptions, providing visibility into how users interact with the application and where they encounter issues. Back-End Monitoring: The telemetry includes back-end fetches, access requests, and exceptions, offering insights into the server-side operations critical for application performance. Infrastructure Metrics: It also monitors infrastructure utilization, thresholds, and exceptions, ensuring that the system's foundation is solid, scalable, and secure. Alignment with Business Metrics: Importantly, full-stack telemetry ties technical metrics to business outcomes, including Objectives and Key Results (OKRs), Key Performance Indicators (KPIs), Flow Metrics, and hypothesis measurements. This alignment helps to ensure that technical performance translates into business value. Harnessing full-stack telemetry effectively requires understanding both the technical and business implications of the collected data. Through Teach Safe Agile's expert training, teams can learn to leverage full-stack telemetry to make informed decisions that enhance both the solution's performance and the organization's objectives. #FullStackTelemetry #DataDrivenDevelopment #AgileMetrics #TeachSafeAgile

Escape the "track everything" mindset by establishing concise KPIs, prioritising the essential rather than exhaustive tracking (not everything is vital), and conducting iterative analytics. Resist the temptation to constantly monitor everything. Be agile. Track with purpose, understanding and agility! Thanks Taylor Cruz for your article: https://lnkd.in/g3phJqsZ

Full-stack Telemetry Insightful Data for Informed Decision-Making Full-stack telemetry is an integral component of contemporary software systems, providing a comprehensive view of the solution's health and performance. The slide presented illustrates the breadth of data that full-stack telemetry encompasses: Comprehensive Data Collection: Full-stack telemetry involves collecting data from all layers of the solution, including front-end interactions, back-end processes, and underlying infrastructure. Front-End Insights: It captures front-end usage data, page analytics, and exceptions, providing visibility into how users interact with the application and where they encounter issues. Back-End Monitoring: The telemetry includes back-end fetches, access requests, and exceptions, offering insights into the server-side operations critical for application performance. Infrastructure Metrics: It also monitors infrastructure utilization, thresholds, and exceptions, ensuring that the system's foundation is solid, scalable, and secure. Alignment with Business Metrics: Importantly, full-stack telemetry ties technical metrics to business outcomes, including Objectives and Key Results (OKRs), Key Performance Indicators (KPIs), Flow Metrics, and hypothesis measurements. This alignment helps to ensure that technical performance translates into business value. Harnessing full-stack telemetry effectively requires understanding both the technical and business implications of the collected data. Through Teach Safe Agile's expert training, teams can learn to leverage full-stack telemetry to make informed decisions that enhance both the solution's performance and the organization's objectives. #FullStackTelemetry #DataDrivenDevelopment #AgileMetrics #TeachSafeAgile

🚀 Embracing Continuous Process Improvement with Technology! 🛠️ In today's fast-paced business landscape, achieving operational excellence is crucial for organizations aiming to stay competitive. At the heart of this endeavour lies Continuous Process Improvement (CPI), a methodology that harnesses technology to optimize operations and drive efficiency gains. Let's explore how technology empowers each stage of the CPI lifecycle: Identifying Inefficiencies and Opportunities for Optimization: Technology acts as a powerful ally in identifying inefficiencies and optimization opportunities. Tools like PowerBI enable the analysis of vast operational data, unveiling insights and patterns for improvement. Predictive analytics and machine learning anticipate bottlenecks, laying the groundwork for targeted improvement efforts. Planning of Process Improvement Changes: Equipped with data-driven insights, organizations confidently embark on the planning phase. Advanced simulation software models scenarios, facilitating informed decision-making. Collaborative platforms foster real-time teamwork, ensuring alignment and transparency across stakeholders. Execution of Process Improvement Changes: Technology streamlines execution, enabling precise implementation of improvements. Automation platforms like Power Automate reduce manual tasks, freeing up time for value-added activities. Agile methodologies embrace iterative approaches, continuously monitoring results and adapting as needed. Evaluation of Results & Feedback: Evaluation is powered by technology, providing real-time visibility into performance metrics. Advanced analytics dashboards track improvement effectiveness, identifying further optimization areas. Feedback mechanisms capture stakeholder insights, ensuring data-driven and customer-centric improvement efforts. Embracing technology as a catalyst for continuous process improvement unlocks efficiency, agility, and innovation. Let's discuss! What are your experiences with technology-driven CPI? Share your thoughts below! #ContinuousImprovement #OperationalExcellence #TechnologyOptimization

"Agile & Composed Data & Analytics: Navigating the Dynamic Business Landscape" In today's fast-paced business environment, where market conditions and customer needs can shift rapidly, traditional data pipelines and analytics processes often struggle to keep up. This is where the trend of Agile and Composed Data & Analytics comes into play. This approach emphasizes: 1. Flexibility: Data pipelines and analysis tools should be adaptable to accommodate changing business needs and integrate with new data sources seamlessly. 2. Rapid Iteration: Continuous improvement through short development cycles ensures insights are delivered faster and remain relevant. 3. Collaboration: Breaking down silos between data teams, analysts, and stakeholders fosters a more agile and responsive environment. Here's why Agile and Composed Data & Analytics is becoming increasingly crucial: 1. Faster Insights: By adapting quickly to market shifts and evolving trends, organizations can seize fleeting opportunities with timely, data-driven insights. 2. Reduced Risk: Mitigating the risk of outdated data pipelines and ensuring insights remain relevant to the current business context. 3. Improved Decision-Making: Empowering stakeholders to make informed decisions based on the latest data, leading to more effective strategies. Key Practices for Implementing Agile & Composed Data & Analytics: 1. Microservices Architecture: Breaking down data pipelines into smaller, independent components allows for easier scaling and updates as needed. 2. Cloud-Based Solutions: Leveraging the scalability and flexibility of cloud platforms for data storage, processing, and analysis. 3. Automation: Automating repetitive tasks frees up resources for data scientists and analysts to focus on more strategic analysis and problem-solving. 4. Data Governance: Establishing clear data ownership, quality standards, and access controls to maintain data integrity and security. By embracing Agile and Composed Data & Analytics, organizations can transform their data into a competitive advantage. This approach allows them to: 1. Respond swiftly to changing market conditions. 2. Deliver data-driven insights faster and more efficiently. 3. Make informed decisions based on the latest and most relevant data. What are your thoughts on this growing trend? Share your experiences and insights in the comments below! #DataAnalytics #BusinessIntelligence #AgileData #DataPipelines #Flexibility #CloudComputing