Data Driven Decision Making And Analytics Framework 194502

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Data-driven Decision Making data and analytics framework 1 data driven decision making T putting the framework into action 2 define the problem • What is the key opportunity? • Engage stakeholders for perspective and concerns develop hypotheses • Answer ‘what is likely to happen’? • Use information from stakeholders and other knowledge to refine hypotheses • Choose the hypothesis for which the best data exists collect data • Collect relevant internal and external data sets • Validate the accuracy of the data discovery T explore data • Explore data sets to understand how they would help in accepting or refuting the hypotheses analyze data • Use qualitative and quantitative analysis techniques to use data to validate the hypotheses • Convert outputs into user-friendly formats and visualizations that will help different stakeholders understand the analysis insights T link insights • Use actionable data insights to explain past outcomes and predict the future landscape • Link insights to financial and operational metrics to specify impact and aid decision making provide recommendations • Prioritize insights to build actionable plans • Provide solutions that help business to address future challenges actions T execute plan • Develop clear pathways of how insights will be delivered to the right stakeholders at the right time • Ensure the plans meet long-term business objectives and help refine solutions in the future outcomes prepare a data analytics plan from the CASE provided, consider the data analysis framework and the types of analysis as you formulate your answers. the questions below will help you structure your plan: what is the business problem you are trying to solve? what's the first step you'll take? what is your approach to developing a hypothesis? (include your hypothesis) what data would you collect? how would you analyze the data? how would you present the information to the client? what insights did you develop? what recommendations would you make? CASE STUDY A global consumer packaged goods (CPG) company has set an ambitious goal for itself: double revenues while keeping the costs minimal within eight years. of course, the leadership knows that such rapid growth won’t happen with guesswork, tribal knowledge and rough estimates. in order to maximize the performance of thousands of salespeople at thousands of worldwide distribution points, they will need to employ advanced business analytics techniques to analyze massive quantities of data being generated at ever-increasing speed. like all CPG firms, the company faces the challenge of making sure that day in and day out, the right products, in the right amounts, were on the right shelves at the right time. complicating the issue is the daunting fact that the company is working in every corner of the globe. the company deals with a diverse set of global contacts and regional business units who share no common skill set, data platform, or analytics strategies. creating one common platform that can collect incoming data, analyze it, and send insights about inventory and sales issues back into the field to any smartphone-equipped sales rep will be a significant undertaking. powering the sales force to make smart micro decisions that would roll up into better macro performance was absolutely necessary if the company was to stay competitive in its many markets and meet its goals for growth.

Paper For Above instruction

To address the ambitious goal of doubling revenues while maintaining minimal costs within eight years for a global consumer packaged goods (CPG) company, a comprehensive data-driven decision-making framework must be employed. This framework begins with defining the core business problem: ensuring optimal product placement, inventory levels, and sales performance across diverse global markets. The key opportunity lies in leveraging advanced analytics to enhance micro-level decisions that collectively drive macro-level growth. Engaging stakeholders across various regions is essential to understand differing needs, challenges, and data capabilities.

The first step involves thoroughly understanding the current data landscape and operational challenges. This includes identifying data sources from different regions, sales channels, inventory systems, and customer feedback. Once the problem is clarified and the data landscape mapped, the next phase is developing hypotheses. An example hypothesis is: "Optimizing product placement based on regional sales trends will increase sales efficiency and reduce stock shortages." To refine such hypotheses, input from stakeholders — including sales teams, inventory managers, and logistics — provides valuable contextual knowledge.

Collecting relevant data follows, encompassing internal data such as sales transactions, inventory levels, and supply chain metrics, alongside external data like market trends, competitors' activities, and economic indicators. Data validation is critical to ensure accuracy; inconsistent or incomplete data must be cleaned and verified before analysis. Exploring the data involves visualizations and summary statistics to identify patterns, outliers, and relationships that support or challenge hypotheses. For example, analyzing sales patterns across regions and time frames helps determine if product shortages correlate with certain external factors or internal processes.

Data analysis employs both qualitative and quantitative techniques. Quantitative methods like regression analysis, clustering, and predictive modeling are used to validate hypotheses and forecast future trends. Qualitative insights may come from customer feedback, sales representative input, and market research. Converting analytical outputs into user-friendly formats such as dashboards, heatmaps, and infographics ensures stakeholders at all levels can interpret and utilize the findings effectively.

Once insights are developed, they are linked to operational and financial metrics to demonstrate tangible impact. For instance, insights indicating that certain products underperform in specific regions can lead to targeted marketing campaigns or adjusted inventory strategies. Prioritizing insights involves assessing their potential to influence revenue growth or cost reduction, thereby forming the basis for actionable plans.

Implementing these plans requires establishing clear pathways for communication and execution. For example, developing a real-time dashboard accessible via smartphones enables sales representatives to make informed micro-decisions, such as restocking or promotional efforts. These micro-level decisions aggregate to improve overall supply chain efficiency and sales performance, enabling the company to remain competitive globally.

In summary, a structured data analytics plan for the CPG firm involves defining the problem, hypothesizing, collecting and analyzing data, deriving insights, and translating these insights into actionable strategies with ongoing monitoring. This approach ensures data-driven decisions align with long-term growth objectives, enabling sustainable expansion while controlling costs. Overcoming regional disparities through a unified analytics platform will be vital in realizing the company’s strategic ambitions.

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