How can you balance data source cost and value when making acquisition decisions?

For example, acquiring data from a proprietary financial market dataset, which provides real-time stock prices, historical financial data, and market analysis, may require a subscription fee that could range from hundreds to thousands of dollars per month, depending on the depth and breadth of the data required. Additionally, integrating this data into your system might involve costs related to developing and maintaining the necessary software interfaces, data storage infrastructure, and ensuring data quality and compliance with financial regulations. On the other hand, using open data sets provided by government agencies, such as the European Union's Open Data Portal, would incur minimal direct costs.

Consider: - Cost-Benefit Analysis: Evaluate data source value against acquisition costs - Strategic Alignment: Align sources with business objectives and goals - Data Quality: Ensure reliable, quality data in decision-making - Scalability: Assess scalability without disproportionate cost increases - ROI Calculation: Calculate return on investment per source - Data Governance: Ensure compliance with standards and regulations - Integration Complexity: Evaluate integration complexity & cost - Innovation Potential: Assess potential for innovation & new opportunities - Collaboration: Involve key stakeholders for diverse perspectives Balancing these factors optimizes decisions on source acquisition, considering the trade-off between cost & value

For example, a company aimS to improve its product offerings through customer feedback analysis. High-value data for this company would come from sources like direct customer surveys, product review sites, and social media feedback, where the information is specific, current, and directly relevant to the company's products. This data can provide actionable insights into customer preferences and pain points directly informing product development and marketing strategies. But, low-value data for the same project might include outdated customer feedback or reviews for unrelated products, collected from forums or websites that no longer align with the company's target market. Such data might be irrelevant, not actionable, or misleading.

Other data sources are perceived low value because their the value is not yet clear. Requires a sophistication in the business for the value to be unlocked.

When evaluating data sources to weigh up the costs and benefits, you should consider: Monetization potential of the data: Evaluate the potential revenue or cost savings that will result from using the data. Data quality and relevance: Evaluate the accuracy, completeness and relevance of the data to your specific use case. Sample testing: Conduct sample testing to assess the effectiveness of the data in achieving the desired results, e.g. personalizing emails to increase click-through rates (CTR). Cost-benefit analysis: Compare the acquisition cost of the data with the potential value it can deliver in terms of insights and actionable results.

Al comparar el valor de una fuente de datos, se debe medir los siguientes puntos: > Que datos se utilizan en sus actuales procesos lucrativos según los planes de la empresa. > Que datos se podrian utilizar para nuevos procesos lucrativos según los planes de la empresa. > Cuantos nuevos recursos y tiempo de desarrollo se requiere para incorporarlo en los sistemas de la empresa: almacenamiento, ingesta, metricas de calidad de datos. El valor de la fuente depende de como los procesos lucrativos actuales y futuro requieran componentes de analisis operativa y predicción; pero también saber que involucra a todas a nivel de costos estos puntos, esto para ver alternativas a los procesos o las opciones a usar.