How can data and analytics identify conflicts in your partnerships?

-Data is the most important if it is backed with facts. One of the usual trick I use with my partners is that I give the data and analyse together. This will helps partners to dive in to topic and to feel close and increases the relation.

Benefits of Data and Analytics: Objectivity: Data offers an unbiased view of the situation, eliminating the subjectivity of personal perceptions. Early identification: Analytics can detect patterns or trends that indicate potential conflicts before they become major problems. Basis for communication: Having tangible data allows involved parties to discuss conflicts more accurately and constructively. Informed decision-making: Insights derived from analytics help you make decisions about how to approach and resolve conflicts effectively.

Throughout my career, I have worked in multidisciplinary teams to solve diverse problems relating to public sector and private enterprises. I advocate for evidence-based decision-making in the design and management of systems involving people, processes, and products/services. Example - policy to reduce waiting times in health delivery. The partnership was between the custodian of the data (NHS Scotland) and the Government (who set the priorities). The team used evidence from the data to understand and agree on the problem and worked together to define policy objectives and key indicators for measuring success, including time frames, roles, and responsibilities for delivery and monitoring progress (including data quality).

Data Sources and Analysis: Performance Data: Performance metrics for activities carried out together. Customer Feedback: Customer opinions and comments on the partnership’s performance. Satisfaction Surveys: Direct assessments of the satisfaction of those involved in the partnership. Financial Data: Relevant financial information, such as costs, revenues and profits. Consumer Behavior: Consumer behavior patterns that may affect the partnership.

As the popular saying goes - garbage in, garbage out (GIGO), data quality is key for robust decision-making more in partnerships - for which behaviors like trust, and collaborations are key. Working together to develop a driven mindset and agree on areas where and how data can solve problems - both parties care about. Data sources, including customer feedback, benchmarks (competitors, peers' performance), customer orders, and complaints/satisfaction data can be used to monitor performance and manage accountabilities in the partnership. A colleague survey can be undertaken periodically to assess the ways of working and identify any gaps and problem areas that need improvement. The analysis and tools used will largely depend on the data.

While having data from several sources is important for a 360 view, it must be gathered with the quality over quantity mindset. Something not discussed often in partnerships and data, finding ways to quantify the intangibles. What I mean by this is you may have a partner that doesn't make the same monthly sales as someone the same size, but they may be able to connect you with better quality customers for long term sustainability.

It is important to have data from several sources to get full knowlege about customers and this will ultimately corelate with partenership

Types of Conflicts: Conflicts of Interest: Divergences between the objectives of the parties involved in the partnership. Communication Conflicts: Failures in communication that lead to misunderstandings or unmet expectations. Resource Conflicts: Disputes related to the inappropriate allocation of resources such as time, money or personnel. Personality Conflicts: Individual differences that cause friction between the parties. Conflicts of Expectations: Disagreements about what each party expects from the partnership.

Steps to Resolve Conflicts: Identification: Recognize the existence of the conflict and its underlying causes. Communication: Open effective communication channels to discuss the conflict openly and honestly. Data Analysis: Using relevant data and analysis to better understand the situation. Negotiation: Seeking mutually acceptable solutions through negotiation and compromise. Implementation: Putting agreed solutions into practice. Follow-up: Continuously monitor the situation to ensure solutions are effective and adjust as necessary.

Understanding the problem to solve using data (the why?) is critical, ensuring focus and alignment across teams, and partnerships. Clear about data sourcing and assumptions around the scale, and scope of the data to be collected and consumed in the analysis. Ensure all relevant stakeholders who are involved in the partnership have a data-driven mindset and are engaged in the end-to-end processes involving data - from collection through to analytics (value add). Data supports the knowledge base for the system - people, processes, and products/services. Timely collection and use will ensure robust decisions, reducing conflicts. Data handling should follow protection rules and any relevant regulations.

Best Practices for Data and Analytics: Regular Data Collection: Establish systems to regularly collect relevant data about the partnership. Weighted Analysis: Conduct careful and unbiased analysis to obtain accurate insights. Transparency: Share results and analyzes transparently between the parties involved. Flexibility: Be willing to adjust strategies based on data insights. Continuous Update: Keeping up to date with changes in data and circumstances to avoid future conflicts.