How do you prioritize which data sets to analyze when making strategic business decisions?

𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐆𝐨𝐚𝐥𝐬: Determine the primary goals of the business, such as increasing revenue, expanding market share, or enhancing customer satisfaction. 𝐀𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬: Ensure the data sets align with the company's broader strategic priorities and long-term vision. 𝐓𝐚𝐫𝐠𝐞𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: Focus on key performance indicators (KPIs) that directly impact these objectives. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧: Consider the availability of resources, including time, budget, and personnel, to analyze and act upon the data. 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫 𝐈𝐧𝐩𝐮𝐭: Incorporate feedback from key stakeholders to ensure the analysis supports their needs.

Start by identifying your key business objectives. This helps you focus on the data that directly impacts those goals. For example, if your priority is expanding into new markets, analyze market research data and consumer trends. Avoid data that doesn't align with your core goals, as it can distract from critical decision-making. Remember: "Align data analysis with strategic objectives for focused, effective decisions."

Analyze the relevance of each data set to your specific objectives. Consider whether the data set directly addresses the metrics associated with your objectives or provides valuable insights that can inform your decision-making process. Focus on data sets that directly align with your key objectives.

When making strategic business decisions, prioritize analyzing data sets that directly impact your key objectives and goals. Focus on relevant, high-quality data that provides actionable insights for the most critical areas of your business.

In my experience the primary key notes are: 1) The present market trends. 2) The competition analysis. 3) Customer experience (feedbacks, demand etc.) last but not the least the vision of the goals you wants to set for yourself and the compmay objective.

Prioritize data sets that directly impact your desired outcomes. For example, to improve customer satisfaction, focus on data from customer feedback and support interactions rather than unrelated financial metrics. Relevant data offers actionable insights, leading to meaningful strategic changes. Remember: "Focus on relevant data for actionable insights and effective strategies."

Determine the recency of the data sets. Depending on your decision-making needs, you may prioritize recent data that reflects the current business environment. Real-time or near-real-time data can be valuable for time-sensitive decisions.

In today's world where everything we do generates data, it is very easy for companies to 'over analyze' data. Many companies analyze all data to help make there strategic plans and to look for ways to improve business outcomes.. but this often has the opposite effect. By focusing on the core data items needed to run your business, you will cut out the often wasted time analyzing data that has little to no effect on the long term goals. This will then allow you to allocate more time to generating more accurate business valuable data and more time thinking about how to use this more accurate data to best effect in your strategy planning Think.. only because it can be measured, should it be measured and analyzed.?

𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐭𝐨 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: Prioritize data sets that directly relate to the current business context and operational challenges. 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Select data sources known for their accuracy, completeness, and relevance to the decision-making process. 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬𝐬: Prefer data that is up-to-date and reflects the most recent market conditions and business trends. 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 𝐀𝐜𝐫𝐨𝐬𝐬 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: Compare multiple data sets to validate findings and ensure comprehensive insights. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐕𝐚𝐥𝐮𝐞: Assess the predictive value of the data in anticipating future trends and opportunities relevant to strategic decisions.

A lack of high quality data in business does surprise me. Due to analyzing all data (over analyzing), many businesses will start to generate low-medium quality data instead of high quality high impactful data, due to the time needed for all this analysis. Poor data, or rushed data analysis is one hidden enemy of business. Working from or making strategy plans based on low-medium 'rushed' data analysis, is something that will negatively impact your business, and you probably wont know this until it is too late and has effected your business. Taking time to ensure the data your are analyzing will be valuable, useful, of high quality and integrity is very important in this big data world we live in.

Assess the consistency of data across different sources or time periods. Look for discrepancies or contradictions in the data that may affect the reliability of insights. Prioritize data sets with consistent and coherent information.

Implement stringent data quality standards and governance frameworks. Prioritize data sets that are not only accurate and complete but also ethically sourced and compliant with regulatory requirements. Utilize AI-driven algorithms for continuous data quality assessment and enhancement. Deploy AI algorithms to automate data quality checks and flag anomalies. For instance, use machine learning models to detect fraudulent transactions based on patterns in customer purchase behavior.

𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐚𝐧𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲: Prioritize data sets that are known for their accuracy and consistency in measurement and reporting. 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐧𝐞𝐬𝐬: Ensure the data sets are comprehensive and include all relevant variables and dimensions needed for analysis. 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐭𝐨 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠: Select data that provides meaningful insights directly applicable to the strategic decision at hand. 𝐃𝐚𝐭�� 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Consider data governance practices that ensure data integrity, security, and compliance with regulatory requirements. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Validate the quality of data through checks, audits, and comparisons with trusted benchmarks or industry standards.

Utilize advanced analytical tools such as machine learning algorithms, natural language processing, and predictive modeling to prioritize datasets that enable the uncovering of customer sentiments, identification of areas for improvement, and discovery of cross-selling opportunities based on frequent itemsets found in transaction data.

Determine the time lag between data collection and its availability for analysis. Some data sets may have a shorter latency period, enabling faster access to insights, while others may have longer processing times. Prioritize data sets with lower latency to ensure timely decision-making.

After analyzing data, you should always do a sanity check and ask yourself these questions.. 1. Does this data look accurate? 2. Is this data of real value, i.e Will this data help me improve my teams or business performance. 3. Can I trust this data?

Decide on Your Strategy Identify Key Areas and Target Data Collect the Data Analyze the Data Make a Decision about the Data Present Your Findings