For many businesses and government agencies, lack of data isn’t a problem. In reality, it’s the opposite: there’s often too much info available to make a clear decision.
With so much data to sort through, you want something more from your data:
Have to know that it’s the right data for answering your question;
You Have to draw true conclusions from this data; and
You need data that informs your decision-making process
In brief, you will need better data analysis. With the proper data analysis procedure and tools, what was once an overwhelming quantity of disparate info becomes a simple, clear decision stage.
Step 1: Establish Your Questions
In your organizational or business data evaluation, you must begin with the right question(s). Questions must be measurable, clear and concise. Design your questions to either qualify or disqualify prospective solutions to your specific problem or opportunity.
Step 2: Determine What To Measure
Utilizing the government contractor example, consider what type of data you’d need to answer you’re a crucial question. In this instance, you’d need to be familiarized with the number and cost of present staff and the percentage of time they spend on necessary business functions. In answering this question, you likely will need to answer many sub-questions (e.g., Are staff currently under-utilized? If so, what procedure improvements would help?). Lastly, in your decision on what to measure, be sure to incorporate any reasonable objections any stakeholders might have (e.g., If staff is diminished, how would the firm respond to surges in demand?).
Step 3: Collect Data
Before you gather new data, determine what info may be gathered from existing databases or sources on hand. Collect this data. Determine a document storing and naming system in advance to aid all tasked team members to collaborate. This procedure saves time and prevents team members from amassing the same info twice. If you need to gather data via observation or interviews, then develop a meeting template in advance to ensure consistency and also conserve time. Maintain your gathered data organized in a log with collection dates and add some source notes as you proceed (like any data normalization done ). This practice divides your conclusions down the road.
Step 4: Analyze Data
Once you’ve collected the right data to answer your question from Step 1, then it’s time for deeper data analysis. Start by manipulating your data in many different ways, like hammering out it and finding correlations or by creating a pivot table in Microsoft Microsoft Microsoft Excel. A pivot table lets you sort and filters data by different factors and lets you figure out the mean, maximum, minimum and standard deviation of your data.
Step 5: Results
After assessing your data and maybe conducting further research, it’s time to translate your results. As you translate your analysis, bear in mind you can not ever prove a hypothesis true: rather, you can only fail to reject the hypothesis. Meaning that no matter how much data you collect, an opportunity might interfere with your results.
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