Summary

1.  Clarify the question

2. Set measurements

3. Data collection

4. Analyze data

5. Interpret results

6. Conclusion

How a simple data analysis process facilitates your decision making.

Today, data is available to every company in vast quantities. There are numerous and diverse. The lack of data is the least of the problems for most companies. On the contrary, the multitude of data available in various forms obscures the key points necessary to make a clear decision.

To structure the data, you must first know,

  • Are the data relevant to the question?
  • What conclusion can be drawn?
  • To what extent does this support the decision in one direction or the other?

In most cases, we do not need additional data, but only a good evaluation. Something where a tool like Canri supports you. Go through the following steps to improve your analysis and simplify your decision making.

Clarify the Question

Before you deal with the data itself, try to check your question: What exactly do you want to know? The question should not be too general, but measurable, clear, and concise. Design your questions so that possible solutions to your specific challenges stand out.

For example, in a labor-intensive project, the question of whether it is lucrative is too general. But a good question might be whether the desired margin is achievable by a moderate adjustment of the hourly rate.

Set Measurements 

Think about what exactly and how the relevant criteria are measured.

1. What is measured

Consider in our hourly rate example, what kind of data you would need to answer your key question. In this case, in addition to the hourly rate, you would need to know the number of hours spent on the project. In answering this question, you will probably also have to answer subquestions such as Are all hours of it billable? If not, how can they be recorded and included in the hourly rate? Finally, when deciding what to measure, you should also consider all reasonable objections and obstacles, e.g. What form of time tracking is feasible for a project or company?

2. How to measure

It is equally important to think about how you measure your data, especially before the data collection phase. The measurement process will later substantiate or discredit your analysis.

  • Over what timeframe is the analysis performed (monthly or quarterly)?
  • What unit of measurement is used (USD or Euro? Exact time or in 15 min, hours, days)?
  • Which factors should be included (e.g. only working days or is vacation also charged)?

Data Collection

With the question and definition of the framework, we now move on to data collection. But wait:

  • Before you start collecting new data, consider what information is already collected from existing databases and sources. This way, every company has at least defined the working hours of its employees. Various time recording systems are used in parallel. Do you determine which existing databases or sources can already be collected? Collect them first.
  • Define in advance a system for storing and naming files. If a team is involved in the evaluation, this will facilitate collaboration. It saves time and prevents team members from collecting the same information twice.
  • Consider whether data sources can be optimized, e.g. time is already recorded, but there is no concrete allocation to which projects it is used. Is it optimized by minor adjustments?
  • Perhaps, relevant data is collected through observations or interviews. Develop a template in advance to ensure consistency and save time.
  • Document the data collections in an orderly manner and add all source notes one by one. It allows you to easily review your conclusions and eliminate weaknesses later on.

Analyze Data

After you have collected the right data to answer your question, it is time for deeper data analysis. Bring the data together. A tool like Canri that can process data from multiple sources and present it together is helpful. Start by manipulating your data in different ways. For example, you can increase the hourly rate and compare the impact. With a pivot table, you can sort and filter the data by different variables and calculate the mean, maximum, minimum, and standard deviation of your data.

If you manipulate data, you may find that you have exactly the data you need, but it is likely that you will need to revise your original question or collect more data. This initial analysis of trends, correlations, deviations, and outliers will help you focus your data analysis on better answering your question and any objections from others.

Interpret Results

After analyzing your data and possibly further investigation, it is finally time to evaluate your results. A question is not always answered clearly. No matter how much data you collect, exceptional circumstances or simple coincidence can affect your results. You may not be able to “prove” a hypothesis to be true, but you can evaluate the possibility.

Question the results:

  • Does the data answer your original question? And how do they answer it?
  • Does the data help you to defend against objections? How can they help?
  • Are there any limitations to your conclusions, any aspects you have not considered?

If your interpretation of the data stands up to all these questions and considerations, then you have probably reached a productive conclusion. The only remaining step is to use the results of your data analysis to determine your best course of action.

Conclusion

If you follow these five steps in your data analysis process, you will make better decisions for your business. Your decisions are supported by data that has been sustainably collected and analyzed. With practice, your data analysis will be faster and more accurate. You make better, more informed decisions to run your business as effectively as possible. Use Canri to support you with these processes.

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