Data analysis, where do you start?
Uppdaterat: 19 nov 2020
Today, data analysis is more often being identified as developing and experimenting with increasingly sophisticated data analysis techniques, which generate business decisions by delivering a whole new way of thinking for the company. In recent years, it has become increasingly difficult to find a successful organization that has not utilized data analysis tools to facilitate decision-making. Therefore, the use of data analysis has transformed from being "good to have" to being an opportunity or even a absolute necessity for your company.
Data analytics is a subject in constant motion. With organizations continuously invest heavily in analytics to support digital transformations, keeping on top of the latest trends is essential to ensuring your organization is adopting the analytics strategies and tactics required for success in the near or further on.
For most businesses, lack of data isn’t the problem. In most cases it’s the opposite: there’s too much information to make a decision. With so much data to sort and classify, you will need something more from your data.
You will need to know it is the right data for answering your question(s)
You will need to draw accurate conclusions to maximize business performance
You will need data to inform your decision making process
To get going with data analysis, this is a solid start:
Step 1: Define Your Questions
Your company must define and begin with the right question(s). Questions should be measurable, clear and concise. The design of your questions should reflect a potential solution to a specific problem.
Example: A company is experiencing rising costs and is starting to loose their competitive advantages because of their undigitalized business. A question that might be asked here is: Can the company by cutting employees, compensate for the lack of digitalization?
Step 2: Set Clear Measurement Priorities
Consider what data you would need to answer your key questions. In the previous example you need to know the number of employees, their productivity and percentage of time spent on unnecessary manual tasks. In your decision on what to measure, include any reasonable consequences this would have on any stakeholders.
Example: If the number of employees is reduced how would the company respond to increasing demand of the company’s product/service.
To identifiy your KPIs (Key Performances Indicators) these are some questions that you need ask yourself:
What is your time frame? (Annual vs quarterly vs monthly costs)
What factors should be included? (Annual salary versus annual salary plus indirect employee costs)
Step 3: Collect Data
Before you collect new data, determine what information could be collected from existing data sources.
Determine a file storing and naming system ahead of time to help all tasked team members collaborate. This process saves you time and prevents team members from collecting the same information twice.
If you need to gather data via observation or interviews, then develop an interview template to ensure consistency and save time.
Step 4: Choose your tool
By attending e.g free demos and seminars, you and your company can come to a conclusion regarding what analytical tool that corresponds the most with your needs in terms of importing data from database(s), visualization in reports and also sharing with others. Otherwise, reach out to us at ERP & Friends to help you, we support companies in business software selection.
Step 5: Analyze Data
There are a numerous different data analysis tools and software that are extremely helpful. Hypergene, SAS, Google Data Studio, KlipFolio, PowerBI and Qlikview are all good software packages for advanced statistical data analysis. Microsoft Excel is however still a very strong competitor to new data analysis tool in terms of decision-making. In this case Google Data Studio and KlipFolio offers a free cloudbased software for data analysis.
As you interpret the results of your data, these key questions should help you on the way
Does the data answer your original question(s)? How?
Does the data help you defend against any objections? How?
Are there any limitation on your conclusions, anything you haven’t considered?
If your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of action.
By following these five steps in your data analysis process, you can make better decisions for your business, because your choices are supported by data that has been collected and analyzed. In time, your data analysis gets faster and more accurate – meaning you make better, more informed decisions to run your organization more effectively.
/William Leo, ERP Management Consultant at ERP & Friends