In the past, people sometimes couldn’t find proper data to support their analysis. After entering the Big Data age, our world is full of data, lack of data for many businesses isn’t a problem anymore; Instead, there is often too much information available for people to find the answers for their business questions.
Because of that change, the data challenges become:
- How to find the right data?
- How to analyze data?
- How to draw conclusion from data analysis to support business decisions?
Although every business has its own data analysis objectives and requirements, there are still some best practices in common to generate better data analysis:
1.Define the right questions
To support a data-driven decision, the most important step is to clearly define your problems, so that you can create a data solution on how to solve your problems. In short, to get the right answers, you need to ask the right questions.
2.Use the right data
You need to understand the data. For example, identifying what kinds of data are required to solve your problems, where the data comes from, whether the data is available. In addition, you need to read the metadata to understand how the data was collected, what attributes are included and what are the limitations when using the data.
3.Collect data with the right designs
If the data required for your analysis is not readily available, you might need to collect your own data.
Firstly, you need to choose the best data collection method such as paper-based survey, telephone interview, web-based survey, active collection or passive collection.
Secondly, you need to think about how to design your methods and questions to release the respondents’ burden and increase the response rate etc.
Last, but not least, you need to follow the statistical design rules to design your data sample.
4.Choose the right methods for data analysis
After you have all data required for your analysis, before using the data, you would need to think about data cleaning. Data cleaning is the most significant step to improve data quality.
You also need to design a data model that correlate the data with the business outcomes. For example, if your business decision is to measure performance, you need choose the right metrics which are quantitative, measurable and should be easy enough to explain.
5.Choose the right tools for data analysis
You need to choose the right tools for your data analysis. Using the right tools can avoid over-complicated or time-consuming analysis process. For example, you can
- use spreadsheets for small dataset and as the fast and easy tool for model testing and brainstorming;
- use database tools and Structured Query Language (SQL) to query and analyze larger datasets; or
- use statistical software to answer more complicated questions like trends and predictions.
6.Choose the right visualization to present your results
Before presenting your data, you need to understand what type of message you are trying to deliver from your data analysis – you want to present relationship, comparison, composition or distribution?
The step to follow is to pick the visualization type that best fit your objectives. For example, scatter plots or maps are best used to show distributions, line charts are better suited for relationships, pie charts do well when you’re trying to communicate a composition, and column charts, bar charts or line charts may be used for comparisons.