Bailey works in a “data-driven organization.” Who doesn’t these days? Yet, Bailey struggles with a deluge of data from different sources. There’s a lot of information. Making sense of it, learning from it, and acting on it proves challenging.
Sound familiar? You’re not alone. We’ve collected these best practices for analyzing data to help you gain insights and make data-informed decisions. By following these practices you can improve the quality and reliability of your data analysis.
The data-driven business world
The data-driven society we see today is the result of a continuous evolution driven by technological advancements, increased data availability, and the recognition of data’s value in informing decisions and solving problems. Statistical analysis started centuries ago, but it wasn’t until the mid-20th century that we saw advances in surveying and data collection techniques enabling a wider embrace of data analysis.
Then, computing technology revolutionized data processing to make it possible for us to analyze and manipulate data more accurately and more quickly, without relying on manual calculations. With the development of databases that could store and manage large volumes of data, plus the Internet and digital technologies transforming data collection and sharing, we reached the age of “Big Data.”
Today, the business world leverages powerful data analytics and machine learning in market research, customer profiling, personalization, and optimization of business processes. Making decisions based on evidence and data has become easier than ever, and it can lead to better outcomes and reduced reliance on gut feelings.
Thus, many employees have access to large volumes of data and many methods of data analysis. These best practices for analyzing data can help you identify patterns, trends, and opportunities to improve business efficiencies, cost savings, and revenue growth.
Best practices for data analysis
Accessing data is seldom the issue. The bigger obstacle is analyzing all your collected data in a useful way. The following strategies can help you develop your data literacy and derive more actionable insights from your analysis.
#1 Know your objective
“Analysis paralysis” is a common phrase today. It is easy to feel overwhelmed in the face of several different spreadsheets or even a custom data dashboard. But you can set out to analyze the data more effectively if you clearly define what you want to understand and learn.
Align your specific queries of the data with your business objectives. Measuring key performance indicators or metrics can help keep your analysis focused to be relevant and actionable.
#2 Work with quality data
You can’t have a data analysis article without reading the phrase “garbage in, garbage out.” That’s because effective data analysis requires you to gather high-quality, accurate data relevant to your question.
Achieving quality data can be more challenging today with the volume of inputs and variety of databases you have to manage. Still, taking the time to identify and fix any data errors pre-analysis can help reduce errors later on. Look for missing values, outliers, inconsistencies, and other issues to ensure the accuracy and reliability of your analysis.
#3 Choose appropriate analysis methods
You may be working with quantitative (numeric) or qualitative (descriptive) data or a mix of both. The appropriate analytical technique depends on the nature of your data as well as your objectives. There are many types of data analysis, including:
- Text analysis or data mining to rearrange large amounts of data to make it easier to manage and filter
- Statistical analysis which relies on statistical techniques to examine the data and help you make better decisions
- Diagnostic analysis which collects and analyzes various data to identify factors that are contributing to an issue and determine an action to take next
- Predictive analysis which relies on historical data and previous trends to anticipate what could happen in the future
- Prescriptive analysis, like predictive, which anticipates potential future outcomes but takes it a step further to draw specific recommendations
#4 Visualize the data
Using graphs, charts, and other visual tools can help make your analysis more accessible. It may be easier to see patterns and trends when you see the data visually. Creating a graphic depiction of your data can also help you identify inaccuracies or errors, inspire new questions, and expedite decision making.
#5 Be aware of biases
Bias in data analysis can arise from biased data collection, sample selection, or the choice of analytical methods. Being conscious of any potential biases in your data or analysis is crucial for accurate results. Common types of data bias include:
- Confirmation bias — wanting to be right so finding data that supports our existing ideas
- Selection bias — basing your analysis on a sample that is not representative or not truly randomized
- Historical bias — replicating systemic prejudices in data systems and data analysis
- Survivorship bias — focusing only on the “winners” and failing to fully consider both the unsuccessful and successful paths
- Availability bias — basing your perception on immediately available information
- Outlier bias — failing to look at the entire range of data distribution to find and investigate outliers and anomalies that could be telling
Validating your results and comparing them with known benchmarks may help you avoid coming to biased conclusions.
#6 Seek feedback
Collaborating with others and seeking feedback about your data analysis can help validate your findings. Plus, you may uncover bias or blind spots with the help of an outsider’s perspective. Your peers may also offer constructive criticism to help you improve your methods and continuously learn how to be a more adept data analyst.
#7 Remain curious
Don’t be afraid to revisit and refine your analytical approach. You may need to ask different questions. You might find new insights emerge if you’re open to exploring your data from different angles. Questioning your assumptions and taking a critical thinking approach to your analysis can also lead to more robust conclusions.
Do data analysis & reporting better
As data collection and processing technologies continue to evolve, we can expect data-driven business to become even more pervasive in the future. This article has shared strategies to support improved data analysis. Next up, we’ll talk about how to enhance your data reporting.
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