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article Data Analysis in 5 Steps
published on May 13, 2020

Modern businesses have more data available to them now than ever before. With this abundance of data also comes the need to interpret it in meaningful ways. Many businesses have already begun the process of expanding their focus on data analytics. So much so that by 2022, global spend on business analytics is projected to reach $260B (1). The growing impact of data on business is also evident with the emergence of the Chief Data Officer role in many companies. Analyzing that data can be a key part of unlocking new insights and learning more about sales and customer trends. While data analysis has clear advantages, it can be overwhelming to figure out where or how to start. In this post we’ll explain five steps to get you started with data analysis.



The first step in data analysis is to clearly define your questions and goals. Similar to creating a hypothesis before an experiment, you should be asking a targeted question before searching the data for an answer. What problems are you trying to solve? Which parts of your business do you want more information about? Are you trying to solve an existing problem or predict how your company will perform based on determined factors? Clearly defining your goals will help guide the rest of the analysis process. For example, questions about overall performance can be open-ended and it can be hard to pinpoint which metrics are needed for analysis. Instead, it is more advantageous to ask questions such as “How have certain metrics changed over time?” and “Do these metrics correlate with others, and if so, how strongly?” These types of questions have a specific focus which will help determine the type of analysis needed and what data is the most relevant to include.



Before you can start analyzing, there needs to be data available for use. Data can include sales records, customer demographics, lead tracking, net promoter scores, and more. When using a business intelligence tool, it is important to make sure that all of the data is accessible and the proper connections are set between your data warehouse and your BI tool of choice. Ultimately the volume of data required will depend on the question you wish to answer. Though you may only need a subset of the data collected, not having enough data can skew the results of your analysis.



Now that you have all of your data in one place, it is important to clean the data before beginning the analysis portion of this process. A large part of the cleansing process includes making sure that the data is in a usable format. This entails searching for outliers, dealing with null values, and looking for data that may have been incorrectly input. Often this can be a lengthy and arduous process. A recent survey among Data Science professionals indicated that Data Analysts spend approximately 27% of their time cleansing data (2). While it may not be glamorous or the most enjoyable portion of the data analysis process, data cleansing is crucial to optimize the accuracy of your analysis.



Once the relevant data is available and cleansed, it is time to analyze. Choosing a method of analysis will heavily depend on the question or goals defined earlier and the type of analysis needed. Diagnostic analysis uses data to search for the cause of, and a solution to, an existing problem. Descriptive analysis is a way to describe the data by summarizing key sections. Predictive analysis combines historical data and statistical modeling to forecast how certain metrics will perform in the future. For instance, if you wanted to project how next year’s sales would differ by fiscal quarter, you could use predictive analysis to analyze data from previous years. Then you could use the observed trends to make predictions on next year’s sales. For these types of analysis you can utilize methods of varying complexities to dissect your data. The methods range from taking averages to training clustering algorithms or other machine learning models. Ultimately, it is important to confirm that the method of analysis matches the intent of the problem statement.



After analyzing your data, it is important to interpret the results. Simply put, what are you learning from the results of your analysis? One way to interpret the results is by creating data visualizations. These can be done with coding libraries such as Plotly or Matplolib. But many BI Tools are also available and will enable users to visualize data in many different ways. These tools excel at allowing users to create visualizations as a method of communicating key metrics and trends to stakeholders, customers, and internal executives. Check out one of our earlier blog posts on choosing the right visualization to learn about the strengths and effects of each visualization type. Combining multiple visualizations in one report enables the users to tell a story about their data that leaders can use to take actions, make changes, or refocus efforts accordingly. This step is key to gaining insights from the data and being able to apply these insights to your business.

With these steps, you will be able to use data analysis to unlock powerful new insights that can be turned into actionable results. These insights could lead to more questions that prompt you to dig even deeper into the data and start the analysis cycle again.



  1. https://www.technologyrecord.com/Article/big-data-and-business-analytics-spend-to-reach-us260-billion-in-2022-72124

  2. https://businessoverbroadway.com/2019/02/19/how-do-data-professionals-spend-their-time-on-data-science-projects/

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