{"id":58583,"date":"2023-08-24T10:27:01","date_gmt":"2023-08-24T14:27:01","guid":{"rendered":"https:\/\/www.sogolytics.com\/blog\/?p=58583"},"modified":"2023-09-13T10:19:24","modified_gmt":"2023-09-13T14:19:24","slug":"best-practices-for-analyzing-data","status":"publish","type":"post","link":"https:\/\/www.sogolytics.com\/blog\/best-practices-for-analyzing-data\/","title":{"rendered":"Best Practices for Analyzing Data"},"content":{"rendered":"

Bailey works in a \u201cdata-driven organization.\u201d Who doesn\u2019t these days? Yet, Bailey struggles with a deluge of data from different sources. There\u2019s a lot of information. Making sense of it, learning from it, and acting on it proves challenging.<\/p>\n

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Sound familiar? You’re not alone. We\u2019ve 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.<\/p>\n

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The data-driven business world<\/h2>\n

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\u2019t until the mid-20th century that we saw advances in surveying and data collection techniques enabling a wider embrace of data analysis.<\/p>\n

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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 \u201cBig Data.<\/a>\u201d<\/p>\n

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Today, the business world leverages powerful data analytics and machine learning in market research<\/a>, customer profiling, personalization<\/a>, 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.<\/p>\n

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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<\/a>, cost savings, and revenue growth.<\/p>\n

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Best practices for data analysis<\/h2>\n

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.<\/p>\n

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#1 Know your objective<\/strong><\/p>\n

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\u201cAnalysis paralysis\u201d 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.<\/p>\n

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Align your specific queries of the data with your business objectives. Measuring key performance indicators<\/a> or metrics can help keep your analysis focused to be relevant and actionable.<\/p>\n

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#2 Work with quality data<\/strong><\/p>\n

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You can\u2019t have a data analysis article without reading the phrase \u201cgarbage in, garbage out.\u201d That\u2019s because effective data analysis requires you to gather high-quality, accurate data<\/a> relevant to your question.<\/p>\n

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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.<\/p>\n

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#3 Choose appropriate analysis methods<\/strong><\/p>\n

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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<\/a>, including:<\/p>\n

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