6 Ways to Make Your Data Analysis More Reliable

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In recent years, big data has exploded, and big data analytics are now more accessible and of higher quality than ever before. This has led to a scrambling among business owners to improve their own data collection and analysis. There are a lot of tactics you can implement to improve data quality and achieve greater accuracy from analysis.

1. Improve data collection.

Your big data analysis begins with data collection, and the way in which you collect and retain data is important. Your company can amass huge quantities of data in a short period of time, but not all of that data is relevant for analysis. Start by defining the types of data that are important to your company. Think about the behaviors among shoppers that are of the greatest pertinence to your overall business, so you can hone in on the most useful behavior-related data.

2. Improve data organization.

Once you have a strategy in place for improving data collection, you need a method for storing and managing that data. Meticulous data organization is pertinent for analysis, and it will enable you to remain in control of data quality while improving the efficiency of analysis.

3. Cleanse data regularly.

Dirty data is perhaps the biggest culprit of low-quality data and poor data analysis. Data cleansing is imperative and will help to ensure data analysis is centered around the highest quality, most current, complete, and relevant data.

4. Normalize your data.

When data is collected from a variety of different sources, it often contains inconsistencies or errors in terms of how different words are spelled. For instance, when entering a country name, the United States often appears written out or as U.S or U.S.A. These small derivations can actually have a big impact on data analysis. You need to create a standard for all data to adhere to, so your data remains uniform throughout.

5. Integrate data across departments.

Data silos are like a black fog in the marketing world, sabotaging the analysis efforts of marketers and clouding their view of the customer. You need a data management platform that will make it easy to integrate all departmental data into a single platform, so you can eliminate silos and achieve greater accuracy in data analysis.

6. Segment data for analysis.

If your data is clean, well-organized, and free of silos, but still isn’t making any sense, the next step is to segment your data for a more detailed and focused analysis. Consider what you’re trying to achieve from data analysis and what specific questions you want to answer. Then you can sort data into relevant groupings to analyze trends within the various data subsets. Not only does this make data analysis easier by breaking the information down into smaller, more digestible chunks, but it also improves accuracy, enabling you to hone in on highly specific trends and behaviors. 

Data quality is important for ensuring your data analysis is both accurate and easy. When your marketing team is dealing with low-quality data, data silos, or dirty data, it makes their jobs much harder and also reduces the reliability of data analysis. With a robust customer data platform, you’ll be able to manage data more effectively while ensuring all data captured and retained is of the highest quality.

A robust customer data platform can help you enrich and maintain the data you have to drive better business results. Download the Ultimate Guide to Driving Revenue with a B2B Customer Data Platform to learn more today.